Feyzan Arikan

A fuzzy solution approach for multi objective supplier selection

In todays severe competitive environment the selection of appropriate suppliers is a significantly important decision for effective supply chain management. Appropriate suppliers reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. In this study a multiple sourcing supplier selection problem is considered as a multi objective linear programming problem. Three objective functions are minimization of costs, maximization of quality and maximization of on-time delivery respectively. In order to solve the problem, a fuzzy mathematical model and a novel solution approach are proposed to satisfy the decision makers aspirations for fuzzy goals. The proposed approach can be efficiently used to obtain non-dominated solutions. A numerical example is given to illustrate how the approach is utilized.

An application of fuzzy goal programming to a multiobjective project network problem

Abstract This paper presents a practical application of fuzzy goal programming (FGP) in a real-life project network problem with two objectives as minimum completion time and crashing costs wanted to be optimized simultaneously. Fuzziness in the problem stems from the imprecise aspiration levels attained by the decision maker (DM) to both objectives. These imprecise aspiration levels are quantified through the use of piecewise linear and continuous membership functions. The objective function of the FGP is to maximize the membership value of both objectives’ intersection which form the fuzzy decision. The problem is solved by using LINDO computer package and the best compromised solution is found. Comparisons between solutions of FGP, fuzzy linear programming (FLP), lexicographic maximization method (LMM) and linear programming (LP) are also presented.

A fuzzy outranking method in energy policy planning

Abstract Natural gas, imported coal and nuclear power plants are compared in terms of long-term (1996–2010) production economy in this study. Calculation of an accurate unit production cost of a power plant is generally a hard task. Especially in countries without any previous production experience, unit production cost calculation becomes more difficult. Therefore, it is difficult to determine the “best” plant alternatives to make a judgment. Owing to the subjective and incomplete data, preference of alternatives is modeled with fuzzy preference relation. Three preferences models are developed to evaluate the nondominance set from a set of alternatives for further development. During the applications of preference models, if alternatives are outranked by other alternatives, they are removed and are not taken into consideration. The approach developed is very much suited for applications in the fuzzy and uncertain environment.

Effects of different fuzzy operators on fuzzy bi-objective cell loading problem in labor-intensive manufacturing cells

In this study, a fuzzy bi-objective cell loading problem in labor-intensive cellular environments is presented and the effects of different fuzzy operators on the model are investigated. The objective functions of the proposed mathematical model for the problem are minimizing the number of the tardy jobs and the minimizing the total manpower needed. The mathematical model determines the number of cells to open and the cell size for each opened cell and assigns products to cells (cell loading) and also determines the sequence of products in each cell simultaneously. Fuzziness stems from the fuzzy aspiration levels attained to both objective functions. To solve the model, fuzzy mathematical programming approach is used and fuzzy achievement function of the model is defined by six different fuzzy operators which are min, fuzzy and, fuzzy or, minimum bounded sum, add, and product. An example problem is solved to represent the performance of the operators. Experimentation shows that the fuzzy and-operator and product-operator are suitable to reach efficient solutions for the problem on hand.

Application of fuzzy decision making in part-machine grouping

Abstract In this study, fuzzy set theory (FST) is used to set out the cell layout. A new algorithm which will consider both design and manufacturing attributes and operation sequences as factors, is proposed to formulate the problem. The structure of the algorithm is based on fuzzy decision making system (FDMS). Hence three factors mentioned above are determined as input variables and fuzzified using membership function concept. Then the pairwise comparison of the analytical hierarchy process (AHP), which ensures the consistency of the designer`s decisions when assigning the importance of one factor over another, is used to find the weights of these factors. Applying IF–THEN decision rules, parts relationship chart (PRC) is generated. After these steps, the traditional cell formation procedure is applied. Finally the proposed method is scored by performance measures such as machine investment, the amount of work load deviations within cell and between cells and the number of skippings. Also the comparison with Akturks study (International Journal of Production Research 34 (8) (1996) 2299–2315) in respect to these performance measures is presented.

