Multiple-Criteria Decision Making

Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria

Multiple Criteria Decision Making

Abstract This paper introduces a new method to estimate the weakly e cient set for the Multiobjective Linear Fractional Programming problem. The main idea is based on the procedure proposed by Tzeng and Hsu (In: G.H. Tzeng, H.F. Wang, U.P. Wen, L. Yu (Eds.), Multiple Criteria Decision Making, Springer, New York, 1994, pp. 459 -470), called CONNISE. However, as we will explain in this paper, the CONNISE method is not always convergent for problems with more than two objectives. For this reason, we have developed a new method, called "The Controlled Estimation Method", based on the same concept as CONNISE regarding the decision-maker being able to control distances between points from the estimation set he/she wants to ÿnd, while ensuring the method is convergent with problems with more than two objectives. Thus, we propose an algorithm able to calculate a discrete estimation of the weakly e cient set that veriÿes this property of the CONNISE method, but further, improves it thanks to its convergence and the fact that it satisÿes the three good properties suggested by Sayin (Math. Programming 87(3) (2000) 543): Coverage, Uniformity, and Cardinality.

Choice Rules with Size Constraints for Multiple Criteria Decision Making

In outranking methods for Multiple Criteria Decision Making (MCDM), pair-wise comparisons of alternatives are often summarized through a fuzzy preference relation. In this paper, the binary preference relation is extended to pairs of subsets of alternatives in order to define on this basis a scoring function over subsets. A choice rule based on maximizing score under size constraint is studied, which turns to formulate as solving a sequence of classical location problems. For comparison with the kernel approach, the interior stability property of the selected subset is discussed and analyzed.Combinatorial optimization; Fuzzy preferences; Integer Programming; Location; Multiple Criteria Decision Aid

A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

A REVIEW OF APPLICATIONS OF MULTIPLE - CRITERIA DECISION-MAKING TECHNIQUES TO FISHERIES

Management of public resources, such as fisheries, is a complex task. Society, in general, has a number of goals that it hopes to achieve from the use of public resources. These include conservation, economic, and social objectives. However, these objectives often conflict, due to the varying opinions of the many stakeholders. It would appear that the techniques available in the field of multiple-criteria decision-making (MCDM) are well suited to the analysis and determination of fisheries management regimes. However, to date, relatively few publications exist using such MCDM methods compared to other applicational fields, such as forestry, agriculture, and finance. This paper reviews MCDM applied to fishery management by providing an overview of the research published to date. Conclusions are drawn regarding the success and applicability of these techniques to analyzing fisheries management problems.Resource /Energy Economics and Policy,

Multiple criteria decision making in application layer networks

This work is concerned with the conduct of MCDM by intelligent agents trading commodities in ALNs. These agents consider trustworthiness in their course of negotiation and select offers with respect to product price and seller reputation. --Grid Computing

USING INFLUENCE DIAGRAMS IN MULTIPLE CRITERIA DECISION MAKING TASKS

The paper presents investigation into the solution of multiple criteria decision making problems. Influence diagrams can be used as a formal model of decision making under risk

Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools

Real-life engineering optimization problems need Multiobjective Optimization (MOO) tools. These problems are highly nonlinear. As the process of Multiple Criteria Decision-Making (MCDM) is much expanded most MOO problems in different disciplines can be classified on the basis of it. Thus MCDM methods have gained wide popularity in different sciences and applications. Meanwhile the increasing number of involved components, variables, parameters, constraints and objectives in the process, has made the process very complicated. However the new generation of MOO tools has made the optimization process more automated, but still initializing the process and setting the initial value of simulation tools and also identifying the effective input variables and objectives in order to reach the smaller design space are still complicated. In this situation adding a preprocessing step into the MCDM procedure could make a huge difference in terms of organizing the input variables according to their effects on the optimization objectives of the system. The aim of this paper is to introduce the classification task of data mining as an effective option for identifying the most effective variables of the MCDM systems. To evaluate the effectiveness of the proposed method an example has been given for 3D wing design.Comment: International Journal of Computer Science Issues at http://ijcsi.org/articles/Multiple-Criteria-Decision-Making-Preprocessing-Using-Data-Mining-Tools.ph

Sistem Penunjang Keputusan Pemilihan Sekolah Luar Biasa Dengan Metode Fuzzy Multiple Criteria Decision Making

Decision support system has many methods that can be used by decision makers. one of the methods is fuzzy multiple criteria decision making (FMCDM) this method will help decision makers to make the final decision with regard to multiple criteria decision alternatives. This final task will be to apply the decision support system with fuzzy multiple criteria decision making to determine the selection of special schools