Multicriteria Decision Analysis Research Papers

Eisenhower matrix * Saaty AHP = Strong actions prioritization? Theoretical literature and lessons drawn from empirical evidences

by Homere Ngandam and

2019, IAETSD JOURNAL FOR ADVANCED RESEARCH IN APPLIED SCIENCES

This paper proposes a tool resulting from the merging of the Eisenhower matrix and Analytical Hierarchy Process to strengthen the prioritization of actions for managers. The case study is from a Ph.D. thesis work, which the first phase is... more

box®; B-The "four D" box’; C-The "Manage-Focus-Avoid-Limit" box®; D-The "colors and signs" Its representations vary from an activity to another with the same background idea: the good time

Figure 2. The position of the AHP in the scientific debate ((1)) 2.3. Common points, divergences and questioning one, between the hard operations research, the soft operations research and the decision analysis. As described above, Eisenhower matrix and Analytic Hierarchy Process are interested on the

Figure 4. Adapted Eisenhower matrix to the priority quotient tasks corresponding quadrants. (Fig.4.). It serves for the experts’ time saving usage. Although the matrix matches the needs of the practitioner, it can be more complex to read After obtaining the PQs, an Eisenhower 2x2 matrix is built and each task is affected tc

Figure 5. The Analytic hierarchy process structure A pair comparison of sub-criteria and criteria enable to derive the weights corresponding.

Table 6. Partial Priority Quotients in the nine districts An Eisenhower matrix 4 x 4 is constructed, based on the maximum of the Uf * If value. Beside does not exist. Then, the PPQs values are the following: (Tab. 6.) here is a scale graduated according to PPQs values, to ease the reading of priorities in actions. to the rural road have been taken in account. The value is more important when the infrastructure

Figure 7a. The Accessibility Governance Matrix with qualifiers coordinates The AGM has some specificities that need to be detailed.

Numerical coordinates are used to locate each sub-quadrant. (Fig.7.b: Figure 7.b. The Accessibility Governance Matrix with Numerical coordinates The first value is for the sub-matrix. The second is related to the sub-matrix quadrant. The third and the fourth values are designating respectively the importance and the urgency. With the abbreviations of the qualifiers, the correspondence is made with the numerical ones to deduct the matrix coordinates. (Tab.7)

Table 1. The fundamental scale of absolute numbers ((6])

Table 2. From principles to tasks on a technical approach A Priority Quotient (PQ) is calculated for each task by using the following formula!?: The roles to be play by the expert are elaborated in technical tasks and they are listed. (Tab.2.)

Table 2. Priority Quotient for technical tasks

Table 4. Weights of Criteria and sub-criteriz

Table 5. Values of Accessibility Index in the nine districts

The table 8 above coupled to the Accessibility Governance Matrix and the Partial Priority Table 8. Final values of the Partial Priority Quotients for the nine districts

activities, according to the priority proposition. An example is given in table 9.

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A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making

by Mehdi Keshavarz-Ghorabaee

A key factor to attain success in any discipline, especially in a field which requires handling large amounts of information and knowledge, is decision making. Most real-world decision-making problems involve a great variety of factors... more

Table 6. The normalized decision matrix of Example 2 To calculate the negative-ideal solution, we should obtain the weighted normalized performance values first. Table 7 shows the weighted normalized decision-matrix and corresponding negative-ideal solutions. Also, in the last two columns of this table, the Euclidean and Taxicab distances of alternatives from the negative-ideal solution are represented.

Table 9. Data of the MCDM problem for comparative sensitivity analysis Table 10. Simulated weights of criteria in different sets

To describe the proposed method, we use a simple situation with seven alternatives and two criteria. Suppose that weighted normalized performance values (7;;) have been calculated. These values are dimensionless and between 0 and 1. Figure 1 shows the position of all alternatives according to these values. Figure 1. A simple graphical example with two criteria As can be seen in this figure, ns = [0.10.1] is the negative-ideal point (solution).The Euclidean distances of alternatives from this point are:

Figure 2. Effect of changing the ct parameter on ranking of alternatives A New Combinative Distance-based Assessment (CODAS) Method for Mullti- Criteria Decision-making

This example is adapted from Chakraborty and Zavadskas (2014) which is related to the selection of the most appropriate industrial robot. Five different criteria which are considered in this robot selection problem are: load kg), maximum tip speed (in mm/s), repeatability (in mm), memory capacity (in capacity (in points or steps) and manipulator reach (in mm). Among these criteria, the load capacity, maximum tip speed, memory capacity, and manipulator reac as benefit criteria, and the repeatability is defined as a cost criterion. 1 h are defined This problem consists of seven alternatives, and the corresponding data are given in Table 1.

Table 2. The normalized decision matrix of Example 1 Using weights of criteria that are given in Table 1, the weighted normalized performance values can be calculated, and then the negative-ideal solution is determined. According to the obtained values, the Euclidean and Taxicab distances of alternatives from the negative-ideal solution are also computed. The results are presented in Table 3.

Table 3. The weighted normalized decision matrix and the negative-ideal solution of Example 1

Mehdi Keshavarz Ghorabaee, Edmundas Kazimieras Zavadskas, Zenonas T urskis, Jurgita A ntucheviciene

Table 5. Data of Example 2 This example is adapted from Zavadskas and Turskis (2010) and considers the evaluation of microclimate in an office. Six criteria determined for this evaluation process are: the amount of air per head (in m*/h), relative air humidity (in percent), air temperature (in °C), illumination during work hours (in 1x), rate of air flow (in m/s), and dew point (in ‘C). All of these criteria are defined as benefit criteria except the rate of air flow and the dew point. Fourteen alternatives should be evaluated according to these criteria. The data of this problem are shown in Table 5,

Table 12. Correlation coefficients between the ranking results of the CODAS and the other methods As previously mentioned, a threshold parameter (z) is used in the process of the CODAS method. We suggest a value between 0.01 and 0.05 for this parameter. However, we want to evaluate the effect of changing this parameter on the ranking result of the CODAS methods. According to Table 12, the minimum value of the Spearman's rank correlation coefficient is in the set 1 of criteria weights (7, = 0.83). So this set of criteria weights, which is more sensitive than the other sets, is selected for analysis of changing the threshold parameter. We use fifteen values for this parameter in the range of 0.01 to 1. The ranking results obtained by the CODAS method in different values of 7 are presented in Table 13. The graphical changes in the ranking of alternatives are also depicted in Figure 2.

Table 13. Ranking results with different values of t

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Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach

by Mehdi Keshavarz-Ghorabaee

Supply chain risk management research has mainly mistreated the important of sustainability issues. Moreover, there is little knowledge about sustainable management of risk and supply chain and the way they impose losses for firms. Risk... more

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"Roads to perdition in the knowledge economy", Environment and Planning A

by Dragos Simandan

Environment and Planning A

This paper highlights two of the ways in which an abundance of frequent and high-quality information can nonetheless lead an economic agent to failure. I begin with the problem of frequent information, continue with the problem of... more

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Application of multicriteria decision analysis in environmental decision making

by Gregory A. Kiker

2005, Integrated …

Decision making in environmental projects can be complex and seemingly intractable, principally because of the inherent trade-offs between sociopolitical, environmental, ecological, and economic factors. The selection of appropriate... more

Figure 1. Current and evolving decision-making processes for contaminated sediment management.

Figure 2. Example decision criteria and matrix.

should be given to designing an adaptable structure such that participants can modify aspects of the project to suit local concerns while still producing a structure that provides the required outputs. The process depicted in Figure 3 follows 2 basic themes: (1) generating management alternatives, success criteria, and value judgments and (2) ranking the alternatives by applying the criteria levels and value weights. The Ist part of the process generates and defines choices, success levels, and preferences. The latter section methodically prunes nonfeasible alternatives by 1st applying screening mechanisms (e.g., overall cost, technical feasibility, and general societal acceptance) followed by more detailed ranking of the remaining options using decision analysis techniques (e.g., AHP, MAUT, and outranking) that adopt the various criteria levels generated by environmental tools, monitoring, or stakeholder surveys. lines tor less direct involvement. While the actual member- ship and the function of these 3 base groups may intersect 1 vary, the roles of each group are essential to collecting the largest amount of information to support the decision process Each group has its own way of viewing the world, its owr method of envisioning solutions, and its own societal responsibility. Policymakers and decision makers spend most of their effort defining the problem context and the overall constraints to the decision. In addition, they may have responsibility for the selection of the final decision and its implementation. Stakeholders may provide input to defining the problem but have the highest degree of interaction ir helping formulate success criteria and contributing value judgments for weighting the various success criteria. Depend. ing on the problem and regulatory context, stakeholders may have some responsibility in ranking and selecting the final option. Scientists and engineers have the most focused role because they provide the measurements or estimations of the desired criteria that determine the success of various alternatives. While scientists and engineers may take < secondary role as stakeholders or decision makers, thei primary role is to provide the technical details as requestec by the decision process.

? PROMETHEE = Preference Ranking Organization METHod for Enrichment Evaluations; ELECTRE = Elimination Et Choix Traduisant la Realite; AHP = analytical hierarchy process; GIS = geographic information system; MAUT = multiattribute utility theory; MCDA = multicriteria decision analysis; EIA = environmental impact assessment; USDA = U.S. Department of Agriculture; USDOE = U.S. Department of Energy; SMART = simple multiattribute rating technique. Table 1. Continued

Table 2. Applications of decision support tools for stakeholder involvement? * MAUT = multiattribute utility theory; USDOE = U.S. Department of Energy; NSF = National Science Foundation; USEPA = U.S. Environ- mental Protection Agency; USDA = U.S. Department of Agriculture; AHP = analytical hierarchy process.

Table 3. Applications of decision support tools for contaminated sites* * AHP = analytical hierarchy process; USDOE = U. S. Department of Energy; MAUT = multiattribute utility theory; INEEL = Idaho National Environmental Engineering Laboratory; USDOD = U. S. Department of Defense.

