Academia.eduAcademia.edu

Figure 5 – uploaded by sylvain Kubler Kubler

See full PDFdownloadDownload figure
References on the topic of*Decision-making’.

Table 5 References on the topic of*Decision-making’.

Related Figures (16)

MAUT — Multi-Attribute Utility Theory ELECTRE, TOPSIS, PROMETHEE ~— see Table
MAUT — Multi-Attribute Utility Theory ELECTRE, TOPSIS, PROMETHEE ~— see Table
Fig. 3. Traditional membership functions used in FAHP.
Fig. 3. Traditional membership functions used in FAHP.
Table 2 (Continued)
Table 2 (Continued)
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*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.
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.
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.
References on the topic of‘ Development’.
References on the topic of‘ Development’.
References on the topic of*Priority’.
References on the topic of*Priority’.
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:
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” .
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” .
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
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
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)).
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.
Fig. 10. Online Testbed making available results and findings of the state-of-the-art survey, as well as FPCMs from the reviewed papers.
Related topics:
Artificial IntelligenceDecision MakingPolicy Analysis and Decision MakingJudgment and decision makingJudgment and Decision Making (Judgment And Decision Making)Fuzzy set theoryDecision Making Under UncertaintyMulti Criteria Decision MakingFuzzy AHPArtifical Intelligence
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved