Real Time Decision Making

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments -which may vary significantly with location -make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with regard to issues such as establishing water saving policies while providing adequate irrigation and choosing the right time to harvest the fruit based on its maturity. This article proposes a methodology consisting of a set of well-defined phases that cover the complete life cycle of WSN applications for agricultural monitoring. We have studied different existing real-world scenarios where WSNs are being applied. Based on this study we have discovered that there exist significant commonalities but no methodology that specifies the best practices that should be used in the general, crop-independent case. The lack of a general methodology negatively impacts the amount of effort, development time, and cost of developing applications.

In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments -which may vary significantly with location -make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with regard to issues such as establishing water saving policies while providing adequate irrigation and choosing the right time to harvest the fruit based on its maturity. This article proposes a methodology consisting of a set of well-defined phases that cover the complete life cycle of WSN applications for agricultural monitoring. We have studied different existing real-world scenarios where WSNs are being applied. Based on this study we have discovered that there exist significant commonalities but no methodology that specifies the best practices that should be used in the general, crop-independent case. The lack of a general methodology negatively impacts the amount of effort, development time, and cost of developing applications.

Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.

The explosive growth of scientific visualization in the past 10 years demonstrate a consistent and tacit agreement among scientists that visualization offers a better representation system for displaying complex data than traditional charting methods.
However, most visualization works have not been unable to exploit the full potential of visualization techniques. The reason may be that these attempts have been largely executed by scientists. While they have the technical skills for conducting research, they do not have the design background that would allow them to display data in easy to understand formats.
This paper presents the architectural methodology, theory, technology and products that are being employed in an ongoing multidisciplinary research in anesthesiology. The project’s main goal is to develop a new data representation technology to visualize physiologic information in real time. Using physiologic data, 3-D objects are generated in digital space that represent physiologic changes within the body and show functional relationships that aid in the detection, diagnosis, and treatment of critical events.
Preliminary testing results show statistically significant reduction in detection times. The research outcome, potential, and recently received NIH grant supporting the team’s scientific methods all point to the contributions that architecture may offer to the growing field of data visualization.

This exploratory study proposes a new approach that utilized pre-built issues libraries in healthcare supply chain. Supply chain related issues are collected and deduced from the literature to build issues libraries. This is followed by application of group decision-making for their prioritization and defining solution requirements from doctors' perspectives. A new approach of shared decision-making is proposed by utilizing literature for developing pre-built issues libraries as an input to shared decision-making. Quick identification and resolution mean that an organisation is continually learning and moving towards excellence. It can be used as a checklist for comparison within and across organisations. Usage of open source applications such as Google Sheets and WhatsApp was utilized for geographically dispersed experts.

This paper presents an approach to the creation of an agent-based system for the assessment of environmental impact upon human health. As indicators of the environmental impact water pollution, indexes of traffic and industrial activity, wastes and solar radiation are assumed. And as human health indicator morbidity is taken. All the data comprise multiple heterogeneous data repositories. The system is logically and functionally divided into three layers, solving the tasks of information fusion, pattern discovery and decision support making, respectively. The outcomes of the system design phase under Prometheus methodology and the complete characteristics of the agents forming the proposal are discussed. The discovered patterns are used as a foundation for real-time decision making, which is of great importance for adequate and effective management by responsible governmental authorities.

Since the start of civilization it has always been considered a magic to transforming an ideal model into a real useful physical object one can hold in one's hand. The object can be a critical component of an industrial machine, a consumer good on a store shelf or even an early physical prototype that unveils one's new idea to the world. In this project proposal, we aim to design and develop a prototype of a portable 3D printer.

Several use of simulation tool in intermodal container terminal are presented. Initially, an overview of the operations within Casablanca container terminal is described. Then it is shown the simulation model that we have proposed and its calibration and validation. Finally, we discuss the design of a decision support system to assist the management in real time decision making.