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A state of the art survey & testbed of fuzzy AHP (FAHP) applications

by sylvain Kubler Kubler and

A state-of the-art survey & testbed of Fuzzy AHP (FAHP) applications

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of... more

MAUT — Multi-Attribute Utility Theory ELECTRE, TOPSIS, PROMETHEE ~— see Table

Fig. 3. Traditional membership functions used in FAHP.

heard throughout the R&D, engineering, and manufacturing stages of the product. QFD originated in the late 1960s in Japan from the work of Akao (1990). Using it, the development team can systemati- cally specify the customer’s requirements and evaluates each proposed product so as to identify the degree to which it meets the set of require- ments. (F)AHP is usually used to‘feed’the House of Quality (HoQ), which displays the relationships between the customer requirements (also called the WHATs) and the technical requirements/characteris- tics (also called the HOWs). Fig. 5 shows a traditional HoQ (WHATSs, HOWs...). There are in fact two distinct ways to combine (F)AHP with HoQ matrices in the literature, namely: Fig. 5. House of Quality (HoQ) in QFD: design & concepts.

Money allocation: The objective consists in allocating a limited amount of money to projects or action plans. Within this context, Lee et al. (2013, 2009) are seeking to allocate money resources to the best R&D energy projects. Both papers consider the same energy-related problem (the authors being the same), where FAHP is applied to clas- sify the best projects. High level criteria are considered in both stud- ies such as economical spin-off, commercial potential, inner capac- ity and technical spin-off. The authors, nonetheless, go a step further in (Lee et al., 2013) since they combine FAHP with a DEA analysis, which helps to include a quantitative economic viewpoint. However, one can argue that both studies do not provide a complete resource al- location framework since the money allocation phase is not fully ad- dressed (or at least presented) in their studies. To promote the suc- cess of University business incubators, Somsuk and Laosirihongthong (2014) propose to identify the most important factors to concentrate their efforts and resources accordingly. However, as previously, the authors do not address the allocation stage. Finally, Hajipour et al. (2014) help to allocate financial resources according to a quality ac- tion plan. To this end, FAHP and BSC are used respectively to com- Fig. 6. Specific distribution of the reviewed papers arranged Theme-wise and Application area-wise (cf. Section 3 for more details). Figs. 6(a) and 6(b) provide a percentage distribution of the 190 reviewed papers respectively arranged Theme-wise and Application area-wise (values in brackets corresponding to the number of papers per category). It can be observed that FAHP is being predominantly used in the theme area of Selection and Evaluation, which is a similar finding to Vaidya’s survey. As far as the area of application is con- cerned, most of the times FAHP has been used in Manufacturing, In- dustry and Government categories. This finding differs significantly from Vaidya’s survey since AHP was primarily used (back to 2006) in Engineering, Personal and Social categories. A plausible explanation of this difference could be attributed to the combination of AHP with the fuzzy logic, which is a theory widely applied within manufactur- ing and industrial applications. To support this statement, let us quote (Karwowski & Evans, 1986) who enumerate three reasons for this:

Fig. 7. Classification of the reviewed papers according to the “Specific objective” addressed in each of them. Table 9 Categorized list of references on the basis of “Other Tools” integrated with FAHP with respect to “Themes” .

sociated FPCMs (see “On all data” label in Fig. 10(a)), and (ii) the subset of papers and associated FPCMs resulting from the use of fil- ters (see “On selected data” in Fig. 10(a)).

Fig. 10. Online Testbed making available results and findings of the state-of-the-art survey, as well as FPCMs from the reviewed papers.

References on the topic of*Decision-making’.

References on the topic of*Benefit-Cost’. Table 5 To this end, two distinct criteria hierarchies are constructed: one iden- tifying the benefits and another the costs. Each alternative thus ob- tains two scores that are divided by each other (2%! ©") in order cost score to obtain the alternative’s profit level. Following a different approach, Thengane, Hoadley, Bhattacharya, Mitra, and Bandyopadhyay (2014) present a FAHP-based BCA approach relying on a unique criteria hi- erarchy, whose goal is to assess in terms of BCA eight of the most common hydrogen production technologies. Five environmental crite- ria selected under the benefits category are considered, namely: green- house gas emissions, raw material/utilities consumption, energy effi- ciency, scalability, and finally waste disposal/atmospheric emissions. The results obtained for benefits category are then plotted against the normalized equivalent annual costs of each technology in order to identify the most beneficial one. Lee (2009) also relies on a unique criteria hierarchy but rather using the BOCR model, meaning that B.O.C.R are defined as four distinct criteria at “Level 1” of the hierar- chy (cf. Fig. 2), thus enabling the selection of suppliers with the con- sideration of benefits, opportunities, costs and risks. The same authors (Lee, Chen, & Chang, 2008a) construct a similar approach for evalu- ating an IT department in the manufacturing industry in Taiwan, even though “Level 1” of the hierarchy is slightly different relying on the BSC model? and related criteria: (i) financial, (ii) customer, (iii) inter- nal business process, and (iv) learning and growth criteria. From the same perspective, Wu, Tzeng, and Chen (2009) propose a three-step approach combining BSC with FAHP and three other MCDM analyt- ical tools (SAW, TOPSIS, and VIKOR) in order to rank the banking performance and improve the gaps with three banks as an empirical example. Pergin (2008) introduces a new/hybrid FTOPSIS approach to evaluate the most suitable business process outsourcing decision. The approach is original as it uses the FAHP hierarchy structure, but not the pairwise comparison process.

References on the topic of‘ Development’.

2015), whose resulting distribution by authors’nationality brought into light that Taiwan, Turkey, Iran, China and India were the most active countries in applying MCDM and TOPSIS techniques. As mentioned in Section 2.3, the Chang’s extent analysis method is one of the most applied technique, despite criticisms. Indeed, a significant number of research papers have demonstrated that this

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Land Suitability Evaluation for Organic Agriculture of Wheat Using GIS and Multicriteria Analysis

by Ali Shirzadi Babakan

2018, Papers in Applied Geography

Land suitability evaluation for organic agriculture of wheat using GIS and multicriteria analysis. Papers in Applied Geography, 4(3), 326-342. Abstract Despite the growing interest in organic farming, its practice remains limited because... more

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Identifying the Risk Factors Affecting Crash Severity at Intersections with Considering Crash Characteristics and Signal Configuration Using an Ordered Logistic Model

by Abdulla Galadari

2018, Proceedings of the 2018 Advances in Science and Engineering Technology International Conferences

Intersections are critical places which experience high levels of accidents due to the availability of vehicle movements from different directions. Therefore, there is a need to understand the factors that significantly contribute to... more

TABLE I: Variables description and basic statistics summery

TABLE IV: Odds Ratio results they are estimated based on logs, therefore, a transformation of the coefficients was done by taking the exponential of the coefficients to estimate odds ratio (OR) results. The basis for interpretation odds ratios are as follows[17]:

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A system of systems approach to energy sustainability assessment: Are all renewables really green?

by Kaveh Madani

Renewable energies are emerging across the globe in an attempt to slow down global warming and to improve national energy security in face of the depleting fossil fuel reserves. However, the general policy of mandating the replacement of... more

Renewable energies are emerging across the globe in an attempt to slow down global warming and to improve national energy security in face of the depleting fossil fuel reserves. However, the general policy of mandating the replacement of fossil fuels with the so-called “green” energies may not be as effective and environmental-friendly as previously thought, due to the secondary impacts of renewable energies on different natural resources. Thus, an integrated systems analysis framework is essential to selecting optimal energy sources that address global warming and energy security issues with minimal unintended consequences and undesired secondary impacts on valuable natural resources. This paper proposes a system of systems (SoS) framework to determine the relative aggregate footprint (RAF) of energy supply alternatives with respect to different sustainability criteria and uncertain performance values. Based on the proposed method, the RAF scores of a range of renewable and nonrenewable energy alternatives are determined using their previously reported performance values under four sustainability criteria, namely carbon footprint, water footprint, land footprint, and cost of energy production. These criteria represent environmental efficiency, water use efficiency, land use efficiency, and economic efficiency, respectively. The study results suggest that geothermal energy and biomass energy from miscanthus are the most and least resource-use efficient energy alternatives based on the performance data available in the literature. In addition, despite their lower carbon footprints, some renewable energy sources are less promising than non-renewable energy sources from a SoS perspective that considers the trade-offs between the greenhouse gas emissions of energies and their effects on water, ecosystem, and economic resources. Robustness analysis suggests that with respect to the existing performance values and uncertainties in the literature, solar thermal and hydropower have the most and least level of RAF robustness, respectively. Sensitivity analysis indicates that geothermal energy and ethanol from sugarcane, have the lowest and highest RAF sensitivity to resource availability, respectively.

Fig. 1. Energy sustainability assessment within a system of systems framework.

Fig. 2. Relative aggregate footprint (RAF) of different energy sources (0-100).

Fig. 3. Comparison of energy sources’ RAF sensitivity to the considered efficiency criteria (C, carbon footprint; W, water footprint; L, land footprint; and $, cost). The dashe horizontal line shows the RAF = 50 level to facilitate comparison. The CWL$ line shows the RAF scores when all four criteria are considered (as shown previously in Fig. 2). Table 4 provides the mean and standard deviation of the energy RAFs, calculated based on 100,000 rounds of Monte-Carlo sampling. Mean RAFs are within 5% of the RAFs calculated in the previous step by applying Eq. (1) at the end of the Monte-Carlo selection process using the overall ranks, as opposed to applying Eq. (1) at the end of each round of Monte-Carlo selection using the ranks specific to that round of selection only. This shows the reasonable reliability of the calculated RAF values in the previous step, which required significantly less computational effort. The Monte-Carlo MCDM method used here orders energy alternatives under each MCDM method based on their winning probabilities at each rank. Once the option with the highest win- ning probability is determined as the best alternative (rank 1) based on a given method, this option is removed from the alternatives

Fig. 4. Individual energy RAF resource sensitivity charts, reflecting RAF sensitivity to the considered efficiency criteria (C, carbon footprint; W, water footprint; L, lan footprint; and §, cost). The RAF value is 0 at the center of the radar chart and 100 at the edge of the chart. In addition to the degree of robustness information, deci- sion makers can benefit from learning about the sensitivity of RAF scores to exclusion/inclusion of different criteria from the analysis. Sensitivity analysis also helps with understanding the major drawbacks of different energy sources that are in need of being addressed in order to improve their efficiency scores significantly. For example, learning the fact that the high water footprint of biofuels is one of the main reasons for their inefficiency would encourage investment and research in the technological improvements that can help with minimizing the biofuels’ water footprint. Moreover, given that the importance of different sus- tainability criteria (carbon footprint, water footprint, etc.) varies regionally depending on the availability of local resources, the sensitivity analysis information can help selecting the best energy

4 Assumed to be the same as biomass woodchip. b Assumed to be the same as geothermal (Jacobson, 2009). Table 1

Overall ranking of the energy sources based on different MCDM methods.

