Eleftherios Siskos

Elicitation of criteria importance weights through the Simos method: A robustness concern

In the field of multicriteria decision aid, the Simos method is considered as an effective tool to assess the criteria importance weights. Nevertheless, the methods input data do not lead to a single weighting vector, but infinite ones, which often exhibit great diversification and threaten the stability and acceptability of the results. This paper proves that the feasible weighting solutions, of both the original and the revised Simos procedures, are vectors of a non-empty convex polyhedral set, hence the reason it proposes a set of complementary robustness analysis rules and measures, integrated in a Robust Simos Method. This framework supports analysts and decision makers in gaining insight into the degree of variation of the multiple acceptable sets of weights, and their impact on the stability of the final results. In addition, the proposed measures determine if, and what actions should be implemented, prior to reaching an acceptable set of criteria weights and forming a final decision. Two numerical examples are provided, to illustrate the papers evidence, and demonstrate the significance of consistently analyzing the robustness of the Simos method results, in both the original and the revised methods versions.

Robustness analysis in Multi-Objective Mathematical Programming using Monte Carlo simulation

In most multi-objective optimization problems we aim at selecting the most preferred among the generated Pareto optimal solutions (a subjective selection among objectively determined solutions). In this paper we consider the robustness of the selected Pareto optimal solution in relation to perturbations within weights of the objective functions. For this task we design an integrated approach that can be used in multi-objective discrete and continuous problems using a combination of Monte Carlo simulation and optimization. In the proposed method we introduce measures of robustness for Pareto optimal solutions. In this way we can compare them according to their robustness, introducing one more characteristic for the Pareto optimal solution quality. In addition, especially in multi-objective discrete problems, we can detect the most robust Pareto optimal solution among neighboring ones. A computational experiment is designed in order to illustrate the method and its advantages. It is noteworthy that the Augmented Weighted Tchebycheff proved to be much more reliable than the conventional weighted sum method in discrete problems, due to the existence of unsupported Pareto optimal solutions.

E-government Benchmarking in European Union: A Multicriteria Extreme Ranking Approach

E-government benchmarking is being conducted by various organizations but its assessment is based on a limited number of indicators and does not highlight the multidimensional nature of the electronically provided services. This paper outlines a multicriteria evaluation system based on four points of view: (1) infrastructures, (2) investments, (3) e-processes, and (4) users’ attitude in order to evaluate European Union countries. In this paper, twenty one European Union countries are evaluated and ranked over their e-government progress. Their ranking is obtained through an additive value model which is assessed by an ordinal regression method and the use of the decision support system MIIDAS. In order to obtain robust evaluations, given the incomplete determination of inter-criteria model parameters, the extreme ranking analysis method, based on powerful mathematical programming techniques, has been applied to estimate each country’s best and worst possible ranking position.

Disaggregation Approach to Value Elicitation

The philosophy of preference disaggregation in multicriteria decision analysis encapsulates the assessment/inference of preference models, from given preferential structures, and the implementation of decision aid activities through consistent and robust operational models. This chapter presents a new outlook on the well-known UTA method, which is devoted to the elicitation of values through the inference of multiple additive value models. On top of that, it incorporates the latest theoretical developments, related to the robustness control of both the decision model and the surfacing decision aiding conclusions. An application example on job evaluation is elaborated as an educative example, while other potential areas for future use applications of the methodological framework are listed. The chapter concludes with several promising directions for future research.

Robust Evaluation of Risks in Ship-to-Ship Transfer Operations: Application of the STOCHASTIC UTA Multicriteria Decision Support Method

Decision making in the maritime environment is a complex and difficult task. The stochastic nature of maritime operations, in combination with the complicated and often hostile sea setting, composes a scene where obvious solutions are not always the best choice. The assessment and evaluation of risks often lays on the exploitation of the experts’ knowledge. However, this approach may suffer from uncertainty, stemming from potential biases inherent to the opinions of the experts. The aim of this chapter is to evaluate the risks of a ship-to-ship transfer of cargo operations, proposing a challenging approach to address the aforementioned uncertainty, by utilizing a sophisticated and targeted multi-criteria decision aid (MCDA) framework. The STOCHASTIC Utility Additives (UTA) method, adopting the philosophy of aggregation–disaggregation, is applied, coupled with a robustness control procedure. This methodological framework helped in the examination, management and reduction of uncertainty, and in the eventual attainment of robust and reliable evaluation results.

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Abstract Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.

International Cooperation for Clean Electricity: A UTASTAR Application in Energy Policy

Energy policy making is a complex, multidisciplinary process that usually requires the assessment of a large number of factors. Consequently, Multiple Criteria Decision Making, which is a sub-discipline of Operational Research, has long been employed as an approach to addressing problems of this domain. This paper aims to explore how the preference disaggregation-aggregation paradigm, which infers a preference model from given global preferences on a set of reference alternatives, can support decision making in energy policy design and implementation. In this direction, a detailed literature review of multicriteria analysis applications in this domain is conducted, in which a knowledge gap regarding preference disaggregation approaches can be observed. The UTASTAR model is, then, described in detail and implemented in an energy policy application regarding the potential development of clean electricity projects through the cooperation between European Union member states and 22 neighbouring countries with which the Union has already established ties towards economic and energy market integration. The results of the study show that European countries outside the Union feature better potential for hosting clean energy projects compared to Middle East and North African countries; finally, the analysis suggests that UTASTAR can also provide concrete insight into the criteria weighting dynamics, as inferred by the global preferences of the decision makers.