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DEX2Web – A Web-Based Software Implementing the Multiple-Criteria Decision-Making Method DEX
Abstract
DEX2Web is an online suite of tools to help individuals and groups with their decision-making. DEX2Web implements the qualitative multiple-criteria decision-modelling method DEX. DEX is useful for supporting complex decision-making tasks, where there is a need to select a particular option from a set of possible ones to satisfy the goals of the decision-maker. DEX2Web primarily supports interactive development and evaluation of DEX models. Most of the functionality of the first available version of DEX2Web is inherited from its desktop ancestor DEXi: development of DEX model structure, editing of attributes and their scales, definition of decision rules, multi-attribute evaluation and analysis of alternatives, and presenting evaluation results with charts. DEX2Web has a modern software architecture and employs a newly developed DEX software library. DEX2Web is freely available on https://dex2web.ijs.si/.
DEX (Decision EXpert) is a hierarchical, qualitative, rule-based, multi-criteria decision modeling method. It combines multi criteria decision analysis with artificial intelligence and is particularly suited for sorting/classification decision problems. DEX puts special attention on the transparency, comprehensibility, consistency, and completeness of decision models, as well as on methods for the analysis, justification, and explanation of decisions. The approach relies on using software tools that actively support the decision maker in both the creation and utilization stages of the process. Since its inception in the 1980s, DEX has been successfully applied in hundreds of real-world decision projects in various areas, including economy, ecology, agronomy, medicine, and health care. In the last decade, there is an increasing trend of including DEX models in decision support systems. In this chapter, DEX is described from the theoretical and practical viewpoint and further explained in terms of motivation, history, software, applications, and method extensions. The presentation is supported by three examples: a didactic example of employee selection and two real-world industrial applications of choosing a raw-material location and assessing electric energy production technologies, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Decision making is a challenging human activity, which often requires support from appropriate methods and tools. One such approach is provided by multiple criteria decision analysis (MCDA), which typically proceeds by developing a multi-criteria decision model, which is in turn used for the evaluation and analysis of decision alternatives. Currently, there are many diverse MCDA methods, and some of them are supported by software. DEX (Decision EXpert) method is in our focus and it belongs to the class of qualitative hierarchical multi-criteria models. The aim is to design a new web-based architecture that would facilitate in developing and using DEX models. We do this through an investigation of already developed MCDA software and their architectures, formulating the requirements for the DEX software and developing appropriate system architecture. The first implementation of this work-in-progress development is expected towards the end of 2019.
DEX (Decision EXpert) is a qualitative multi-criteria decision modeling methodology. DEX models are used to evaluate and
analyze decision alternatives. An essential component of DEX
models are decision rules, represented in terms of decision tables.
Decision tables may contain many elementary decision rules and
may be difficult to be understood by the decision maker. A more
compact and comprehensible representation is obtained by
converting elementary decision rules to complex rules. The DEXRule algorithm, which is currently implemented in software
DEXi, has been found inefficient with large decision tables. This
research is aimed at improving the efficiency of the DEX-Rule
algorithm. We propose a novel algorithm, called jRule, which
generates complex rules by specialization. According to
performance analysis, jRule is indeed more efficient than DEXRule. The compactness of complex rules produced by both
algorithms varies and there is no clear winner.
DEX is a qualitative multi-criteria decision analysis method. The method supports decision makers in making complex decisions based on multiple, possibly conflicting, attributes. The attributes in DEX have qualitative value scales and are structured hierarchically. The hierarchical topology allows for decomposition of the decision problem into simpler sub-problems. In DEX, alternatives are described with qualitative values, taken from the scales of corresponding input attributes in the hierarchy. The evaluation of alternatives is performed in a bottom-up way, utilizing aggregation functions, which are defined for every aggregated attribute in the form of decision rules. DEX has been used in numerous practical applications—from everyday decision problems to solving decision problems in the financial and ecological domains. Based on experience, we identified the need for three major methodological extensions to DEX: introducing numeric attributes, the probabilistic and fuzzy aggregation of values and relational models. These extensions were proposed by users of the existing method and by the new demands of complex decision problems, which require advanced decision making approaches. In this paper, we introduce these three extensions by describing the extensions formally, justifying their contributions to the decision making process and illustrating them on a didactic example, which is followed throughout the paper.
