Zülal Güngör

A fuzzy AHP approach to personnel selection problem

Due to the increasing competition of globalization and fast technological improvements, world markets demand companies to have quality and professional human resources. This can only be achieved by employing potentially adequate personnel. In this paper, we proposed a personnel selection system based on Fuzzy Analytic Hierarchy Process (FAHP). The FAHP is applied to evaluate the best adequate personnel dealing with the rating of both qualitative and quantitative criteria. The result obtained by FAHP is compared with results produced by Yagers weighted goals method. In addition to above-mentioned methods, a practical computer-based decision support system is introduced to provide more information and help manager make better decisions under fuzzy circumstances.

K-harmonic means data clustering with simulated annealing heuristic

Abstract Clustering procedures partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some predefined criteria. Clustering is a popular data analysis and data mining technique. Since clustering problem have NP-complete nature, the larger the size of the problem, the harder to find the optimal solution and furthermore, the longer to reach a reasonable results. One of the most used techniques for clustering is based on K-means such that the data is partitioned into K clusters. Although k-means algorithm is easy to implement and works fast in most situations, it suffers from two major drawbacks. One is sensitivity to initialization and the other is convergence to local optima. It is seen from the studies K harmonic means clustering solves the problem of initialization but since its greedy search nature, the second problem; convergence to local optima, still remains. In this paper we develop a new algorithm for solving this problem based on a simulated annealing technique – simulated annealing K-harmonic means clustering (SAKHMC). The experiment results on the Iris and the other well known data, illustrate the robustness of the SAKHMC clustering algorithm.

A new mixed integer linear programming model for product development using quality function deployment

Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.

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.

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 genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.

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.

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

The aim of this paper is two fold. First we investigate the three different types of systems, namely cellular layout (CL), process layout (PL) and virtual cells (VCs). VCs are addressed by using family-based scheduling rule, developed by a part allocation algorithm in a PL. Simulation is used to compare three types of systems under the performance metrics such as mean flow time and mean tardiness. Results indicate that VCs have better responsiveness in terms of the performance metrics. Second we develop a new ant colony optimization-based metamodels fed by existing simulation runs to represent the prospective simulation runs, which require a lot of time and effort. Regression metamodels, which allow us to obtain much faster results, are seen to be promising to estimate the systems behaviors.

Applying K-harmonic means clustering to the part-machine classification problem

Cellular manufacturing system (CMS) is an application of group technology (GT) to the production environment. There are many advantages of CMS over traditional manufacturing systems like reduction in setup-time, throughput time, etc. The grouping of machine cells and their associated part families so as to minimize the cost of material handling is a major step in CMS and it is called as cell formation (CF) problem. Cell formation is important to the effective performance of manufacturing. In this paper, an attempt has been made to effectively apply the K-harmonic means clustering technique to form machine cells and part families simultaneously, which we call K-harmonic means cell formation (KHM-CF). A set of 20 test problems with various sizes drawn from the literature are used to test the performance of the proposed algorithm. Then, the results are compared with the optimal solution, and the efficacy of the proposed algorithms is discussed. The comparative study shows that the proposed KHM-CF algorithm improves the grouping efficacy for 70% of the test problems, and gives the same results for 30% of the test problems.

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.