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments -which may vary significantly with location -make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with regard to issues such as establishing water saving policies while providing adequate irrigation and choosing the right time to harvest the fruit based on its maturity. This article proposes a methodology consisting of a set of well-defined phases that cover the complete life cycle of WSN applications for agricultural monitoring. We have studied different existing real-world scenarios where WSNs are being applied. Based on this study we have discovered that there exist significant commonalities but no methodology that specifies the best practices that should be used in the general, crop-independent case. The lack of a general methodology negatively impacts the amount of effort, development time, and cost of developing applications.

Mobile communication technologies enable truck drivers to keep abreast of changing traffic conditions in real-time. We assume that such communication capability exists for a single vehicle traveling from a known origin to a known destination where certain arcs en route are congested, perhaps as the result of an accident. Further, we know the likelihood, as a function of congestion duration, that congested arcs will become uncongested and thus less costly to traverse. Using a Markov decision process , we then model and analyze the problem of constructing a minimum expected total cost route from an origin to a destination that anticipates and then responds to changes in congestion, if they occur, while the vehicle is en route. We provide structural results and illustrate the behavior of an optimal policy with several numerical examples and demonstrate the superiority of an optimal anticipatory policy, relative to a route design approach that reflects the reactive nature of current routing procedures.

A method for forcing norms onto individual agents in a multi-agent system is presented. The agents under study are supersoft agents: autonomous artificial agents programmed to represent and evaluate vague and imprecise information. Agents are further assumed to act in accordance with advice obtained from a normative decision module, with which they can communicate. Norms act as global constraints on the evaluations performed in the decision module and hence no action that violates a norm will be suggested to any agent. Further constraints on action may then be added locally. The method strives to characterise real-time decision making in agents, in the presence of risk and uncertainty.

With the advancement of hand-held devices, wireless and sensor network pervasive computing has achieved a perfect momentum. Formerly, a requirement existed that was a serious impediment and threat to the mobility of users – the necessary presence of a fixed wired network. This has been resolved by the recent advances in wireless and mobile technologies, particularly Bluetooth and WiFi. The advancement of available, portable, low cost mobile devices (PDAs, cell phones, etc.) has resulted in the user's mobility at unprecedented levels. As these devices can communicate with one another, the combined capabilities can be leveraged to form a useful new set of tools. Presently, pervasive computing is being extended into the sophisticated healthcare sector with the promise of providing an easier and more efficient mode of communication between physicians and patients or between the physicians themselves. In this paper we provide the details of our application 'Healthcare Aide', which has been designed to provide not only more convenience for doctor – doctor, resident doctor – doctor, patient – doctor and nurse – doctor interaction but also a smooth pathway for real-time decision making. Our pervasive middleware MARKS (Middleware Adaptability for Resource Discovery, Knowledge Usability and Self-healing) provides the underlying support for this application in a completely transparent manner. In this paper, we have also presented our survey results from users' point of view along with performance analysis. Die technologischen Fortschritte bei Pocketcomputern, drahtlosen Netzwerken (insbesondere Bluetooth und WiFi) und Sensoren ermöglichen die weitgehende Unterstützung der Mobilität von Benutzern – dort wo früher Einschränkungen aufgrund fest verlegter Netzwerkkabel bestanden. Da Pocketcomputer immer leistungsfähiger und preiswerter werden und auch miteinander problemlos kommunizieren können, verspricht die Entwicklung entsprechender Applikationen Unterstützung im Bereich des Gesundheitswesens. In diesem Beitrag präsentieren die Autoren Details zur Anwendung "Healthcare Aide", die zur Interaktion und Echtzeit-Entscheidungsfindung bei Arzt – Arzt, Patient – Arzt, Schwester – Arzt verwendet werden kann. Die Middleware MARKS stellt die technische Plattform für diese Anwendung dar. In diesem Beitrag werden darüber hinaus Studienergebnisse aus der Sicht der Benutzer zusammen mit einer Performanzanalyse präsentiert.

In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments -which may vary significantly with location -make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with regard to issues such as establishing water saving policies while providing adequate irrigation and choosing the right time to harvest the fruit based on its maturity. This article proposes a methodology consisting of a set of well-defined phases that cover the complete life cycle of WSN applications for agricultural monitoring. We have studied different existing real-world scenarios where WSNs are being applied. Based on this study we have discovered that there exist significant commonalities but no methodology that specifies the best practices that should be used in the general, crop-independent case. The lack of a general methodology negatively impacts the amount of effort, development time, and cost of developing applications.

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 ‘trains’ comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses.

SUMMARY Digital maps and datasets provide the positioning of variety types of geo-spatial entities. These are given in different reference coordinate systems based on different geodetic projections and datums. Simultaneous use of this varied data requires geo-oriented systems to apply real-time data manipulation, and mechanisms enabling the support of immediate and precise processes (for example RTK GPS systems calculating an

We summarise our experiences of a number of demonstrators and simulation experiments designed to test the feasibility of using artificial decision making agents in real-time domains, and comment on the significance of our results to autonomous artificial agent action patterns in markets. Our main hypothesis is that the use of norms can extend the capability of artificial decision makers beyond what is obtained from implementing individual utility maximizers in keeping with rational choice theory.

The paper tries to emphasize the usual meanings of this collocation introduced together with the title. So it starts from the globalization phenomenon and continues with some usages and trends for business intelligence applications within this context. The focus is on the further developments in this field, on their challenges and limits. The approach is a critical one and involves

Many practical engineering problems involve the determination of optimal control trajectories for given multiple and conflicting objectives. These conflicting objectives typically give rise to a set of Pareto optimal solutions. To enhance real-time decision making efficient approaches are required for determining the Pareto set in a fast and accurate way. Hereto, the current paper integrates efficient multiple objective scalarisation strategies (e.g., Normal Boundary Intersection and Normalised Normal Constraint) with fast deterministic approaches for dynamic optimisation (e.g., Single and Multiple Shooting). All techniques have been implemented as an easy-to-use add-on module of the automatic control and dynamic optimisation toolkit ACADO (both freely available at www.acadotoolkit.org). Several algorithmic synergies (e.g., hot-start initialisation strategies) are exploited for an additional speed-up. The features of ACADO Multi-Objective are discussed and its use is F. Logist · J. Van Impe (B) illustrated on different multiple objective optimal control problems arising in several engineering disciplines.

This paper elaborates on the use of future wireless communication networks for autonomous city vehicles. After addressing the state of technology, the paper explains the autonomous vehicle control system architecture and the Cybercars-2 communication framework; it presents experimental tests of communication-based real-time decision making; and discusses potential applications for communication in order to improve the localization and perception abilities of autonomous vehicles in urban environments.

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments – which may vary significantly with location – make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with

The classification of power systems operating states plays an important role in power systems control and operation. Determining the state of a power system is crucial and requirements for real-time decision making in power systems security assessment demand low dimensionality and low computational time. This paper investigates the performances of feature extraction based on mutual information in power system state classification with machine learning. The AdaBoost algorithm is used for classification based on large training databases and feature extraction is applied in order to reduce their dimensionality.

In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

This paper deals with decision making in a real time optimization context under uncertain data by linking Bayesian networks (BN) techniques (for uncertainties modeling) and linear programming (LP, for optimization scheme) into a single framework. It is supposed that some external events sensed in real time are susceptible to give relevant information about data. BN consists in graphical representation of probabilistic relationship between variables of a knowledge system and so permit to take into account uncertainty in an expert system by bringing together the classical arti®cial intelligence (AI) approach and statistics approach. They will be used to estimate numerical values of parameters subjected to the in¯uence of random events for a linear programming program that perform optimization process in order to select optimal values of decision variables of a certain real time decision-making system.