The MCDM methods used in this study. Table 2

Mean and standard deviation of relative aggregate footprint scores. Table 4

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Development of a novel framework for the design of transport policies to achieve environmental targets

by Araz 𝖳aeihagh

2009, Development of a novel framework for the design of transport policies to achieve environmental targets

The formulation of policies requires the selection and configuration of effective and acceptable courses of action to reach explicit goals. A one-size-fits-all policy is unlikely to achieve the desired goals; as a result, the... more

The formulation of policies requires the selection and configuration of effective and acceptable courses of action to reach explicit goals. A one-size-fits-all policy is unlikely to achieve the desired goals; as a result, the identification of a suite of alternative policies, together with clear indications of their trade-offs, is crucial to accommodate the diversity of stakeholders’ preferences. At present, the formulation of transport policies is done manually; this fact, together with the size of the space of possible policies, results in a large part of that space being left unexplored. A six-step framework to explore the space of alternative transport policies in order to achieve environmental targets is proposed. The process starts with a user-defined set of specific policy measures, using them as building blocks in the generation of alternative policy packages, clusters and future images according to the user's preferences and goals.The analysis framework is based on the visioning and backcasting approach used in the VIBAT report [Banister, D., & Hickman, R. (2006a). Visioning and backcasting for UK transport policy (VIBAT) project. Department for Transport's Horizons Research Programme 2004/06. The Bartlett school of planning and Halcrow Group Ltd. Retrieved 1/18/2008 http://www.ucl.ac.uk/∼ucft696/vibat2.html]. The framework is being implemented as a prototype decision support system around a case study: the formulation and analysis of policies required to achieve CO2 emission targets for the transport sector in the UK. Important insights on how to develop the framework have also been elicited from engineering design. The goal is to accelerate the task of policy-making and improve the effectiveness of the resulting policies.The proposed method and computer implementation is fundamentally different from the tools commonly used in the transport sector and is intended to assist (not replace) transport policy makers, and complement (not substitute nor compete with) existing mathematical modelling tools. This research constitutes the first step towards the development of a general family of computer-based systems that support the design of policies to achieve environmental targets—not only for transport, but also for other sectors such as energy and water.

Two conceptual frameworks are especially important: included inside the dashed box in Figure 2(b)). regarding which measures to use and how to combine them to achieve the targets (the set of tasks

Figure 3. Packages, measures, and pathways (based on (Banister & Hickman, 2006b))

Figure 4. Policy formulation process The proposed policy formulation framework is broken down into six steps (see Figure 4) 4.2. Proposed framework for policy formulation

analyzed for cycles in order to acquire additional information to assist policy evaluation. The tree in Figure 6 represents several alternative policies. It is constructed incrementally using the

Figure 7. Software architecture of the decision support system integration between Java and Mathematica is established using J/Link (J/Link, 2008). mathematics package Combinatorica (Pemmaraju & Skiena, 2003; Pemmaraju & Skiena, 2004). The

5.3. Development of the library of policies

Figure 9. Example policy measure and its preconditions systems” policy measure has the rest of the policy measures as its preconditions. ‘onvention for the direction of the relation: in this example the “Advanced traffic managemen This section describes the results achieved so far in the development of the decision support syste

right hand side are preconditions to those in the left and thus should be implemented first. In the next step of the analysis, policy measures were grouped into different clusters based on the Some of the results from the analysis are presented in the rest of Section 7.1 and will be expanded information provided in the VIBAT project report. The main criterion for clustering was that the

Figure 11. Network of preconditions based on manually created clusters significant differences existed among measures that related to a certain goal, measures were separated policy measures supporting the same overall goal should be grouped together. In the cases where

Figure 12. Clustered network of preconditions with a directional flow from left to right to right). Policy measures in the same column have the same implementation time.

Figure 13. A sample target and goals structure After creating the target and goals structure, the next step is to connect it with the policy measures, in

Figure 14 A final view of the sample target and goals structure with the added layer of policy measures added layer of policy measures enabling the hybrid tree structure to grow automatically. measures. Figure 14 illustrates the final view of the user-generated target and goals structure with the

Figure 15. Generation and visualization of the first branch of the tree tree in Figure 15 illustrates the point showing the full expansion of a single branch with 17 nodes. Growth of the tree as described follows a breadth-first methodology, where all of the leaf nodes grow Figure 15 depicts the generation of the tree branch based on the methodology described. The level of

Figure 16. Example of a cycle in the network 7. Current Limitations and Future Work

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Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

by Jurgita Antuchevičienė and

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described... more

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Rough sets theory for multicriteria decision analysis

by Benedetto Matarazzo

2001, European Journal of Operational Research

... Krusinska, E., Slowinski, R. and Stefanowski, J., 1992. Discriminant versus rough set approach to vague data analysis. Applied Stochastic Models and Data Analysis 8, pp. ... Lin, T., 1989.Neighborhood systems and approximation in... more

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Les méthodes multi-critères pour analyser les aptitudes des terres agricoles : le cas du blé tendre en Languedoc-Roussillon, analysé avec la méthode AHP

by BEKHTARI M Cherif

Multicriteria methods to analyze the suitability of agricultural land: the case of wheat in Languedoc-Roussillon region analyzed with AHP method. Suitability analysis identifies the main factors limiting the production of a particular... more

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Network-centric Policy Design

by Araz 𝖳aeihagh

Abstract Two important challenges in policy design are better understanding of the design space and consideration of the temporal factors. Moreover, in recent years it has been demonstrated that understanding the complex interactions of... more

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A new TOPSIS-based multi-criteria approach to personnel selection

by Alecos Kelemenis

Selection of qualified human resources is a key success factor for an organization. The complexity and importance of the problem call for analytical methods rather than intuitive decisions. The aim of this paper is to support adequately... more

Fig. 1. Membership function of a triangular fuzzy number. An example of a membership function is shown in Fig. 1. Member- ship of 4 is 0.5.

Scale for weighting the criteria. Table 1

Fuzzy decision matrix. Scores of the alternatives in criteria (linguistic values).

Normalized fuzzy decision matrix. Table 9 Table 10

Weighted normalized fuzzy decision matrix.

Comparative results of different distance measurements. Table 11 a group of experts, each one of different importance, are involved in the decision making process. In this situation, the aggregation process or the consensus among the DMs is a main issue of concern.

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Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets

by Mehdi Keshavarz-Ghorabaee

The main goal of green supply chain management (GSCM) is to reduce the negative environmental impacts in all activities and stages of a supply chain. Evaluation of suppliers in a supply chain according to environmental criteria can help... more

Fig. 2. The framework for using the proposed approach. modification is performed in the WPM process of the proposed approach to avoid calculations with complex numbers. In the proposed approach, we use a procedure for calculation of criteria weights in which the weights expressed by decision-makers are combined with the weights resulted from the entropy method. We can obtain more realistic criteria weights for decision-making process by using this combination. Using IT2FSs gives more de- grees of flexibility to the decision-makers to express their prefer- ences in an uncertain environment. The framework for using the proposed approach is represented in Fig. 2.

Step 5. The average subjective weight of each criterion is calculated based on Step 4 and Eq. (27). The results are shown in Table 5. Cinwn EC Dacanrl nn Crank 9 Takla GC ana Br (9O@N <axecrawm pou lowlatectha

Ranking of the alternatives with different methods and the corresponding correla- tion (r;). Fig. 3. Seven sets of the criteria weights for sensitivity analysis. Table 14 analysis. We name these methods M-1 to M-6, respectively and the illustrative example is solved using them. To compare the results of the proposed approach with the other methods, we use the Spearman's rank correlation coefficient (rs) (Zavadskas et al., 2014). Table 13 shows the interpretation of different values of r; (Walters, 2009). According to this table, when the values of r; are greater than 6. Discussion

Linguistic terms and their corresponding interval type-2 fuzzy sets. Table 1

The average subjective weights of criteria. Table 5

The aggregated weights of criteria. Table 6

a The values of WPM measure (Q;_) for each alternative. Table 9

Losi sa 7 The values of WSM measure (Q;_) for each alternative. Table 8

Table 7 (continued ) Step 10 and 11. Based on Table 10, Table 11 and Eq. (34), we can calculate the WASPAS measure for each alternative (with A = 0.5). Table 12 shows the values of Q; and the corresponding crisp score values. According to this table, the ranking order of alternatives is A> Ag > A> A> AG > A> A 5 >A 4. Therefore, 7 is the best alternative. aggregated weights of criteria (with 6 = 0.5). Table 6 represents the results of this step.

The scaled values of WSM measure (Q; ~) for each alternative. Table 10

bone ieee? 2 The scaled values of WPM measure (Q; _) for each alternative.

The calculated WASPAS measure (Q;) and the corresponding crisp score values.

Interpretation of the correlation values (r;).

Ranking results with different sets of criteria weights and different parameters. Table 15 Multi-criteria decision-making methods have been widely used in the field of green supply chain management (Govindan et al., 2015). This study has considered the process of multi-criteria evaluation of green suppliers within the context of interval type-

Correlation (r;) between ranking results with different sets of criteria weights and different parameters. Table 16

References using a procedure for obtaining more realistic weights of criteria and increasing the stability of method. However, transforming the non-beneficial criteria to beneficial criteria by normalization is the main disadvantage of the proposed approach. Improving the method with respect to this disadvantage could be performed in the future researches. The proposed approach can be used for any problems that have the common structure of MCDM problems and use interval type-2 fuzzy information. Although this study has focused on problem with subjective criteria, the proposed approach can be used in a general situation. For the future researches, we suggest using some objective criteria and application of the pro- posed approach in other MCDM problems.

