A hierarchical AHP/DEA methodology for the facilities layout design problem

Abstract

Layout design often has a significant impact on the performance of a manufacturing or service industry system and is usually a multiple-objective problem. Neither an algorithmic nor a procedural layout design methodology is usually effective in solving a practical design problem. This paper proposed a hierarchical analytic hierarchy process (AHP) and data envelopment analysis (DEA) approach to solve a plant layout design problem. A computer-aided layout-planning tool was used to generate a considerable numbers of layout alternatives as well as to generate quantitative decision-making unit (DMU) outputs. The qualitative performance measures were weighted by AHP. DEA was then used to solve the multiple-objective layout problem. Empirical illustrations from a practical case study illustrated the effectiveness of the proposed methodology.

Introduction

Layout design has a significant impact on the performance of a manufacturing or service industry system (Apple, 1997) and has been an active research area for many decades (Meller and Gau, 1996). Most literature for a layout design problem falls into two major categories, algorithmic and procedural approaches.

Algorithmic approaches usually simplify both design constraints and objectives in order to reach a surrogate objective function, the solution of which can then be obtained. The majority of the existing literature reports on algorithmic approaches (Heragu, 1997). Algorithmic approaches can generate layout alternatives efficiently, particularly, when commercial software is available, e.g., Spiral^® (Goetschalckx, 1992) and LayOPT^® (Bozer et al., 1994). However, the resulting quantitative results often do not capture all of the design objectives.

Procedural approaches can incorporate both qualitative and quantitative objectives in the design process (Muther, 1973). For these approaches, the design process is divided into several steps that are solved sequentially. The success of a procedural approach implementation is dependent on the generation of quality design alternatives, often provided by an experienced designer. Thus, such an approach may be subjective and may generate an inferior solution due to a lack of a sound scientific foundation. Accordingly, both possible subjectivity and inefficiency hinder the adoption of a procedural approach to solve a layout design problem.

Layout generation and evaluation is often challenging and time consuming due to its inherent multiple objective nature and its data collection process (Lin and Sharp, 1999a, Lin and Sharp, 1999b). Past and emerging research has been aimed at developing a solution methodology to meet these needs. However, algorithmic approaches have focused mainly on minimizing flow distance in order to minimize material handling costs. On the other hand, procedural approaches have relied heavily on experts’ experience. Neither an algorithmic nor a procedural layout design methodology is necessarily effective in solving practical design problems (Yang et al., 2000).

This paper proposes a hierarchical analytic hierarchy process (AHP) and data envelopment analysis (DEA) methodology to solve plant layout design problems. A computer-aided layout planning tool is adopted to facilitate the layout alternative generation process as well as to collect quantitative performance data such as flow distance (or material handling cost), adjacency score, and shape ratio. AHP is then applied to collect qualitative performance data. Finally, DEA is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative performance data leading to the identification of performance frontiers.

The proposed methodology features both the merits of the algorithmic and procedural layout design approaches. It innovatively solves a layout design problem by utilizing both AHP and DEA procedures. A practical case study illustrates its efficiency and effectiveness.

The remainder of this paper is organized as follows. Section 2 reviews the background pertinent to this research. The details of the proposed methodology are presented in Section 3. It is followed by the empirical illustration in Section 4. Conclusions are then given in Section 5.