Analyzing clustering structures in data streams can provide critical information for real-time decision making. Most research in this area has focused on clustering algorithms for numerical data streams, and very few have proposed to monitor the change of clustering structure. Most surprisingly, to our knowledge, no work has been proposed on monitoring clustering structure for categorical data streams. In this paper, we present a framework for detecting the change of primary clustering structure in categorical data streams, which is indicated by the change of the best number of clusters (Best K) in the data stream. The framework uses a Hierarchical Entropy Tree structure (HE-Tree) to capture the entropy characteristics of clusters in a data stream, and detects the change of Best K by combining our previously developed BKPlot method. The HE-Tree can efficiently summarize the entropy property of a categorical data stream and allow us to draw precise clustering information from the data stream for generating high-quality BKPlots. We also develop the time-decaying HE-Tree structure to make the monitoring more sensitive to recent changes of clustering structure. The experimental result shows that with the combination of the HE-Tree and the BKPlot method we are able to promptly and precisely detect the change of clustering structure in categorical data streams.

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum marginbased support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses.

For public transport authorities, the most important aspects for operations are passenger satisfaction and safety. Based on surveys, passengers regard punctuality as the most important aspect, which makes it an important factor of passenger satisfaction for the operators [10].

Decision-making is central to human cognition. Fundamental to every decision is the ability to internally represent the available choices and their relative costs and benefits. The most basic and frequent decisions we make occur as our motor system chooses and executes only those actions that achieve our current goals. Although these interactions with the environment may appear effortless, this belies what must be incredibly sophisticated visuomotor decision-making processes. In order to measure how visuomotor decisions unfold in real-time, we used a unique reaching paradigm that forced participants to initiate rapid hand movements toward multiple potential targets, with only one being cued after reach onset. We show across three experiments that, in cases of target uncertainty, trajectories are spatially sensitive to the probabilistic distribution of targets within the display. Specifically, when presented with two or three target displays, subjects initiate their reaches toward an intermediary or 'averaged' location before correcting their trajectory in-flight to the cued target location. A control experiment suggests that our effect depends on the targets acting as potential reach locations and not as distractors. This study is the first to show that the 'averaging' of target-directed reaching movements depends not only on the spatial position of the targets in the display but also the probability of acting at each target location.

This paper considers the construction of Petri nets to simulate the computation performed by real-time decision systems. A description of Petri nets which model decision-making in real-time decision systems is briefly described in Section 1. A rough fuzzy Petri net extension of rough Petri nets are presented in Section 2. These nets are used to create highly parallel programs to

Monitoring of negative effects of urban pollution and realtime decision making allow to clarify consequences upon human health. Large amounts of raw data information describe this situation, and to get knowledge from it, we apply intelligent agents. Further modelling and simulation gives the new knowledge about the tendencies of situation development and about its structure. Agent-based decision support system can help to foresee possible ways of situation development and contribute to effective management and planning.

Monitoring of negative effects of urban pollution and real-time decision making allow to clarify consequences upon human health. Large amounts of raw data information describe this situation, and to get knowledge from it, we apply intelligent agents. Further modeling and simulation gives the new knowledge about the tendencies of situation development and about its structure. Agent-based decision support system can help to foresee possible ways of situation development and contribute to effective management and planning.

This paper presents an approach to the creation of an agent-based system for the assessment of environmental impact upon human health. As indicators of the environmental impact water pollution, indexes of traffic and industrial activity, wastes and solar radiation are assumed. And as human health indicator morbidity is taken. All the data comprise multiple heterogeneous data repositories. The system is logically and functionally divided into three layers, solving the tasks of information fusion, pattern discovery and decision support making, respectively. The outcomes of the system design phase under Prometheus methodology and the complete characteristics of the agents forming the proposal are discussed. The discovered patterns are used as a foundation for real-time decision making, which is of great importance for adequate and effective management by responsible governmental authorities.

