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.

Job evaluation is defined as the methods and practices of ordering jobs or positions with respect to their value or worth to the organization. The purpose of the job evaluation is eliminating the pay inequalities by developing a pay structure based on values of the jobs. The job evaluation problem may be treated as a managerial decision-making problem under multiple criteria. In this study, an integrated fuzzy approach is developed for job evaluation problem which aims to establish a base for an ideal compensation system. In the proposed approach initially, the relative importance weights of the job factors are determined and subsequently, job compensation groups are maintained by a fuzzy inference system integrated with importance weights of the factors. A sample case study has been considered and ten different jobs are classified into four different job compensation groups according to the proposed approach.

Disaster management is extremely important in today's world, which is defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters. Disaster response is one of the critical stages of disaster management, which necessitates spontaneous decision making when a disaster occurs. Fuzzy inference systems are very suitable for such decision making environments since the inputs and outputs of disaster events cannot be sharply defined. This chapter describes potential applications of fuzzy inference systems in disaster response.

The paper evaluation for a special issue is a lengthy process, which requires the collective collaboration of authors, reviewers and editors. In the first step, the evaluation criteria specific to each journal are determined by the editorial board and then reviewers make judgements whether to accept or reject the submitted manuscript by considering the previously determined evaluation criteria. The last decision of the submitted paper is given by the editor of this special issue by considering the reviewers’ evaluations. The purpose of this study is to propose a systematic approach for the selection of academic papers to a special issue. This approach integrates fuzzy analytical hierarchy process (FAHP) with fuzzy multi-criteria scoring method (FMSM). The results of the FAHP imply that, through the academic paper evaluation process, the most important criteria are originality of the subject and the methodology proposed in the research study. After these criteria, discussion and conclusions and data analysis have higher importance weights than the other criteria as language, abstract and keywords, literature review and format of the paper.

The urban rail system in Istanbul carries in total more than 700.000 passengers per a day on different types of lines which require well organized risk governance. This paper evaluates the urban rail systems in Istanbul under different risk factors using Fuzzy Analytic Hierarchy Process (FAHP) to uncover the critical risk criteria of these systems and to make a multi-criteria evaluation of existing rail systems for the assignment of the scarce resources. Linguistic variables are used in the pairwise comparisons of criteria and alternatives. The risk factors considered are regional criticality, line characteristics, line safety and station structure. The evaluation results imply that the most risky critical urban rail system in Istanbul is the subway line from Sishane to Darussafaka.

Nowadays companies have to be environmentally conscious because of the governmental policies on environmental issues, consumer preferences and insufficient resources. Therefore, the multicriteria evaluation of manufacturers according to environmentally consciousness may give important outputs for the government, companies and consumers. This study presents a framework for multicriteria evaluation of manufacturers. Initially the criteria for being an environmentally conscious manufacturer are determined and their importance ranking is maintained by using Fuzzy Analytical Hierarchy Process (FAHP). Subsequently, the most important criteria are chosen and set as input variables for a Fuzzy Inference System (FIS) to get output values representing environmentally consciousness scores of manufacturers. The proposed multicriteria evaluation method is experimented with an numerical illustration The results of the numerical illustration implies that, the most important criteria for being an environmentally conscious manufacturer are; life cycle analysis, design for environment, environmentally conscious process planning, recycling and reducing the waste ate the source.

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 repre-sented 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.

Risk management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events. In the last decade risk management has become a vital part of supply chain management. The risk sources of supply chain are identified in five areas namely: transport/distribution, manufacturing, order cycle, warehousing, and procurement. The aim of the study is to build a supply chain risk measurement system using Fuzzy Inference Systems (FIS).

In this paper, the optimization of single-period inventory problem under uncertainty is analyzed. Due to lack of historical data, the demand is subjectively determined and represented by a fuzzy distribution. Uncertain demand causes an uncertain total cost function. This paper intends to find an analytical method for determining the exact expected value of total cost function for a fuzzy single-period inventory problem. To determine the optimum order quantity that minimizes the fuzzy total cost function we use the expected value of a fuzzy function based on credibility theory. The closed-form solutions to the optimum order quantities and corresponding total cost values are derived. Numerical illustrations are presented to demonstrate the validity of the proposed method and to analyze the effects of model parameters on optimum order quantity and optimum cost value. The proposed methodology is applicable to other inventory models under uncertainty.

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. Santrauka Straipsnyje pasiūlytas neraiškusis keliu laikotarpiu pardavimo modelis, papildytas paruošiamuoju laikotarpiu inovatyviems produktams. Produkto paklausa apibūdinama neraiškiaisiais skaičiais, aprašytais trikampe priklausomumo funkcija. Turto ir sanaudu parametrai laikomi netiksliais ir taip pat apibūdinami trikampe priklausomumo funkcija aprašytais neraiškiaisiais skaičiais. Kai pardavimo laikotarpis priarteja, tiekimo laikas trumpeja, o gaminio kaina išauga. Priešingai, del praeinančio pardavimo laikotarpio paklausos neapibrežtumas sumažeja ir galima tiksliau prognozuoti paklausa, o tai padeda sumažinti išlaidas del gaminiu pertekliaus ar trūkumo. Šio modelio tikslas – rasti geriausia užsakymo laika ir geriausia užsakymo kieki, kurie padetu sumažinti visu numatomu sanaudu neapibrežtuma. Pateikiamas modelio taikymo pavyzdys ir modelio jautrumo analize.

In this paper a fuzzy c-means (FCM) based cell formation (CF) algorithm is used to design cellular manufacturing in a tractor manufacturing firm. In cell formation problems as the size of the part-machine matrix increases the problem gets complicated. Fuzzy models are suitable for CF problems by allowing the representation of uncertain information. FCM based algorithms seem to be efficient for cell formation problems at cellular manufacturing design.

RFID (radio frequency identification) has been taking great attention of industry for its various advantages. This technology enables to monitor and track items in production facilities, warehouse operations and distribution. However, RFID system performance is dramatically affected from the operational settings of the system. In particular, identification of appropriate levels for each operational factor is not trivial. In this paper we address an effective methodology to reveal appropriate operational settings for any RFID system. The purpose of this study is two fold (1) to present an organized, concise methodology for identification of influential factors of any RFID system (2) to evaluate operational parameters on RFID controlled conveyor system.