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Sustainability analysis of different hydrogen production options using hesitant fuzzy AHP

by Canan Acar

2018, International Journal of Hydrogen Energy

Hydrogen is seen as the key component of energy systems for a sustainable future. In the literature, there has been extensive efforts on making hydrogen energy systems more sustainable. True sustainability of such systems requires... more

Hydrogen is seen as the key component of energy systems for a sustainable future. In the literature, there has been extensive efforts on making hydrogen energy systems more sustainable. True sustainability of such systems requires hydrogen to be produced in clean, reliable, affordable, and safe manners without harming neither the environment nor the societies. In the literature, there is a lack of studies focusing on a complete technical, environmental, social, and economic evaluation of hydrogen production systems by taking availability and reliability into account. Therefore, the primary aim of this study is to provide a comprehensive review and investigation on sustainability of hydrogen production systems, which could potentially guide researchers, policy makers, different industries, and energy market customers. The selected hydrogen production methods are grid electrolysis (electricity from fossil fuels), wind electrolysis, PV electrolysis, nuclear thermochemical water splitting cycles, solar thermochemical water splitting cycles and photoelectrochemical cells. To deal with the ambiguity and vagueness in the evaluation process, and to overcome the observed hesitancy in decision makers’ preferences, a novel approach, hesitant fuzzy AHP, is used to evaluate sustainability of the selected hydrogen production methods. In the proposed model, five criteria; economic performance (initial cost and running cost), environmental performance (GHG emissions, land use, water discharge quality, and solid waste), social performance (impact on public health, employment and training opportunities, and public acceptance), technical performance (energy and exergy efficiencies, process control, and raw material input), and availability/reliability (dependence on imported resources, predictability, and scalability) are taken into account. The results show that grid electrolysis is expected to hold the key of sustainable hydrogen production in the near future while the technologies of other methods advance and their associated costs decrease.

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Can We Rely on Renewable Energy Sources to Overcome Global Warming?

by Kaveh Madani

2011, World Environmental and Water Resources Congress 2011

Greenhouse gas emissions, resulting from burning non-renewable energy sources, are recognized as the major cause of global warming. Hence, reducing carbon footprint of energy uses through replacing non-renewable energy sources with... more

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A multicriteria proposal for large-scale solar photovoltaic impact assessment

by Alessandra Magrini

2019, Impact Assessment and Project Appraisal

Large-scale photovoltaic (LSPV) may cause significant changes in the environment and lead to detrimental impacts on the natural and anthropic environments. First, this paper reviews the scholarly literature to collect data regarding the... more

Figure 1. Reduced AHP diagram for multicriteria decision-making on the environmental impact assessment of large-scale photovoltaic projects. In total, 64 leaf-level objectives are inserted into the model. Figure 1 shows the reduced AHP diagram for assessing environmental impacts of large-scale PV, all objective at level 2 are broken down until the leaf-level objective, see figure 4 and table 5 in the supplementary material. The AHP tree in the supplementary material is very comprehensive tackling the main aspects to be assessed on terrestrial and floating PV aspects. Its appli- cation can require adaptations such as suppression of As described in the ‘SAMAMBAIA concept’ section, the evaluation criteria should follow an increasing scale of degradation addressing the proj ect’s possible alterations on the environment. There is not a ‘right’ number of evaluation criteria at the eaf-objective, though the general recommendation is not to have too many (varying from 3 to 6) to facili ate the com- parison. The process is applied to all leaf-level objec- tive criteria to reduce environmental im pacts of LSPV and should be standard for impact assessment of PV Abbreviations: RNE: reduce the impact on the natural environment. RAE: reduce the impact on the anthropic environment. RAL: reduce the aesthetic impact on natural landscape. RBF: reduce the impact on biotic factors. RAF: reduce impact on abiotic factors. RIP: reduce the impact on populations. RSE reduce the impact on local socio-economic. RIT: reduce the impact on the territory. assessed on terrestrial and floating PV aspects. Its appli-

Figure 2. Specialist assignment of magnitudes in the evaluation criteria PTH and respective preference value function estimated through matrix of judgement and eigenvector method. Key legend: PA: project area. DNI: area of direct and near interaction. AMI: area of moderate interaction. ECI: area of economic interaction. NM: Normalised magnitude. Values are merely for description.

N/A: not available or not stated in the EIA. The analysis covered the EIA and its methodology to assess environmental impacts, the status of the project (construction or operation) is not given at this point. Table 1. Large-scale solar PV and main methods to assess their environmental impacts.

Table 3. Assessment matrix and assignment of magnitudes. Different from typical AHP studies that assign weights to the objectives on the bottom of the hierarchy, our model structure does not have a distinguished bot- tom line with the project alternatives. All ranking criteria used to minimise the detrimental impacts are addressed during the AHP tree and in the leaf-level objective criteria. The following procedure is standard for the method. The consulting firm in charge of the EIA surveys for experts (biologists, engineers, socio scientists, etc.) to implement the scoring in their respective field; the final magnitude reflects the group decision for each evaluation criteria and should be used as a standard for PV project. The Saaty eigenvector is also applied to construct the preference value function and provide the consistency ratio (CR) of the previous score. This method aids stakeholders to judge the pair-wise scores to mini- mise subjectivity in the analysis (Al Garni et al. 2016). The linear aggregation sum approach is used to estimate the cumulative total value magnitude crosswise criteria, similar to (Laken 2007; Huang et al. 2011; Klein 2013). Figure 2 visually expresses the steps by providing an example created to pair-wise the leaf-level objective ‘reduce the impact on the physical terrestrial habitat (PTH)’ and the subordinated preference value function. Different MCDA might adopt other scaling score, such as Saaty scale (Haurant et al. 2011; Rahman et al. 2016). addressed during the AHP tree and in the leaf-level

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Multi-Criteria Service Recommendation Based on User Criteria Preferences

by liwei liu

Research in recommender systems is now starting to recognise the importance of multiple selection criteria to improve the recommendation output. In this paper, we present a novel approach to multi-criteria recommendation, based on the... more

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PROMETHEE and AHP: The design of operational synergies in multicriteria analysis

by alain verbeke

2004, European Journal of Operational Research

This paper discusses the strengths and weaknesses of the Preference Ranking Organisation MeTHod for Enrichment Evaluations (PROMETHEE) and analytic hierarchy process (AHP) methods. Building upon this analysis, recommendations are... more

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Multikriterielle Optimierung: Eine Übersicht

by Hanne Thomas

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Aplicación de teorías de decisión multicriterio en la selección de predios en procesos de estructuración de proyectos inmobiliarios

by José Luis Ponz Tienda

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Mapeamento de áreas prioritárias para ações de conservação e recuperação ambiental na bacia do rio Descoberto

by Thaiane V M N Santos

2016

A Bacia Hidrográfica do Rio Descoberto possui 1.289,42 km² e está localizada na divisa oeste entre o Distrito Federal e o estado de Goiás, sendo responsável por mais de 60% do abastecimento de água destinado ao consumo humano no Distrito... more

A Bacia Hidrográfica do Rio Descoberto possui 1.289,42 km² e está localizada na divisa oeste entre o Distrito Federal e o estado de Goiás, sendo responsável por mais de 60% do abastecimento de água destinado ao consumo humano no Distrito Federal. Sua região vem sofrendo uma intensa ocupação do solo que resulta na conversão de áreas naturais em áreas agrícolas e urbanas, o que tem afetado a quantidade e a qualidade da água da bacia, de modo que ações de recuperação ambiental e conservação do solo são agora necessárias. A análise multicritérios é um método que permite a análise de várias
alternativas, útil para o manejo de bacias hidrográficas pois utiliza vários critérios relacionados ao objeto de estudo, possibilitando identificar alternativas mais adequadas dentre as consideradas. Nesse contexto, o presente trabalho tem como objetivo selecionar áreas prioritárias para conservação de solo e recuperação ambiental na Bacia do Rio Descoberto, visando ao aumento da quantidade e da qualidade da água, com base em análise de múltiplos critérios em ambiente SIG. Para atingir os objetivos, foram analisados os usos atuais das Áreas de Preservação Permanente (APPs) e para as demais áreas da bacia foi feito uma análise multicritério para definir a prioridade de ações de
conservação de solo e recuperação ambiental, segundo os fatores: Distância à rede de drenagem, Fator Número-Curva (CN), Erodibilidade e Declividade. Foram necessários os planos de informação: limites da bacia hidrográfica, mapa de uso e cobertura do solo, rede de drenagem, mapa de solos, mapa de áreas urbanas e modelo digital de elevação –
DEM. Os resultados obtidos foram o mapa de APPs e os respectivos usos do solo identificados nessas áreas e o mapa de áreas prioritárias para ações de conservação de solo e recuperação ambiental nas áreas fora das APPs. Ambos os mapas finais foram reclassificados, de maneira a apresentar três graus de prioridade. Observou-se que 31,70%
do total de APPs da Bacia do Rio Descoberto foram consideradas de alta prioridade para ações de recuperação, por estarem em áreas urbanas, com solo exposto ou sob cultivos agrícolas. Em relação à análise multicritérios, 47,01% da área analisada foi classificada como de alta prioridade de intervenção, por apresentar simultaneamente valores críticos para todos os fatores. Foi verificado o potencial da região para a aplicação de programas de pagamentos por serviços ambientais, visto que foram identificadas diversas situações em que investimentos são necessários, como na recuperação de áreas de preservação permanente e na adoção de boas práticas de manejo em cultivos agrícolas.

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Fuzzy outranking for environmental assessment. Case study: iron and steel making industry

by Thomas Spengler

2000, Fuzzy Sets and Systems

Recent years have seen a world-wide change in the environmental policy towards integrated pollution prevention, taking into account all environmental media. Consequently, the environmental assessment of production techniques has to... more

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Multiple criteria group decision-making for supplier selection based on COPRAS method with interval type-2 fuzzy sets

by Mehdi Keshavarz-Ghorabaee

2014

Supplier selection is one of the most critical activities of purchasing management in a supply chain because of the key role of supplier’s performance in achieving the objectives of a supply chain. Supplier selection problem requires a... more

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Genetic algorithms in bus network optimization

by Maurizio Bielli and

2002

This paper focuses on a new method to compute ®tness function () values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus... more

Fig. 8. The transit planning process — Ceder and Wilson (1986). Referring to the framework proposed by Ceder and Wilson (1986) on the transit planning process (Fig. 8) this work tries to unify the planning activities at level A and level B. In fact,

Fig. 1. Layout of a GA implementation. M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 e GA uses objective function information and not derived or auxiliary knowledge; e GA uses probabilistic transition rules and not deterministic ones.

The second operator denoted simple crossover (see e.g., Chambers, 1995; Goldberg, 1989; Michalewicz, 1992) works as follows: the members reproduced in the new mating pool are mated randomly and afterward each pair of strings undergoes a cross-change. In order to do this, an integer position k (cut point) is selected uniformly at random among the strings between position | and the string length / less one. Two new strings are created swapping all genes (with the cor- responding al elic values) between the selected position k and the end of the string. Fig. 3 shows a simple crossover operator.

Fig. 5. A solution procedure for the urban network design problem — Cantarella and Vitetta (1994). M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34

Fig. 6. The simple genetic algorithm — Xiong and Schneider (1993). M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34

M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 Fig. 7. The cumulative genetic algorithm — Xiong and Schneider (1993).

Fig. 9. Genetic representation: chromosome and cut points (network).

M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 Fig. 11. Crossover operator implementation.

(2) The second phase is a routine that aggregates the output of the assignment phase in order to btain a set of performance indicators (Bielli et al., 1996) reported in Table 1. The first column in he table reports indicators and the second their abbreviations. The third column contains two ymbols: W means that the lowest the value, the best the indicator, while A means that the highest he value, the best the indicator. The fourth column defines the type of the indicator: effectiveness

M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 Fig. 13. The network representation for the flow assignment phase.

Fig. 15. Details of the fitness function evaluation. M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34

M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 geometrical structures. Parma? is formed by 20 circular lines, involving 1130 nodes, 3006 arcs and 459 stops. Parma3 is constituted by 20 semicircular lines, where each line starts from a suburban node, goes through the centre of the city, where it first makes a semicircular route, and then gets to another suburban node. Parma3 is formed by 1092 nodes, 2892 arcs and 459 stops. Each one of the considered bus networks has 99 centroids and 685 pedestrian nodes which allow to complete the two-level representation given in the previous section. What we have done is to create four initial chromosomes having lensth 80. being the tota

Fig. 17. Fitness function values for each generation. M. Bielli et al. | Transportation Research Part C 10 (2002) 19-34 1.0, and a mutation probability 0.0, 0.1, and 0.2. Results show that the best ff is obtained in correspondence to a 0.8 crossover probability and a 0.1 mutation probability. We remark that our computation was conducted on 100 generations and the best ff value was obtained in corre- spondence to the 66th generation. Moreover, the best initial ff value, i.e., the best ff value among the four initial bus networks, was 0.47. In Fig. 17. we renort the granhic representing the trend of the f# with resnect to the number of

quency represented by the value of the other allele in that gene. Finally two further considerations can be done (Fig. 16). The first one is related to the intro- duction of a n-best solutions container, that during the whole process stores a predefined number of good solutions generated. This container is very useful to perform an off-line sensitivity analysis on the weights used in the multicriteria analysis, in order to accept the results obtained or perform a new run with a new set of weights. rest 1 4 1+, 4 oq °4e, 7 4 41 7 1 . 1

Best fitness function values for different combinations of crossover and mutation probability

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GIS‐based multicriteria decision analysis: a survey of the literature

by Kaleab Degefaw

2006, International Journal of Geographical Information …

The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so. This paper surveys the GISbased multicriteria decision analysis (GIS-MCDA) approaches using a literature review... more

4.2.1 Multiattribute and multiobjective decision analysis (MADA and MODA). Criterion is a generic term including both the concept of attribute and objective. Thus, MCDA is used as the blanket term which includes both multiobjective and multiattribute decision-making. The multiattribute decision problems are assumed to have a predetermined, limited number of alternatives. Solving this type of problem is a selection process as opposed to a design process. The multiobjective problem is continuous in the sense that the best solution may be

Table 1. The list of refereed journals that have published four or more articles on GIS-MCDA in 1990-2004.

Fable 2. Classification of the GIS-MCDA articles according to the GIS data model and evaluation criterion. research that involved explicitly and implicitly spatial criteria, respectively. Examples of the former category include: Eastman et al. (1995), Brookes (1997), Cromley and Hanink (1999, 2003), Seppelt and Voinov (2002), and Church et al. (2003). The articles by Nevo and Garcia (1996), Brakewood and Grasso (2000), Fuller et al. (2003), Perez et al. (2003) and Store and Jokimaki (2003) provide examples of the raster-based implicitly spatial criteria. Similar classification for the vector-based GIS-MCDA showed that there were 20 (13.3%) articles reporting the use of explicitly spatial criteria (e.g. MacDonald 1996, Weigel and Cao 1999) and 12 (8.0%) studies involved implicitly spatial criteria (e.g. Vertinsky et al. 1994, Kachele and Dabbert 2002).

Table 4. Classification of the GIS-MCDA articles according to the combination rule. Note: “some articles presented more than one combination rule.

Note: percentages of the total are given in brackets. Table 5. Classification of GIS-MCDA papers according to the type of multicriteria decision method for individual decision maker.

Table 7. Classification of the GIS-MCDA articles according to the extent of integration and the direction of integration of GIS and MCDA. Note: the numbers in parentheses are the percentages of the total number of articles.

Table 8. Classification of the GIS-MCDA articles according to the extent of integration and the application domain and decision problem.

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Criteria in AHP: a Systematic Review of Literature

by Rosária F S M Russo

The Analytic Hierarchy Process (AHP) is widely used by decision makers and researchers. The definition of criteria and the calculation of their weight are central in this method to assess the alternatives. However, there are few studies... more

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Land management with GIS and multicriteria analysis

by Andre Musy

2000, International Transactions in Operational Research

In land management, con¯icts are more frequent and stronger. Any important project on land becomes dicult to implement. MAGISTER (Multicriteria Analysis with GIS for TERritory) model proposes a decision support method to integrate... more

Fig. 1. MAGISTER model. MAGISTER proposes to consider that an alternative is not only an area with a sufficiently

Fig. 2. Alternatives are defined as homogenous zones. The global concordance index characterises the relative importance of the criteria considered to be in concordance with the affirmation “A and B are close’. It is computed by the weighted average of partial concordance index (c;) evaluated for each criterion (Fig. 3):

Weights express the relative importance of criteria in the closeness relation. The partial concordance index is a function of the difference between two elements (on the considered criteria) and the thresholds defining indifference and the strict difference (Fig. 3).

Fig. 4. Partial discordance index. The zones formation begins with a classification of the elementary surfaces (Fig. 5). This operation can be realised by an ordinary algorithm (unsupervised classification). Then, the

Fig. 5. Applied process to construct the homogeneous zones. Es; means elementary surface. Land management services in public administration have frequently to take quick decisions without being able to analyse the consequences. To avoid this situation, we propose to use MAGISTER to establish land suitability maps, as a decision tool for public administrations (Fig. 6). These maps should integrate all the relevant data and some precise analysis. Their

Fig. 6. Land management by using land suitability maps. MMPOLldAHee OL AITO diiG Ol spdlidl diidlyoslis lil LUIS ULI Id ©VdlUuduOl. Land suitability maps are based on eight criteria: impacts, air quality, noise, accessibility climate, landslide, technical networks (water, electricity, etc.) and view. These criteria hav been evaluated by use of simulation models (solar radiation, noise analysis (Fig. 7). The study area contains more than 80,000 land units 625 m?. By use of a homogeneity index a total of 650 zones composi were formed (Fig. 8). Each zone is described by nine fields. These field eight criteria and the value of the homogeneity index. To be consid zone must have an index greater than a chosen threshold (0.8 for t pro ng Ss his threshold will increase the numbers of homogeneous zones. Obvious y> pagation) and spa (pixels) with a surface the set of alternative: ore the scores for ered as homogeneous, ¢ map). Increasing t heir surface will a ia oO he hi S¢ become smaller. Decision-makers should give their point of view about the value o indifference, strict difference and veto. Then, the threshold has to be fixed in order to obtain « reasonable number of alternatives. However, it should not be smaller than 0.6 neither bigge than 0.9. A value smaller than 0.6 would mean that the homogeneity conditions could be quit easily transgressed, while a value bigger than 0.9 would impose very strongly these conditions.

Fig. 7. Examples of maps resulting from the criteria evaluations. From left to right and top to bottom: noise, climate, distance to technical networks and impacts. Clearer areas are better suited for housing.

Fig. 8. Map of homogeneous zones. The thin lines delimit the 650 homogeneous zones. One of them is presented in black, in order to show that a zone can be composed of several separated areas.

Fig. 9. Example of a land suitability map for housing. _— Homogenous zone construction introduces three new parameters in the decision support process (indifference, strict difference and veto). However, these parameters are analogous to those used in ELECTRE methods (indifference, preference, and veto). Therefore, to simplify the decision-makers involvement, the analyst can either choose the same value, or either deduce one set of parameters from the other set.