An approach to decision making that integrates multi-attribute decision techniques with expert systems is described. The approach is based on the explicit articulation of qualitative decision knowledge which is represented by a tree of attributes and decision rules. The decision making process is supported by a specialized expert system shell for interactive construction of the knowledge base, evaluation of options and explanation/analysis of the results. Practical use of the shell is illustrated by an application in the field of performance evaluation of enterprises.
DEX is a qualitative multi-attribute decision modeling methodology that integrates multi-criteria
decision modeling with rule-based expert systems. The method was conceived in 1979. Since, it has been
continuously developed and implemented in a wide range of computer programs that have been applied
in hundreds of practical decision-making studies. Here we present its main methodological concepts,
contributions to the theory and practice of decision support, and outline a history of its development and
evolution.
New technologies, especially the World-Wide Web technologies, have created many opportunities for research about Decision Support Systems. This paper reviews key concepts and technical issues. The technology of DSS is evolving rapidly and academic researchers need to catch-up with practitioners who are implementing innovative DSS. The paper briefly mentions a number of Web sites with innovative DSS that highlight current developments. Much remains however to be investigated and studied if DSS are to contribute to a company's success.
In two volumes, this new edition presents the state of the art in Multiple Criteria Decision Analysis (MCDA). Reflecting the explosive growth in the field seen during the last several years, the editors not only present surveys of the foundations of MCDA, but look as well at many new areas and new applications. Individual chapter authors are among the most prestigious names in MCDA research, and combined their chapters bring the field completely up to date.
Part I of the book considers the history and current state of MCDA, with surveys that cover the early history of MCDA and an overview that discusses the “pre-theoretical” assumptions of MCDA. Part II then presents the foundations of MCDA, with individual chapters that provide a very exhaustive review of preference modeling, along with a chapter devoted to the axiomatic basis of the different models that multiple criteria preferences. Part III looks at outranking methods, with three chapters that consider the ELECTRE methods, PROMETHEE methods, and a look at the rich literature of other outranking methods.
Part IV, on Multiattribute Utility and Value Theories (MAUT), presents chapters on the fundamentals of this approach, the very well known UTA methods, the Analytic Hierarchy Process (AHP) and its more recent extension, the Analytic Network Process (ANP), as well as a chapter on MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). Part V looks at Non-Classical MCDA Approaches, with chapters on risk and uncertainty in MCDA, the decision rule approach to MCDA, the fuzzy integral approach, the verbal decision methods, and a tentative assessment of the role of fuzzy sets in decision analysis.
Part VI, on Multiobjective Optimization, contains chapters on recent developments of vector
and set optimization, the state of the art in continuous multiobjective programming, multiobjective combinatorial optimization, fuzzy multicriteria optimization, a review of the field of goal programming, interactive methods for solving multiobjective optimization problems, and relationships between MCDA and evolutionary multiobjective optimization (EMO). Part VII, on Applications, selects some of the most significant areas, including contributions of MCDA in finance, energy planning problems, telecommunication network planning and design, sustainable development, and portfolio analysis. Finally, Part VIII, on MCDM software, presents well known MCDA software packages.
This book presents an introduction to MCDA followed by more detailed chapters about each of the leading methods used in this field. Comparison of methods and software is also featured to enable readers to choose the most appropriate method needed in their research. Worked examples as well as the software featured in the book are available on an accompanying website.
A multiattribute decision making, based on a new approach to utility measurement is treated. The approach assumes the decision knowledge acquisition by examples of utilities of some alternatives of their parts. The proposed decision making process differs from the existing ones in offering the possibility of identification and verification of utility functions in a learning loop between the man and a computer. The proposed decision making approach is supported by the corresponding computer programs and it has been successfully used in ten of practical decision making situations.
World Wide Web technologies have transformed the design, development, implementation and deployment of decision support systems. This article reviews and summarizes recent technology developments, current usage of Web-based DSS, and trends in the deployment of such systems. Many firms use the Web as a medium to convey information about DSS products or to distribute DSS software. The use of Web-based computation to provide product demonstrations or to deploy DSS applications for remote access remains less common. The academic literature on Web-based DSS is largely focused on applications and implementations, and only a few articles examine architectural issues or provide design guidelines based on empirical evidence.
Environmental concepts are becoming common and ever more important parts of decision support models, which are a vital part of decision support systems. proDEX is a software tool for use of decision support models that are based on extended DEX methodology for qualitative multi-criteria decision modelling and support. The supported modelling methodology and the software features are adjusted to environmental modelling needs.
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