EVALUATING ALTERNATIVE FARMING SYSTEMS: A FUZZY MADM APPROACH

This paper develops a decision support method that integrates measures of achievement in the economic, environmental, and social aspects of farming. The decision support method combines multiple attribute decision making (MADM) with fuzzy logic. The fuzzy MADM model fully ranks decision alternatives relative to the preferences of decision makers and overcomes several problems inherent in other MADM approaches. It is concluded that fuzzy MADM can improve decision making on the farm.fuzzy logic, fuzzy sets, multiple attribute decision making, MADM, Institutional and Behavioral Economics, Research Methods/ Statistical Methods,

A Group Decision Making Approach for Dealing with Fuzziness in Decision Process

In order to deal with various imprecise opinions and preferences of decision makers in group decision-making process, this paper proposes a fuzzy group decision-making approach. The approach has three advantages from existing approaches. First, it can handle simultaneously group members’ fuzzy preferences for alternative solutions, fuzzy judgments for solution selection criteria and fuzzy weights for their roles in group decision-making to arrive a group consensus decision. Second, it allows group members to generate selection criteria for the best solution rather than assume them to be given before a group meeting. The third is that it uses general fuzzy number to express linguistic terms which is used to describe the fuzziness of individual preferences, judgments and weights in group decision-making. It therefore accepts any forms of fuzzy number, including triangular fuzzy number, rectangle fuzzy number and continuous fuzzy number, when applying the group decision-making approach

Fuzzy multi-criteria simulated evolution for nurse re-rostering

Abstract: In a fuzzy environment where the decision making involves multiple criteria, fuzzy multi-criteria decision making approaches are a viable option. The nurse re-rostering problem is a typical complex problem situation, where scheduling decisions should consider fuzzy human preferences, such as nurse preferences, decision maker’s choices, and patient expectations. For effective nurse schedules, fuzzy theoretic evaluation approaches have to be used to incorporate the fuzzy human preferences and choices. The present study seeks to develop a fuzzy multi-criteria simulated evolution approach for the nurse re-rostering problem. Experimental results show that the fuzzy multi-criteria approach has a potential to solve large scale problems within reasonable computation times

The Selection of Investment Priorities using Expanded Dual Hesitant Fuzzy Sets

The concept of expanded dual hesitant fuzzy set is a concept that can be applied in decision-making problems. In decision-making problems, expanded dual hesitant fuzzy set can be applied to represent the opinions of multiple experts or stakeholders in a more elaborate manner. It enables decision-makers to provide more detailed information about their preferences and hesitancy

On a Consensus Measure in a Group Multi-Criteria Decision Making Problem.

A method for consensus measuring in a group decision problem is presented for the multiple criteria case. The decision process is supposed to be carried out according to Saaty's Analytic Hierarchy Process, and hence using pairwise comparison among the alternatives. Using a suitable distance between the experts' judgements, a scale transformation is proposed which allows a fuzzy interpretation of the problem and the definition of a consensus measure by means of fuzzy tools as linguistic quantifiers. Sufficient conditions on the expert's judgements are finally presented, which guarantee any a priori fixed consensus level to be reached.group decision making; multiple criteria; degree of consensus; fuzzy preferences

Fuzzy preferences in decision-making

The purpose of the paper is to formulate some properties of Orlovsky’s concept of decision making. This concept is related to the notions of max-min and max *-transitivity. The concept of fuzzy acyclicity is proposed to characterize the existence of Orlovsky’s choice set. The problem of the emptiness set is analyzed. The present approach is applicable to many decision-making problems

Application of F-WASPAS in the Ranking of Crops for Agro-Processing: The Case of Ikondo Ward in Njombe, Tanzania

The purpose of this work was to develop and apply a Fuzzy Weighted Aggregated Sum Product Assessment (F-WASPAS) method in ranking selected crops for agro-processing at Ikondo Ward in Njombe Region, Tanzania. The fuzzy technique for order preferences by similarity to ideal solution (TOPSIS) was applied in determining the fuzzy importance weights of criteria, while the fuzzy WASPAS successfully ranked the crops, and maize was ranked the highest. Keywords:    F-WASPAS, Linguistic variables, Fuzzy aggregation, Agro-processing, Decision making, Multi-Criteria Decision Making

The dynamics of consensus in group decision making: investigating the pairwise interactions between fuzzy preferences.

In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear nature of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations.

Fuzzy Set Ranking Methods and Multiple Expert Decision Making

The present report further investigates the multi-criteria decision making tool named Fuzzy Compromise Programming. Comparison of different fuzzy set ranking methods (required for processing fuzzy information) is performed. A complete sensitivity analysis concerning decision maker’s risk preferences was carried out for three water resources systems, and compromise solutions identified. Then, a weights sensitivity analysis was performed on one of the three systems to see whether the rankings would change in response to changing weights. It was found that this particular system was robust to the changes in weights. An inquiry was made into the possibility of modifying Fuzzy Compromise Programming to include participation of multiple decision makers or experts. This was accomplished by merging a technique known as Group Decision Making Under Fuzziness, with Fuzzy Compromise Programming. Modified technique provides support for the group decision making under multiple criteria in a fuzzy environment.https://ir.lib.uwo.ca/wrrr/1001/thumbnail.jp

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