Intermediate results during the homogeneous construction process Table 1

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La Decisión Multicriterio; Aplicación en la Selección de Ofertas Competitivas en Edificación

by José Luis Ponz Tienda

LA DECISIÓN MULTICRITERIO; APLICACIÓN EN LA SELECCIÓN DE OFERTAS COMPETITIVAS EN EDIFICACIÓN 7 14 de que estos son adecuados. Generalmente en los procesos de Análisis de Decisión Multicriterio se pueden diferenciar las siguientes 7... more

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artículo_CLAS.pdf

by Benjamin Gossweiler

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Paradigms of mangroves in treatment of anthropogenic wastewater pollution

by Fen Guo and

2016, Science of the Total Environment

Mangroves have been increasingly recognized for treating wastewater from aquaculture, sewage and other sources with the overwhelming urbanization trend. This study clarified the three paradigms of mangroves in dis- posing wastewater... more

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Stochastic Multicriteria Acceptability Analysis (SMAA)

by Risto Lahdelma

2010, Trends in Multiple Criteria Decision Analysis

Fig. 1.3. Two domains of competence set analysis

Let us also suppose that only the objective function coefficients are known ex-
actly and that, by hypothesis, all the constraint matrix lines correspond to quantities
that are expressed in the same type of unit (e.g., physical, monetary). The values of
the constraint matrix coefficients are uncertain. A finite set S' of variable settings
allows this imperfect knowledge to be modelled. In the general case, it is possible
for the intersection of the feasible domains of all the variable settings to be empty.
In addition, even if this intersection is not empty, the price of robustness, as Soyster
refers to it, can be high. The decision maker might accept an unfeasible robust so-
lution in a small subset of S,, but only if the cost is relatively low. In fact, it is often
acceptable in practice to not respect equality; however, in this case, it is important
for the non-zero deviations between the right and left members to be “small” and
few in number. Thus, an unfeasible mathematical solution may be preferable to a
much more costly solution that perfectly satisfies all the equalities. For a solution x,
the deviations that must be taken into account are defined as follows:

methods which define the different approaches to problem structuring and MCDA
can be effectively combined. The diagrammatic representation of the MCDA pro-
cess in Fig. 8.1 suggests a natural way to do so, with the problem structuring phase
supported by one of the more general PSMs and providing a rich description of
the problem from which an appropriate multicriteria model may be derived. This
way of working, illustrated in Fig. 8.3A, provides the foundation for an Honours
year class, entitled “Problem Structuring to Evaluation”, which one of the authors
has taught, together with Fran Ackermann since 2003. Whilst there is rich potential
for interaction and iteration between models, in reality this is dependent on practical
circumstances, in particular constraints on time and resource. Figure 8.3B illustrates
a way of working which is perhaps more likely if using MCDA with one of the more
focused PSMs (see, for example [61]); however, such an intervention might be fur-
ther enhanced if initiated and supported by the use of a general PSM, as shown in
Fig. 8.3C.

Fig. 8.4 Causal map from Hawston workshops
As illustration, the causal map extracted from the Hawston/Hermanus commu-
nity is shown in Fig. 8.4. A central theme is probably that of concept 103, namely
that the “wrong” people were receiving rights, rather than the traditional fishing
communities. A number of reasons for this feature are identified, suggesting pos-
sible courses of action and external forces. More importantly to the interests of
MCDA, a small number of concepts appear near the top of the map, and which
are at the end of chains of links. These concepts can then be interpreted (at
least tentatively) as the primary objectives of the rights allocation process as per-
ceived by the Hawston/Hermanus community, namely (a) reduction of poverty and
unemployment in the community, (b) countering of gangsterism and crime, and
(c) preservation of stocks.

Although, different structures emerged from the three community groups, it was

Fig. 8.5 Overall value tree for fisheries rights allocation
ee ee

Interaction with the relevant government department, the Marine and Coastal
Management (MCM) directorate of the Department of Environmental Affairs and
Tourism, was carried out separately from that with community representatives. The
process was similar, but also included a deconstruction of effective criteria that
were in operation in previous rights allocations (even if not explicitly stated at the
time). Similar structures evolved, and the objectives identified with MCM did in-
clude those identified by the communities. However, additional criteria emerged.
notably those related to concerns for financial stability of the industry and regional
economic development. Finally, an aggregate value tree incorporating all concerns
could be tabled as a basis for future rights allocation decisions. This tree is presented
in Fig. 8.5.

The individual interviews revealed a strong consistency in employee values and
desire to see the company grow in order to realise, eventually, a “pot of gold”. How-
ever, different perspectives on how this might be achieved were apparent and the
interview process led one employee to realise that they had more substantial con-
cerns about developments that would ensue from accepting the venture capital than
they had previously thought. The key concepts from the individual maps were con-
solidated into one map (Fig. 8.6) by the team of facilitators before the workshop and,
as this was of a manageable size, it was manually recreated step-by-step in front of
the group of employees. As the map unfolded, participants were able to comment
on the concepts and links and to further develop these.

Se ee ee ee er i . . , . i fe

some of the issues and uncertainties faced. This was complemented by a stakeholder
analysis, which sought to identify and map all stakeholders against the two dimen-
sions of interest in the issue and their power to influence outcomes (positively or
negatively). See Eden and Ackermann [35, p121—125] for a more detailed discus-
sion of this type of analysis. This power-interest grid groups stakeholders in four
categories: players (high interest and power) need to be managed closely; subjects
(high interest, low power) should be kept informed; context setters (high power but
low interest) need to be kept satisfied; and the crowd (low interest and power) who
should be monitored.

Fig. 8.9 Multicriteria model for King Communications and Security Ltd

concepts. There did not seem to be a need for causal mapping, as the “alterna-
tives” were defined by the applicants, and the stakeholders and environment were
self-evident. The MCDA perspective led to using emergent criteria as the rationale
for clustering. In between the first and second workshops a value tree could thus
be structured and circulated to participants, and was broadly accepted in the form
displayed in Fig. 8.10.

Fig. 9.1 Necessary partial ranking at the first iteration
Fig. 9.2 Necessary partial
ranking at the second iteration

Analyzing this first result, the DM observes that the necessary ranking is still
very poor, which makes it difficult to discriminate among the solutions in A. He/she
reacts by stating that s4 is preferred to ss. Considering this new piece of preference
information, the necessary ranking is computed again and shown in Fig. 9.2. At this
second iteration, it should be observed that the resulting necessary ranking has been
enriched as compared to the first iteration (bold arrows), narrowing the set of “best
choices,” i.e., solutions that are not preferred by any other solution in the necessary
ranking: {s1, 53, 56, Sg, S10, 514, 515, 8517, S18, 519, S20 },

The DM believes that this necessary ranking is still insufficiently decisive and
adds a new nairwise comparison: se is preferred to sin. Once again. the necessarv

Fig. 9.3. Necessary partial ranking at the third iteration

Fig. 10.1 Traditional approach: decision model determines the “best” solution based on criteria
measurements and DMs’ preferences

Fig. 10.2 Inverse approach: identify preferences that are favourable for each alternative solution

be maximized
Table 10.1 Problem with four alternatives and two criteria to
be maximized

Fig. 10.4 Favourable rank weights and rank acceptability indices in deterministic 2-criterion case
with linear utility function
tee a eee ee ee Ee Se Ee ee eee ee a

Besides the weights that make an alternative x; most preferred, we can als
identify the sets of favourable rank weights W,’ that give a particular rank r fc
alternative x;. Figure 10.4 illustrates the favourable rank weights for the alternz
tives. We can see that alternative x2 will always obtain either rank | or 2 and neve
rank 3 or 4, while x4 can only receive either one of the two last ranks. The rele
tive size of set W,” is the rank acceptability index, and it measures the variety c
weights that give rank r to alternative x;. We can see that the second and third ran
favourable weights are not necessarily connected sets. Therefore the midpoints c
the favourable rank weights are meaningful only for the first rank.

The weight space analysis is simple to perform graphically or analytically i

Fig. 10.5 Rank acceptability indices in deterministic 2-criterion problem

Fig. 10.6 Examples of simulated cardinal values for four ranks
is represented by a directed acyclic graph between the alternatives.

In SMAA-O, ordinal criteria are treated by simulating corresponding (unknowr
cardinal criteria values. The best alternative(s) corresponds to cardinal value
and the worst alternative(s) corresponds to 0. In the case of a complete ranking
the intermediate ranks correspond to values between 0 and 1. These values ar
generated randomly from a uniform distribution, sorted into descending order, an
assigned for the intermediate ranks. Equally good alternatives on the same ran
level are assigned the same value. In the case of a partial ranking, sorting can b
implemented efficiently using a variant of the topological sorting algorithm [22
Figure 10.6 illustrates random cardinal values generated for a complete rankin
with four different ranks.

— 1 1 1 7 a 1 ee er 7 1 le ae 1

In the extreme case, no weight information is available. However, even in that case,
we can assume something about the weights. Normally the MCDA problem can be
formulated so that all DMs want to maximize the outcome for each criterion. There-
fore, we can assume that the weights should be non-negative. Also, practically all
decision models are insensitive to scaling all weights by a positive factor. There-
fore, we can, without loss of generality, assume that the weights are normalized
somehow, e.g., so that their sum equals |. This means that the feasible weight space
is an (n — 1)-dimensional simplex (see Eq. 10.3). Figure 10.7 illustrates the feasi-
ble weight space in the 3-criterion case as a triangle with corners (1,0,0), (0,1,0) and
(0,0,1). We therefore represent missing weight information by a uniform weight dis-
tribution in the feasible weight space. Because the uniform distribution carries the
least amount of information, it is well justified to use it to represent missing weight
information.

linear inequality constraints based on the intervals. Generation of weights from the
restricted distributioncan be implemented easily by modifying the above procedure
to reject weights that do not satisfy the interval constraints. Upper bounds can be
treated efficiently using the rejection technique, but in high-dimensional cases lower
bounds may cause a high rejection rate. A special scaling technique can be applied
to treat lower bounds efficiently [69]. Figure 10.9 illustrates the resulting weight
distribution. The interval constraints are parallel to the sides of the triangle.

importance ranking for some criteria (w; ? w;) or equal importance (w; = wz ;). In
the general case, ordinal preference information means that the DMs’ preference
statements correspond to a partial importance ranking of the criteria. Multiple DMs
may either agree on a common partial ranking, or they can provide their own rank-
ings, which can then be combined into a partial ranking that is consistent with each
DM’s preferences. Without any consensus, the combined order may result into ab-
sent preference information.

ru . _ y ae 1 cr aot 4, a ao ae

Implicit weight information can result, e.g., from DMs’ holistic preference state-
ments “alternative x; is more preferred than x;”. This can be expressed as x; > Xx,
and it means that the weights and other possible preference parameters must be such
that the decision model ranks the alternatives consistent with the preference state-
ments. In the case of an additive utility or value model, such holistic constraints
result into general linear constraints in the weight space, as illustrated in Fig. 10.12.
In the general case, with non-additive utility/value function model, outranking mod-
els, etc., holistic constraints correspond to non-linear constraints in the weight space.
The rejection technique can be used to generate the weights with arbitrary decision
models.

To this effect, a recent study [32] has demonstrated that a particular EMO procedure,
starting from random non-optimal solutions, can progress towards the theoretical
Karush-Kuhn-Tucker (KKT) points with iterations in real-valued multi-objective
optimization problems. The main difference and advantage of using an EMO com-
pared to a posteriori MCDM procedures is that multiple trade-off solutions can be
found in a single run of an EMO algorithm, whereas most a posteriori MCDM
methodologies would require multiple independent runs.