We consider a class of scheduling problems of n weighted tasks on M identical, and parallel processors with an objective of minimizing the sum of the tasks weighted flow-times. A priority rule for total weighted flow-time (PRTWF), is then proposed for locally optimal scheduling of tasks with unequal release dates and processing times. Then, an algorithm based on a heuristic and the PRTWF, is worked out to minimize the total weighted flow-time of the given set of tasks on a single server. This algorithm is designed for implementation in a dynamic process of real-time decision-making. It is next extended to tasks scheduling (with unequal release dates and processing times) on parallel servers, while minimizing their total weighted flow-time. A lower bound of solutions is also proposed to evaluate the algorithm, with a complexity in O(n 3 ) in the off-line scheduling process. The rule is then used in an algorithm of on-line planning and scheduling of maintenance tasks in a large size distributed system with weighted Equipments. q

This paper presents an approach to the creation of an agent-based system for the assessment of environmental impact upon human health. As indicators of the environmental impact water pollution, indexes of traffic and industrial activity, wastes and solar radiation are assumed. And as human health indicator morbidity is taken. All the data comprise multiple heterogeneous data repositories. The system is logically and functionally divided into three layers, solving the tasks of information fusion, pattern discovery and decision support making, respectively. The outcomes of the system design phase under Prometheus methodology and the complete characteristics of the agents forming the proposal are discussed. The discovered patterns are used as a foundation for real-time decision making, which is of great importance for adequate and effective management by responsible governmental authorities.

The Integrated Situational Awareness System (ISAS) initiative at the University of Florida Digital Worlds Institute has demonstrated an effective web services-enhanced graphicallybased environment for globally-distributed operations ranging from humanitarian aid during large-scale environmental disasters to high-level collaboration and augmented decision-making in civil and coalition activities.

Advanced Traffic Management Systems constitute one of the major envisaged applications of Intelligent Transport Systems. Various architectures and approaches have been proposed and tested in projects of R&D programmes of the European Commission in recent years, as for example ARTIS, KITS and ENTERPRICE, just to mention a few. An approach shared by all of them has been the conception of

This paper presents a fuzzy system for controlling the fire command in a surface-to-surface engagement by assuming siege of friendly side by enemy forces. The system must be capable of interacting with a dynamic and uncertain battle world in a real-time manner. In siege situation, it is assumed that the enemy troops are advancing toward friendly side in different directions and the defence resources are limited and nonrenewable at a single platform. Thus, resource allocation problem in a real-time manner is an important and vital component of battle management until coming of auxiliary forces. This paper addresses the design of a fuzzy system as an efficient tool for real-time decision-making in order to optimize the defense resource allocation in engagement.

The wet and dry seasons are distinctive in Taiwan as the amount of precipitation in wet seasons accounts for over three-fourth of the total rainfall. And the waterresources management relies pretty much on the rainfall brought in by typhoons as it accounts for a significant portion of the precipitation during wet seasons. Furthermore, as the storage of reservoirs is limited due to topographical factors, the management of typhoon rainfall has always been an important issue in Taiwan. The technique of decisiontree analysis is applied in this article to determine the optimal reservoir release in advance upon the issuance of a typhoon warning by the Central Weather Bureau (CWB), and the proposed methodology may provide solution to the trade-off judgment of reservoir operations between flood control and water supply according to economic efficiency. In this article, the economic loss functions of flooding damage and water-supply shortage are assumed in linear and nonlinear conditions, and the respective expected optimal releases based on the predicted precipitation as issued by CWB are derived. The proposed methodology has been applied to the Shihmen Reservoir System, and the capabilities of the model as an aid to real-time decision-making as well as the evaluation of the economic worth of forecasts is presented.

Mobile users making real-time decisions based on current information need confidence about their context been taken into consideration in producing recommendations. This chapter reviews current use of mobile technologies for decision support. Specifically, it describes a framework for assessing the impact of mobility in decision-making. The framework uses dynamic context representation of data quality to represent uncertainties in the mobile

We develop a model for determining the economically optimal amount of control eort required to manage a homogeneous project (one consisting of a large number of similar activities). The model is formulated in terms of the losses generated by deviations from the plan and of the costs associated with carrying out control activities. It accounts for changing levels of project activities and includes parameters that represent control eectiveness and project management eectiveness. The model is studied by applying optimal control theory, which yields the optimal control eort described by a number of control functions and switching points at which they change over. We identify three analytically solvable cases for the most commonly used forms of control cost and deviation loss functions. Our analysis of the model leads to several speci®c conclusions regarding the extent and timing of managerial attention that should be devoted to keep projects on track. We also point out how the optimal o-line policy can be adapted for on-line control and real-time decision making throughout the project life cycle.