A two-phase approach for multi-objective programming problems with fuzzy coefficients

In this study, a two-phase procedure is introduced to solve multi-objective fuzzy linear programming problems. The procedure provides a practical solution approach, which is an integration of fuzzy parametric programming (FPP) and fuzzy linear programming (FLP), for solving real life multiple objective programming problems with all fuzzy coefficients. The interactive concept of the procedure is performed to reach simultaneous optimal solutions for all objective functions for different grades of precision according to the preferences of the decision-maker (DM). The procedure can be also performed to obtain lexicographic optimal and/or additive solutions if it is needed. In the first phase of the procedure, a family of vector optimization models is constructed by using FPP. Then in the second phase, each model is solved by FLP. The solutions are optimal and each one is an alternative decision plan for the DM.

Bi-objective cell loading problem with non-zero setup times with fuzzy aspiration levels in labour intensive manufacturing cells

In this study, four different bi-objective mathematical models are presented to solve the cell loading problem with setup times in labour-intensive cellular environments. The objectives of each model are minimizing the number of the tardy jobs and the minimizing the total manpower needed. Model I does the following four tasks simultaneously: (1) determine the number of cells to open; (2) determine cell size among alternatives for each opened cell; (3) assign products to cells (cell loading); (4) determine the sequence of products in each cell. The other three models are extensions of model I. Model II restricts all cells to the common cell size. Model III allows lot-splitting and model IV allows lot-splitting only if the entire job can be completed. Fuzziness stems from the fuzzy aspiration levels attained to both objective functions. Fuzzy mathematical models are developed for each crisp model and fuzzy achievement function is defined by different fuzzy operators. Experimentation is conducted in two stages. In the first stage, an example problem is solved to represent the performance of the min-operator, fuzzy and- operator and the proposed representation for the fuzzy mathematical modelling. In the second stage, another example is considered to compare the models and to investigate the behaviour of each model with different setup time levels. First, models are solved to maximize the number of the early jobs considering each possible manpower level as a constraint and then fuzzy model solutions are obtained. Experimentation shows that each model represents alternative cell loading systems requirements effectively and the fuzzy and-operator and the proposed fuzzy model representations are convenient to reach the efficient solutions for each model with non-zero setup times.

A parametric model for cell formation and exceptional elements’ problems with fuzzy parameters

This paper introduces a new fuzzy mathematical model based on the fuzzy parametric programming (FPP) approach for the cellular manufacturing system (CMS) design. The aim of the proposed model is to handle two important problems of CMS design called cell formation (CF) and exceptional elements (EE) simultaneously in fuzzy environment. The model is capable to express vagueness of all the system parameters and gives the decision-maker (DM) alternative decision plans for different grades of precision. So, it is expected to provide a more realistic CMS design for real life problems. To illustrate the model proposed here, an example with fuzzy extension in data set is adopted from literature and computational results are presented.

Using fuzzy decision making system to improve quality-based investment

In this paper, fuzzy set theory is used to select the quality-based investment in small firm. Here a new algorithm, which will consider both exogenous and endogenous variables as factors, is proposed to formulate the problem. The structure of the algorithm is based on fuzzy decision-making system (FDMS), which uses fuzzy control rules. Hence, one exogenous factor and five endogenous factors mentioned above are determined as input variables and fuzzified using membership function concept. Then, the weights of these factors are fuzzified to ensure the consistency of the decision maker when assigning the importance of one factor over another. Applying IF-THEN decision rules, quality-based investments are scored. Also the comparison with Analytical Hierarchy Process (AHP) and Fuzzy Linguistic Approach (FLA) in respect to these scores is presented.

An interactive solution approach for multiple objective supplier selection problem with fuzzy parameters

Increased competition and the globalization of markets have made the purchasing function an increasingly vital activity in supply chain management. The most crucial decision in purchasing is the selection of appropriate suppliers which reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. This decision is complicated when buyers face multiple suppliers, multiple conflicting criteria and imprecise parameters. In this study a multiple sourcing supplier selection problem is considered as a multiple objective linear programming problem with fuzzy demand level and/or fuzzy aspiration levels of objectives. Three objective functions are minimizing the total monetary costs, maximizing the total quality and maximizing the service level of purchased items respectively. In order to solve the problem, a novel interactive solution procedure which integrates three well-known fuzzy mathematical models is presented. The proposed approach handles three different scenarios related with mainly the sources of fuzziness and can be efficiently used to obtain non-dominated solutions. Interactivity gives the Decision Maker opportunity to incorporate his/her preferences during the iterations of the optimization process. A numerical example is given to illustrate how the approach is utilized.