Tee QGaencee 1 Af thw CRAATL *\.. hee eee bee ils eee Ae BRA Hee Sie bcaw

Fig. 12.9 Obtained nondominated solutions using NSGA
Fig. 12.10 Four trade-off trajectories

Fig. 13.1 Vector and raster data models in GIS
GIS utilizes two basic types of data: spatial data and attribute data [76]. The former
describes the absolute and relative locations of spatial (geographic) entities (e.g.,
building, parcel of land, street, river, lake, state, country, etc.). The attributes re-
fer to the properties of spatial entities. These properties can be quantitative and/or
qualitative in nature. Attribute data are often referred to as tabular data.

Spatial data are typically arranged in a GIS using one of two models: vector and
raster (Fig. 13.1). Entities in vector format are represented by strings of coordinates.

Although the advent of desktop computing and cartographic modeling in the 1980s
was instrumental in stimulating the development of GIS-MCDA, it was not until
the 1990s that GIS-MCDA established itself as an identifiable subfield of research
within the GIS literature [16, 29, 36, 66, 82, 104, 105, 115, 118, 145, 173]. The pro-
liferation of GIS-MCDA can be illustrated by examining the increasing number of
relevant publications in refereed journals. Figure 13.2 shows that there has been an
exponential growth of the number of refereed publications on GIS-MCDA in the
post-1990 period [18]. The rapid increase in the volume of GIS-MCDA research
can be attributed to two main factors. First, during the 1990s, increasingly powerful
personal computer-based GIS software was developed, refined, and utilized in ap-
plications. GIS has gradually been regarded as a routine software application within

Table 2.1 Data for an example on international business management (Empl. = employees)

Table 2.2, An example of objective ranking and classification for the data from Table 2.1

attachment (JAIST consists of three schools), nationality (Japanese or foreign — the
latter constitute over 10% of young researchers at JAIST), research position (master
students, doctoral students, research associates, etc.). In total, the data base was not
very large, but large enough to create computational problems.

52

180
145
130
120

90
125
125
140
125
130
135
140
150
125
130
140
130
135
130
125

53
170
120
175
190
210
135
175
190
190
175
110
190
150
155
145
190
155
145
135
125

S4
175
155
170
195
205
150
175
195
165
195
120
190
165
160
150
195
135
130
155,
135

S5

165
165
175
190
200
145
175
190
165
160
195
170
160
155
165
200
130
125
150
155

160
195
175
195
205
150
190
195
155
155
150
175
150
145
170
195
120
145
145
150

175

175

160

165

125

140 1

175

200

170

S9

160
165
170
190
190
165
175
190
165
170
150
165
140
155
195
190
145
125
150
155

S10

175
170
175
195
195
195
175
195
150
155
130
160
150
160
170
190
125
130
145
150

Si]

170
155
190
190
195
155
190
180
150
145
145
190
165,
155
155
195
195
135
135
145

S12
165,
160
190
195
200
145
195
185
165,
140
155
165
170
160
150
160
135
195
140
135

S13
155
165
195
190
205
170
190
180
170
155
145
170
145
165
165
175
125
160
195
155

S14

160
170
190
195
210
155
195
185
165
160
160
190
160
170
160
160
155
155
150
195

S15
170
155
195
190
215
145
190
180
160
155
155
190
170
155
155
175
135
150
145
140

S16
165
155
190
200
210
140
195
185
155
160
150
190
155
145
170
160
120
145
155
145

S17
170
170
195
190
210
145
190
180
165
155
135,
190
170
165
165
150
110
140

S18
165
165
190
195
205
135

180
165
170
160
170
175
135

Sig S

180
195
195
200
220
160
200
125

150

145 150 190

160

165

180

Table 5.1 Parametric families of continuous distributions

Table 5.2. Cycle-transitivity for the continuous distributions in Table 5.1

In the cases of the unimodal uniform, Gumbel, Laplace and normal distribu-
ions we have fixed one of the two parameters in order to restrict the family to a
yne-parameter subfamily, mainly because with two free parameters, the formulae
yecome utmost cumbersome. The one exception is the two-dimensional family of
1ormal distributions. In [18], we have shown that the corresponding reciprocal rela-
ion is in that case moderately stochastic transitive.

to MCDA, together with System Dynamics and Viable Systems Modelling are also
briefly outlined, as “near neighbours” of PSMs [83, Chapter 12]. Other approaches
which it is felt might lay claim to share the same characteristics [83, p xv] are Ack-
off’s Idealized Planning, Mason and Mitroff’s Strategic Assumption Surfacing and
Testing and Saaty’s Analytic Hierarchy Process. The key features of the five prin-
cipal approaches and the potential for integration with MCDA are summarised in
Table 8.1.

such a comparison is suggested in Table 8.2.

Of course, MCDA does not end with structuring. On occasions, a good struc-
uring may make the solution self-evident, but in most cases the analytical or
onvergent phase of MCDA will follow the structuring. The advantage of using the
VICDA framework as a template for structuring is that the transition from divergent
tructuring to convergent analysis is essentially seamless. The general feature of the
‘ase studies above was that the analytical models (in these cases multiattribute value

unctions, but the method is not central) flowed out of the structuring as a natural
‘onsequence.

The existence of a collective preference therefore presents a dilemma: We must
either accept that group preference can be intransitive, or we must make one person
a dictator. The first option is to accept that even though A is preferred to B and B
to C, it does not follow that A is preferred to C, as transitivity would require—in this
case, in fact, the opposite preference holds. The second option is to accept that a
collective preference is transitive only because one person is more important than
the other two combined. If the collective ordering in the Condorcet example A to
B to C, for instance, then person | is more important than 2 and 3 combined, in
that 1’s preference of A to C outweighs 2’s and 3’s preferences of C to A. Simply
put, person | a dictator, so the group preference is the same as 1’s preference, and
2 and 3 simply don’t matter. In any case, we must accept that even when individual
preferences are well-behaved, a well-behaved group preference that reflects all in-
dividual preferences may not exist.

Further Investigation: In 2002, after several meetings and public consultations,
Kitchener City Council adopted alternative A!. In a study of this project [16], the
authors reanalyzed the decision process by applying MCDA tools to A!, A”, and
A?, the three alternatives that survived the screening process. Criteria were selected
for the evaluation of these alternatives, as follows: C,: protection of human health
and the environment; C2: acceptability to the community; C3: operational and main-
tenance costs; C4: expected property tax returns; Cs: effect on property values; C¢:

demolition time; C7: amount of waste to be excavated.
Tha analxoie wae pnnndirtead wocing tho Awnalsstinr Li ararvnhty Dennancc (A LID)\ FQ21

This rather typical environmental conflict was studied by Kilgour et al. [54], who
assessed what negotiation support could have been provided by the Graph Model
‘or Conflict Resolution and the DSS GMCR II [34, 53]). The application of GMCR
I takes into account not only multilateral negotiation, but also the possible benefits
of coalition formation. Essentially, one asks how well each DM can do on his or
1er own, and then whether some group of DMs would benefit by cooperating in a
-oalition.

Table 13.1 Classification of the GIS-MCDA articles according to the GIS data model, the spatial
dimension of the evaluation criteria (EC), and the spatial definition of decision alternatives (DA)

Table 13.2 Classification of the GIS-MCDA articles according to the multicriteria decision rule
multiattribute decision analysis (MADA) and multiobjective decision analysis (MODA). Some
articles presented more than one combination rule

13.4.3 MCDA Components of GIS-MCDA

Note: percentages of the total are given in brackets
Table 13.3 Classification of GIS-MCDA papers according to the type of multicriteria decisior
method for individual decision maker

Note: percentages of the total are given in brackets
Table 13.4 Classification of GIS-MCDA papers according to the type of multicriteria decision
methods for group decision making

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A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria

by Mehdi Keshavarz-Ghorabaee and

Evaluation of airlines based on service quality criteria can help to improve the processes of airlines, and also can give guidance to travel agencies to provide better choices for passengers and tourists. In this study, a hybrid... more

Fig. 1. An example of the PDF of a PERT distribution.

Fig. 2. The flowchart of the proposed approach.

Fig. 4. Normalized and aggregated ranking scores in z = 2 and different values of N. values of N, and the stability of these values increase when we run the proposed approach with higher number of iterations. This fact can also be seen in the normalized ranking scores of TOPSIS, CO- PRAS, WASPAS and EDAS. The instability of the ranking scores can lead to instability of final ranking of alternatives. However, in these figures we can see little variation in the ranking scores after N = 1000.

The multi-criteria decision-making approaches have been used in many fields of science and engineering. However, there are still some issues to deal with MCDM problems with a group of decision- makers and validation of the evaluation results. In the case of group decision-making, when we are confronted with a few members in the group of decision-making, handling the problem is not difficult. To solve the MCDM problems, the values of criteria weights and performance values of alternatives should be specified first. The Fig. 5. Normalized and aggregated ranking scores in z = 3 and different values of N.

Fig. 6. Normalized and aggregated ranking scores in z = 4 and different values of N.

Fig. 7. Normalized and aggregated ranking scores in z = 5 and different values of N.

Fig. 8. The computational time of running the proposed approach.

The service quality criteria for airline evaluation. Table 1

The PERT distribution parameters for performance values of the alternatives.

The PERT distribution parameters for criteria weights.

The aggregated ranking scores in different numbers of iterations.

The ranking results in different numbers of iterations. Table 5

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Structuring transport decision-making problems through stakeholder engagement: the case of Catania metro accessibility

by Giuseppe Inturri and

This paper presents a procedure for the structuring of a problem hierarchy by involving key stakeholders, rep-resenting a first step towards a participatory decision-making process. The case study re-gards the building of a new metro... more

Table 7. Criteria and sub-criteria definition for the hierarchy model

Figure 2. Area of intervention By the end of 2016 it is expected the ex the line until the sta “Galatea” to Piazza S ion ‘“Nesima” and planned the opening of a branch linking t esicoro. In a few ension of it’s also he station months a new subway station, named Milo, will be inaugurat- ed. Its position will make it the closest sta ion and health services and a area where some educati ion to an park — and — ride facility (S. Sofia parking) are locat- ed (Fig2). Due to slope and distance between Milo station and S. Sofia parking, the implementation of a transit service linking the two transport nodes has been proposed.

