Multiple-attribute decision making methods for plant layout design problem

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Abstract

The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.

Introduction

Layout design invariably has a significant impact on the performance of a manufacturing or service industry system, and consequently has been an active research area for several decades [1], [2]. Much of the plant layout design literature is either algorithmic or of a procedural type. The former approach, such as Spiral® [3] and MULTIPLE [4], can efficiently generate alternative layout designs, but the design objectives are often over-simplified. For example, the resulting departmental shapes often deviate from practical constraints. For another example, the flow distance, either measured in Euclidean or rectilinear distance, and so may not represent the physical flow distance. This is particularly important when there are qualitative design criteria; causing the resulting layout design to lack functionality and credence for a quality solution.

Additional algorithmic approaches could model the layout design problem as a mixed integer programming formulation [5], [6], [7], [8]. These approaches use the flow distance as the surrogate function, and are often computationally prohibitive.

The procedural approach, such as the systematic layout planning procedure [9], has the flexibility to incorporate a variety of design objectives but is often lacking sound theoretical foundation and credence to be a quality solution [10].

The layout decision is usually based on both quantitative and qualitative performance ratings pertaining to the desired closeness or closeness relationships among the facilities. The ‘closeness’ is a vague notion that captures issues such as the material flow and the ease of employee supervision [11]. Clearly, the evaluation of critical criteria for a layout design is often a challenging and complex task [12], [13].

The present study focuses on the evaluation of alternative layout designs by considering both qualitative and quantitative design criteria. It simultaneously evaluates all the desired criteria for design alternatives. This will permit the desired design criteria to be better incorporated and evaluated. In addition, the direct evaluation of a design alternative in lieu of incomplete design, e.g., an improvement type layout design algorithm, will increase the level of confidence in searching for a quality solution. It solves a layout design problem using multiple-attribute decision making (MADM) methods. It seeks to evaluate a large number of layout design alternatives generated by an efficient layout design algorithm. The evaluation of a large number of design alternatives will thereby reduce the risk of missing a high-quality solution.

We propose two MADM methods in solving a plant layout design problem. They are: technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. A case study from an integrated-circuit (IC) packaging plant is adopted for the empirical testing.

The remainder of this paper is organized as follows. The pertinent literature is reviewed in Section 2. Section 3 provides the background information for the case study problem. The theories and empirical description for the two methods are discussed in detail sequentially in 4 TOPSIS, 5 Fuzzy TOPSIS. The discussion that summarizes the empirical results is given in Section 6. Finally, conclusions and future research opportunities are drawn together in Section 7.

Section snippets

Literature review

Karray et al. [11] proposed an integrated methodology using the fuzzy set theory and genetic algorithms to investigate the layout of temporary facilities in relation to the planned buildings in a construction site. It identifies the closeness relationship values between each pair of facilities in a construction site using fuzzy linguistic representation.

Grobelny [14], [15] explored the use of a fuzzy approach to facilities layout problems using a fuzzy criterion to determine the closeness

The case

The layout design problem presented in Yang and Kuo [10] is adopted for the present study. It is an IC packaging plant. The detail of IC fabrication process is not discussed in this paper for a concise presentation. Interested readers are referred to Xiao [26] for a detailed discussion of the IC fabrication process.

The IC packaging plant usually adopts the process layout strategy that clusters the same tool type to form a workstation. A product traverses all the workstations in the same

Principles of TOPSIS

A MADM problem can be concisely expressed in a matrix format, in which columns indicate attributes considered in a given problem; and in which rows list the competing alternatives. Specifically, a MADM problem with m alternatives (A1, A2, …, Am) that are evaluated by n attributes (C1, C2, …, Cn) can be viewed as a geometric system with m points in n-dimensional space. An element xij of the matrix indicates the performance rating of the ith alternative, Ai, with respect to the jth attribute, Cj,

Fuzzy TOPSIS model

It is often difficult for a decision-maker to assign a precise performance rating to an alternative for the attributes under consideration. The merit of using a fuzzy approach is to assign the relative importance of attributes using fuzzy numbers instead of precise numbers. This section extends the TOPSIS to the fuzzy environment. This method is particularly suitable for solving the group decision-making problem under fuzzy environment. We briefly review the rationale of fuzzy theory before the

Discussion

The resulting layouts vary among the different design methods to some extent. The top five design alternatives, according to the two proposed design methods, as well as the results by DEA [10], are summarized in Table 11.

All methods lead to the choice of A11 a priori as the final layout design. A15 is apparently the second choice. Other than these two alternatives, the preferences vary between methods. The fuzzy TOPSIS concludes with the same top three alternatives as those DEA. The TOPSIS

Conclusions

The layout design problem is a strategic issue and has significant impacts to the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and therefore lead to a poor layout design due to the MADM nature of a layout design problem.

The present study explored the use of TOPSIS and fuzzy TOPSIS in solving a layout design problem. A practical application

Acknowledgements

This work is supported, in part, by the National Science Council of Taiwan, Republic of China, under Grant NSC93-2212-E006-065 and NSC94-2212-E006-007.

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