Purpose: To demonstrate the feasibility of an intraoperative inverse planning technique with advanced optimization for prostate seed implantation. Methods and Materials: We have implemented a method for optimized inverse planning of prostate seed implantation in the operating room (OR), based on the genetic algorithm (GA) driven Prostate Implant Planning Engine for Radiotherapy (PIPER). An integrated treatment planning system was deployed, which includes real-time ultrasound image acquisition, treatment volume segmentation, GA optimization, real-time decision making and sensitivity analysis, isodose and DVH evaluation, and virtual reality navigation and surgical guidance. Ten consecutive patients previously scheduled for implantation were included in the series. Results: The feasibility of the technique was established by careful monitoring of each step in the OR and comparison with conventional preplanned implants. The median elapsed time for complete image capture, segmentation, GA optimization, and plan evaluation was 4, 10, 2.2, and 2 min, respectively. The dosimetric quality of the OR-based plan was shown to be equivalent to the corresponding preplan. Conclusion: An intraoperative optimized inverse planning technique was developed for prostate brachytherapy. The feasibility of the method was demonstrated through an early clinical experience. © 1999 Elsevier Science Inc.

We study a make-to-order manufacturing system consisting of several processing centers that are subject to failures and repairs. Our objective is to build a model that can be used as a tool for negotiating the delivery date and the price of a certain upcoming order. The model takes into account the congestion level of the shop floor at the time the order is placed. Based on the workload of the processing centers, the model splits the order into lots and assigns them to the processing centers so as to determine the order completion time associated with the minimum operating cost. The efficiency of the solution method for the model allows real-time decision-making while negotiating the price and delivery date of the order to be placed. Since the decisions are made based on a snapshot of the congestion level at the shop floor, using this model will reduce the conflict between the marketing and the production activities in manufacturing organizations. ____________________________________________________________________________________________

For public transport authorities, the most important aspects for operations are passenger satisfaction and safety. Based on surveys, passengers regard punctuality as the most important aspect, which makes it an important factor of passenger satisfaction for the operators [10].

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 ‘trains’ comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses.

This paper describes an approach to real-time decisionmaking for quality of service based scheduling of distributed asynchronous data replication. The proposed approach addresses uncertainty and variability in the quantity of data to replicate over low bandwidth fixed communication links. A dynamic stochastic knapsack is used to model the acceptance policy with dynamic programming optimization employed to perform offline optimization. The obtained optimal values of the input variables are used to build and train a multi-layer neural network. The obtained neural network weights and configuration can be used to perform near optimal accept/reject decisions in real-time. Off-line processing is used to establish the initial acceptance policy and to verify that the system continues to perform near-optimally. The proposed approach is implemented via simulation enabling the evaluation of a variety of scenarios and refinement of the scheduling portion of the model. The preliminary results are very promising.

Mobile users making real-time decisions based on current information need confidence that their context has been taken into consideration in producing the system’s recommendations. This chapter reviews current use of mobile technologies for context-aware real-time decision support. Specifically, it describes a framework for assessing the impact of mobility in decision making. The framework uses dynamic context model of data quality to represent uncertainties in the mobile decision-making environment. This framework can be used for developing visual interactive displays for communicating to the user relevant changes in data quality when working in mobile environments. As an illustration, this chapter proposes a real-time decision support procedure for on-the-spot assistance to the mobile consumer when choosing the best payment option to efficiently manage their budget. The proposed procedure is based on multi-attribute decision analysis, scenario reasoning, and a quality of data framework. The feasibility of the approach is demonstrated with a mobile decision-support system prototype implementation.