Table 4. SWOT analysis for the alternative Minimetro

Fig. 1 schematically illustrates the developed hi- erarchy model for the choice of the best transport system, with the goal at the top, the four alternatives at the bottom, and the four criteria with their sub- critera in the middle. The one-way arrows indicate the influence between each element of the hierarchy.

Figure 1. Tree illustrating the hierarchy of criteria and sub- criteria identified in the process critera in the middle. The one-way arrows indicate

Table 5. SWOT analysis for the alternative Monorail

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Combining Multicriteria Analysis and GIS to help decision making processes in Portneuf County (Quebec, Canada)

by Carlo Previl

2003, Proceedings of 2 nd …

The planning of a linear section of a park of less than 15 km generated a very complex situation in Portneuf County (Quebec, Canada). Faced with dissatisfaction from various civic groups, the county's board tried to calm down the spirits... more

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Protected Area Zoning for Conservation and Use: A Combination of Spatial Multicriteria and Multiobjective Evaluation

by Davide Geneletti

2008, Landscape and urban planning

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Evidence Admissibility and Evaluation Models in Commercial Arbitration

by Abdulla Galadari

Contracts are designed to govern the relations between business partners and allocate risk among them, yet they cannot mitigate all risks; hence, dispute resolution mechanisms have been developed to assist. According to research,... more

Fig. 1. Proposed evidence admission framework.

Fig. 4. Percent sufficiency of expensive evidence. spent to achieve any marginal sufficiency of expensive evidence is 20 days (tmin). Moreover, the tribunal estimates that achieving 15% sufficiency of expensive evidence would take 40 days. The esti- mated percent sufficiency of expensive evidence is plotted against discovery time in Fig. 4.

Table 2. Schedule of consolidated admitted evidence

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Guía técnica para la restauración en El Salvador Renovación de cafetales

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La deforestación y la degradación de ecosistemas es un problema global e impacta su capacidad de proveer una gran diversidad de bienes y servicios. La restauración del paisaje forestal tiene la intención de asegurar que la funcionalidad... more

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Modelling risk for prioritisation of interventions for environmental hazards to human health using multicriteria decision analysis

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When we published the spring edition of the Chemical Hazards and Poisons Report, we asked you, our readers, to tell us what you think -and what you would like to see in future editions . Thank you to all of those who took the time to... more

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Multi-criteria GIS modeling for optimum route alignment planning in outer region of Allahabad City, India

by Mukund Pratap Singh

A route alignment planning has various stages. At different stages, multi-criteria decision analysis (MCDA) is required. This paper integrates geographical information system (GIS) and MCDA for designing the optimum route alignment. A... more

change in value from that cell to its neighbors. Basically, the maximum change in elevation over the distance between the cell and its eight neighbors identifies the steepest downhill percentage from the cell. Conceptually, the tool fits a plane to the z values of a 3 x 3 cell neighborhood around the pro- cessing or center cell.

The slope criterion in an Indian Road Congress (IRC) spec- ification should be acceptable in the range 0 to 10% for a flat area, 10 to 25% for a rolling area, 25 to 60% for a mountain- ous area, and more than 60% for the steepest area. In the

Fig. 4 Cost criteria maps of physical features

Supervised classification method using the maximum likeli- hood classifier was performed for land classification. The sat- ellite image is classified for acquiring the available informa- tion of land use/land cover. The classified map is given in Fig. 6. The various land use classes, delineated from the LISS-II image, such as agriculture land, water body, rivers, trees, ur- ban area, and sandbank, are used for analyzing the study area and for creating the least-cost route alignment.

Fig. 7 Cost criteria maps of land use and land cover The task of selecting a particular route alignment for a high- way as an alternative is complex and challenging. MCDA is applicable to solving complex and challenging problems that are characterized as a choice among alternatives (Malczewski, 1999a, b, 2004, 2006). The MCDA provides a rich collection of techniques and procedures for structuring decision prob- lems and designing, evaluating, and prioritizing alternative decisions (Carver 1991). A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the Evaluation of criteria map attributes can only be compared if the measurement units are the same. Because the evaluation of the criteria had their own characteristics and extent and each datum of criteria had its own dimension and distribution, it was difficult to directly compare them or include them togeth- er in an operational system. The process of setting the relative importance of each criterion is known as the standardization of criteria (Feizizadeh and Blaschke 2013). Standardization of the attributes was performed for each of the cost maps in ESRI Spatial Analyst reclassification module. In this process, scales of 0 to 1, 0 to 10, or 0 to 100 are normally used for criteria

Fig. 9 Relative surface cost maps of four different perspectives

Validation and discussion alignment which is near to sum of the weights is the optimum alignment. Alignment 4 is near to sum of the weights, so we can decide that alignment 4 is the optimum alignment. Table | explains these different computed criteria for each suggested alignment and the results.

Fig. 14 Field survey of some intersection locations

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A multicriteria decision analysis model for faculty evaluation

by Carlos Costa

2012

In the context of increasing demands for social and financial accountability of universities, the required implementation of transparent faculty evaluation systems constitutes a challenge and an opportunity for universities strategically... more

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Evaluation of the Parameters of the Green Supplier Selection Decision in Textile Industry

by İsmail Önden and

2016, Fibres & Textiles in Eastern Europe

Green issues are a popular topic in today's business life, and it is possible to see green products or advertisements in many different places due to the demand for a healthier lifestyle. Companies that are willing to be a part of the... more

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Towards Proactive and Flexible Agent-Based Generation of Policy Packages for Active Transportation

by Araz 𝖳aeihagh

2014, 47th Hawaii International Conference on System Sciences (HICSS) 2014

One of the approaches gaining ground in policy design is the implementation of combinations of policy measures as policy packages with the aim of increasing efficiency and effectiveness of the designed policies. In this paper, we describe... more

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Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS)

by Mehdi Keshavarz-Ghorabaee and

An effective way for managing and controlling a large number of inventory items or stock keeping units (SKUs) is the inventory classification. Traditional ABC analysis which based on only a single criterion is commonly used for... more

Fig. 1. PDA and NDA values in a simple situation. Step 5: Determine the weighted sum of PDA and NDA for all alternatives, shown as fol-

Fig. 2. Importance level of criteria in different sets.

Data of the inventory classification problem and average solutions.

The results of the proposed method. Table 2

The result of ABC classification with different methods.

The values of S, for comparison between ABC classification methods. Table 4 Table 5

Data of the MCDM problem for comparative analysis. As can be seen in Table 4, there is good similarity between the proposed method and the other methods. Moreover, all methods that have chosen for comparison use DEA-like mathematical modeling for classifying the items. Using this mathematical modeling tech- nique usually leads to a high computational process. However, we can say that the sim- plicity and lower computational process are the major advantages of the proposed method. As can be seen in Table 4, there is good similarity between the proposed method and

Normalized weights of criteria in different sets. Figure 2 represents the importance level of each criterion in each set, and Table 6 shows the normalized weights of criteria in these sets. In each set, one of the criteria has the highest weight. The ranking results that obtained by different methods and weights are shown in Table 7. We use also the Spearman’s correlation coefficient (7; ) for analyzing the correlation between the results of the proposed method and the other methods in different criteria weights. The results of this analysis are shown in Table 8.

The results of ranking with EDAS method and four MCDM methods.

Values of Spearman’s correlation coefficient (rs) in different sets.

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Biodiversity Conservation Planning Tools: Present Status and Challenges for the Future

by Paul Williams

2006, Annual Review of Environment and Resources

■ Abstract Species extinctions and the deterioration of other biodiversity features worldwide have led to the adoption of systematic conservation planning in many regions of the world. As a consequence, various software tools for... more

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Measuring customer satisfaction using a collective preference disaggregation model

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A new model to assess customer satisfaction is developed through this paper. The proposed model is based on the principles of multicriteria analysis, using ordinal regression techniques. The procedure uses survey's data on customer... more

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Integrated assessment of a new Waste-to-Energy facility in Central Greece in the context of regional perspectives

by George Perkoulidis

2010, Waste Management

The main aim of this study is the integrated assessment of a proposed Waste-to-Energy facility that could contribute in the Municipal Solid Waste Management system of the Region of Central Greece. In the context of this paper alternative... more

The main aim of this study is the integrated assessment of a proposed Waste-to-Energy facility that could contribute in the Municipal Solid Waste Management system of the Region of Central Greece. In the context of this paper alternative transfer schemes for supplying the candidate facility were assessed considering local conditions and economical criteria. A mixed-integer linear programming model was applied for the determination of optimum locations of Transfer Stations for an efficient supplying chain between the waste producers and the Waste-to-Energy facility. Moreover different Regional Waste Management Scenarios were assessed against multiple criteria, via the Multi Criteria Decision Making method ELECTRE III. The chosen criteria were total cost, Biodegradable Municipal Waste diversion from landfill, energy recovery and Greenhouse Gas emissions and the analysis demonstrated that a Waste Management Scenario based on a Waste-to-Energy plant with an adjacent landfill for disposal of the residues would be the best performing option for the Region, depending however on the priorities of the decision makers. In addition the study demonstrated that efficient planning is necessary and the case of three sanitary landfills operating in parallel with the WtE plant in the study area should be avoided. Moreover alternative cases of energy recovery of the candidate Waste-to-Energy facility were evaluated against the requirements of the new European Commission Directive on waste in order for the facility to be recognized as recovery operation. The latter issue is of high significance and the decision makers in European Union countries should take it into account from now on, in order to plan and implement facilities that recover energy efficiently. Finally a sensitivity check was performed in order to evaluate the effects of increased recycling rate, on the calorific value of treated Municipal Solid Waste and the gate fee of the candidate plant and found that increased recycling efforts would not diminish the potential for incineration with energy recovery from waste and neither would have adverse impacts on the gate fee of the Waste-to-Energy plant. In general, the study highlighted the need for efficient planning in solid waste management, by taking into account multiple criteria and parameters and utilizing relevant tools and methodologies into this context.

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Multicriteria Decision Analysis

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