A case-based fuzzy multicriteria decision support model for tropical cyclone forecasting

This paper proposes a multi-stage framework for intelligent decision support. The proposed framework integrates case-based reasoning and fuzzy multicriteria decision making techniques. It potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. Additionally, the framework provides intelligent assistance in articulating domain expert's preferences through outranking relations. We illustrated the proposed approach in the context of tropical cyclone prediction. Ten years of historical observation data about tropical cyclones was represented within fuzzy multicriteria decision-making problem. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.     

A decision model for interdependent information system project selection

Existing methods for information system (IS) project selection neglect an important aspect of information technology, namely the interdependencies that exist among various IS applications (projects). Recognizing and modeling these project interdependencies provides valuable cost savings and greater benefits to organizations. In this paper, an IS project selection model is developed that identifies and models benefit, resource and technical interdependencies among candidate projects. The proposed model is formulated as a nonlinear 0–1 programming problem and represents a significant addition to existing IS, capital budgeting and R&D project selection models. The model is converted, using linearization techniques, and tested (validated) by applying it to real-world IS project selection data. By comparing the performance of this model with existing project selection models, the contribution of this model is highlighted.     

A multicriteria facility location model for municipal solid waste management in North Greece

Up to 2002, Hellenic Solid Waste Management (SWM) policy specified that each of the country’s 54 prefectural governments plan its own SWM system. After 2002, this authority was shifted to the country’s 13 regions entirely. In this paper, we compare and contrast regional and prefectural SWM planning in Central Macedonia. To design the prefectural plan, we assume that each prefecture must be self-sufficient, and we locate waste facilities in each prefecture. In contrast, in the regional plan, we assume cooperation between prefectures and locate waste facilities to serve the entire region. We present a new multicriteria mixed-integer linear programming model to solve the location–allocation problem for municipal SWM at the regional level. We apply the lexicographic minimax approach to obtain a “fair” nondominated solution, a solution with all normalized objectives as equal to one another as possible. A solution to the model consists of locations and technologies for transfer stations, material recovery facilities, incinerators and sanitary landfills, as well as the waste flow between these locations.     

A fuzzy outranking relation in multicriteria decision making

It frequently happens that a decision maker must establish a ranking within a finite set of alternatives with respect to multiple criteria. The subjective evaluation of each alternative according to each criterion is expressed in the form of a distributive evaluation. To capture the preferences of one alternative over another, a concept of fuzzy outranking relation can be used. This fuzzy outranking relation is characterized by a degree of credibility which is computed from two indices: a confidence index and a doubt index. Each of these indices is calculated from the distributive evaluations over the various criteria. In this paper, such a fuzzy outranking relation (fuzzy binary relation) is constructed and an application is presented.     

A priority assessment multi-criteria decision model for human spaceflight mission planning at NASA

Analog missions are real-life, Earth-based science missions whose purpose is to help understand the operations, techniques, and technologies required to perform similar tasks during future human spaceflight missions. The goal of performing an analog mission is to prepare crewmembers and support teams for future space missions in a low risk, low-cost environment. Vehicle, habitat, and surface terrain simulators are used to test hardware, operations, and tasks repeatedly for analog missions. This study presents a multi-criteria decision making model that was developed for the Integrated Human Exploration Mission Simulation Facility project at Johnson Space Center to assess the priority of a set of human spaceflight mission simulators. The proposed framework integrates subjective judgments derived from the analytic hierarchy process with entropy data into a preference model to prioritize five mission simulators for the human exploration of Mars.     

A multicriteria decision support tool for modelling bank credit ratings

We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

     

Rough set-based logics for multicriteria decision analysis

In this paper, we propose some decision logic languages for rule representation in rough set-based multicriteria analysis. The semantic models of these logics are data tables, each of which is comprised of a finite set of objects described by a finite set of criteria/attributes. The domains of the criteria may have ordinal properties expressing preference scales, while the domains of the attributes may not. The validity, support, and confidence of a rule are defined via its satisfaction in the data table.     

Model choice in multicriteria decision aid

The purpose of this paper is to illustrate, using a real case study, one of the points stressed in the “Manifesto of the new Multi Criteria Decision Aid (MCDA) era” (Bouyssou et al., 1993) regarding the application of the basic theory of MCDA procedures. Although the great diversity of MCDA procedures may be seen as a strong point, it can be a weakness, and a systematic analysis of decision procedures if one method makes more sense than another for a specific problem is necessary. The problem of selecting the most appropriate (MCDA) technique for a particular application is in itself a MCDA problem since the decision making criteria used for the selection are different and conflicting in nature. In this paper three selection models are implemented to assist the system analyst, when confronted with a multi-objective decision problem, to select the most appropriate MCDA technique for application to the problem of optimal ranking of water development projects in an arid country. These models are developed by Deason (1984), Gershon (1981), and Tecle (1988). Results indicated that PROMETHEE was the most preferred method for this problem.     

Rough sets theory for multicriteria decision analysis

The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identified with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.     

A method for solving fuzzy multicriteria decision problems with dependent criteria

We propose a new multi-criteria decision making (MCDM) method based on fuzzy pair-wise comparisons and a feedback between the criteria. The evaluation of the weights of criteria, the variants as well as the feedback between the criteria is based on the data given in pair-wise comparison matrices. Extended arithmetic operations with fuzzy numbers are used as well as ordering fuzzy relations to compare fuzzy outcomes. An illustrating numerical example is presented to clarify the methodology. A special SW-Microsoft Excel add-in named FVK was developed for applying the proposed method. Comparing to other software products, FVK is free, able to work with fuzzy data and utilizes capabilities of widespread spreadsheet Microsoft Excel.     

Urban stormwater drainage management: The development of a multicriteria decision aid approach for best management practices

Multicriteria analysis methods have been used over the past decade for resolving environmental issues. This paper deals with the application of a multicriteria analysis (MCA) approach to urban stormwater drainage management. Stormwater source control has become a popular alternative solution for managing stormwater in urban areas. Source control constitutes one variant of best management practices (BMPs) that can be evaluated with respect to various criteria, including: hydraulic efficiency, pollution retention, environmental impact, operation and maintenance, economic investment, and social and sustainable urban living. A French survey was undertaken to assess the performance of different BMPs at the national scale; results highlight the main reasons justifying the use of BMPs. These reasons are primarily related to flood prevention, which far outweighs the economic incentives. Moreover, hydraulic and technical aspects are most frequently noted by users, whereas operation and maintenance aspects are often seen as obstacles to application of these techniques. The survey results, completed by a literature review and expert statements, have been used to establish a matrix of alternatives for multicriteria analysis. The MCA results obtained allow ranking the various alternatives from best to worst, taking into account the different strategies adopted by the decision-makers involved. The development of a multicriteria approach could, in the future, serve as a supporting decision-aid tool, whose purpose would be to guide users in their choice of stormwater source solution.     

ARMMS: Analogical reasoning model management system for multicriteria vehicle scheduling

The transportation industry problem of scheduling vehicles combines the spatial characteristics of routing with time domain considerations of activity schedules. The problem is complex because of the numerous interacting constraints in the spatial and time domains. Further, some of the constraints are flexible and some arise in real-time. The scheduling problem is often presented with multiple objectives that are not all economic in nature and which can be contradictory to one another. In response to these needs, this paper describes an analogical reasoning model management system, called ARMMS, designed in the domain of vehicle scheduling. ARMMS consists of knowledge bases and data bases, a truth maintenance system, a user interface, an inference engine, a learning mechanism, and a model library. Given a scheduling problem, ARMMS searches its memory for solutions. If no solution is available, ARMMS falls back on an analogical problem solving approach in which similar experience can be recalled, and solutions to new, but similar, problems can be constructed. If no similar experience exists, ARMMS intelligently selects an appropriate algorithmic model from its model library, based on the input parameters and problem type, to solve the given problem. By combining experts' knowledge, analogical problem-solving approaches, and algorithmic methods, ARMMS provides an efficient problem-solving approach for vehicle scheduling and routing. ARMMS is also a feasible base for the development of intelligent model management systems.     

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

Aggregation of fuzzy preference relations to multicriteria decision making

Weighted aggregation of fuzzy preference relations on the set of alternatives by several criteria in decision-making problems is considered. Pairwise comparisons with respect to importance of the criteria are given in fuzzy preference relation as well. The aggregation procedure uses the composition between each two relations of the alternatives. The membership function of the newly constructed fuzzy preference relation includes t-norms and t-conorms to take into account the relation between the criteria importance. Properties of the composition and new relation, giving a possibility to make a consistent choice or to rank the alternatives, are proved. An illustrative numerical study and comparative examples are presented.     

How to treat strict preference information in multicriteria decision analysis

This paper addresses the use of incomplete information on both multi-criteria alternative values and importance weights in evaluating decision alternatives. Incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds. En route to prioritizing alternatives, the majority of previous studies have replaced these strict inequalities with weak inequalities, by employing a small positive number. As this replacement closes the feasible region of decision parameters, it circumvents certain troubling questions that arise when utilizing a mathematical programming approach to evaluate alternatives. However, there are no hard and fast rules for selecting the factual small value and, even if the choice is possible, the resultant prioritizations depend profoundly on that choice. The method developed herein addresses and overcomes this drawback, and allows for dominance and potential optimality among alternatives, without selecting any small value for the strict preference information. Given strict information on criterion weights alone, we form a linear program and solve it via a two-stage method. When both alternative values and weights are provided in the form of strict inequalities, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve this linear program via the same two-stage method. One application of this methodology to a market entry decision, a salient subject in the area of international marketing, is demonstrated in detail herein.     

Spatial fuzzy consumer's decision making: A multicriteria analysis

This paper is devoted to a multicriteria analysis of the consumer's behavior when the decision maker is acting in a fuzzy space and manifesting an imprecise attitude. At first, the process of decision making is described with the help of three relationships between the set of goods which are supplied in several locations, the set of their characteristics and the set of the consumer's a priori possible behaviors. All these relations are fuzzy. The model applies the theory of fuzzy relations equations. Then, the stages of the decision process are analyzed. Often fuzzy behavior relations are like ‘black boxes’. The mathematical solution of the model indicates in which conditions their valuations are possible. The main interest of this method is not to use additive operations on subjective items and to use operators which are coherent with the fuzzy nature of the variables.     

Analysis of multicriteria decision aid in Europe

As asked by the editors of this special issue, the purpose of this paper is to analyze the existing literature on multicriteria decision-aid, to bring out the successes and the difficulties of this field and to propose some ways of research. Furthermore, as mentioned in the title, this analysis is essentially devoted to the research in Europe, even if some comments are concerned with the American literature. This paper is not a survey of all what has been done in multicriteria decision-aid, but we have tried to approach its main aspects.     

A graph colouring model for assigning a heterogeneous workforce to a given schedule

We analyze a heterogeneous workforce assignment problem in which the minimum number of workers required to carry out a machine load plan is calculated. The problem is formulated as a restricted vertex colouring problem and a branch and bound algorithm is presented. The special characteristics of the graph to be coloured allow an efficient implementation of the branch and bound. Computational results show that the algorithm can solve problems of 50 activities, 5, 10 and 15 machines and between 2 to 15 different types of workers in just a few seconds.