A GROUP DECISION MAKING MODEL WITH INTUITIONISTIC FUZZY PREFERENCE RELATIONS

In this study a priority weight generation method on the intuitionistic fuzzy preference relations is proposed. The method consists of two linear programming models which are generalized to the case of group decision making with the weight information defined by each DM. In the models, the collective preference relations are obtained by using the intuitionistic fuzzy weighted geometric averaging operator and additive consistency is considered. Finally, a numerical example is give to verify the validity and applicability of the models

Group decision making with intuitionistic fuzzy preference relations

The capability of intuitionistic fuzzy preference relation in representing imprecise or not reliable judgments which exhibit affirmation, negation and hesitation characteristics make it an attractive research area in group decision making. As traditional fuzzy set theory cannot be used to express all the information in a situation as such, its applications are limited. In Zadeh's fuzzy set, the membership degree of an element is defined by a real value, and nonmembership is expressed by a complement of membership. This membership definition actually ignores the decision maker's hesitation in the decision making process. The advantage of Atanassov's intuitionistic fuzzy sets is the capability of representing inevitably imprecise or not totally reliable judgments and the capability of expressing affirmation, negation and hesitation with the help of membership definitions. The consistency of intuitionistic fuzzy preference relations and the priority weights of experts gathered from these preference relations play an important role in group decision making problems in order to reach an accurate decision result. In this paper, we propose a group decision making process with the usage of intuitionistic fuzzy preference relations where we mainly focus our attention on the investigation of consistency of intuitionistic fuzzy preference relations. Initially, we present two different optimization models to minimize the deviations from additive and multiplicative consistency respectively. The optimal deviation values obtained from the model results enable us to improve the consistency of considered preference relations. Then, based on consistent collective preference relations, two mathematical programming models are established to obtain the priority weights, of which the first is a linear programming model considering additive and the second one is a nonlinear model considering multiplicative consistency. Furthermore, a number of numerical illustrations are presented to observe the validity and practicality of the models. Finally, comparative analyses were performed in order to examine the differences between fuzzy and intuitionistic fuzzy preference relations and the results of the analyses showed that the priority vectors and ranking of the alternatives maintained from fuzzy or intuitionistic fuzzy preference relations change significantly. (C) 2014 Elsevier B.V. All rights reserved

Performance analysis of a hybrid system under quality impact of returns

In this paper we analyze a hybrid system that meets the demand with remanufactured or new products. In the remanufacturing stage there are uncertainties in the quality of remanufactured products, return rates and return times of returned products. These uncertainties affect raw material order quantities, processing times and material recovery rates. In the study returned products are classified by considering quality uncertainties. According to this classification remanufacturing processing times. material recovery rates, remanufacturing costs and disposal costs are determined. In order to analyze the effect of uncertainties in return quality a simulation model is constructed by using the ARENA simulation program. Our analysis denotes that under different cost scenarios quality based classification of returned products brings significant cost savings. The numerical analysis indicates that a cost improvement of more than 8% is achieved when return rates are high. (C) 2007 Elsevier Ltd. All rights reserved

A MULTI-PERIOD NEWSVENDOR PROBLEM WITH PRE-SEASON EXTENSION UNDER FUZZY DEMAND

This paper proposes a fuzzy multi-period newsvendor model with pre-season extension for innovative products. The demand of the product is represented by fuzzy numbers with triangular membership function. The holding and shortage cost parameters are considered as imprecise and also represented by triangular fuzzy numbers. As the selling season draws closer, suppliers lead times shortens and thus production costs increase. In contrast, caused by the oncoming selling season, demand fuzziness decreases and more accurate demand forecasts can be maintained that lead to lower overage/underage costs. The objective of the model is to find the best order period and the best order quantity that will minimize the fuzzy expected total cost. The model is experimented with an illustrative example and supported by sensitivity analyses

Single-Period Inventory Models with Discrete Demand Under Fuzzy Environment

This paper analysis single-period inventory models with discrete demand under fuzzy environment. In the proposed models three different cases are examined. In the first case, demand is represented by a triangular fuzzy number and a discrete membership function. In the second case, demand is a stochastic variable while inventory costs such as unit holding cost and unit shortage cost are imprecise and represented by fuzzy numbers. In the third case, both demand and inventory costs are imprecise. The objective of the models is to find the product's best order quantity that minimizes the expected total cost. The expected total cost that includes fuzzy parameters is minimized by marginal analysis and defuzzified by the centroid defuzzification method. Models are experimented with illustrative examples and supported by sensitivity analyses