Application of the integrated framework for the plant layout evaluation problem

Application of the integrated framework for the plant layout evaluation problem

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Article ID: iaor20001989
Country: Netherlands
Volume: 116
Issue: 1
Start Page Number: 118
End Page Number: 138
Publication Date: Jul 1999
Journal: European Journal of Operational Research
Authors: ,
Keywords: decision theory: multiple criteria, analytic hierarchy process
Abstract:

During the past two decades, there was not enough research effort spent to study the plant layout evaluation problem compared with that in the plant layout design problem. Only lengthy criterion sets with little structure were proposed for evaluation purpose. Very few quantitative indices were developed for measuring the impacts resulting from the criteria. In addition, there was no reasonable mechanism available to integrate the criteria and indices to obtain a score for each layout alternative. Therefore, an integrated framework for the plant layout evaluation problem is proposed to overcome these three drawbacks. This framework includes a structured criterion set having 18 criteria represented by a three-level hierarchical structure, the corresponding quantitative and qualitative indices for the criteria, and the integration methodology. This paper presents the integration methodology. Six essential issues in developing the integration methodology are discussed (i.e., user flexibility, method of weight assignment, normalization of index values, preceding direction of integration process, insensitivity to intermediate changes, data deficiency and information imbalance). The characteristics of criteria and indices are utilized to reduce the integration effort. For example, the efforts spent in pairwise comparisons of criteria and alternative-criterion comparisons usually seen in the Analytic Hierarchy Process are greatly reduced. The application of the integrated framework to two industrial cases including an electronic equipment manufacturer and a home furnishing manufacturer demonstrates the applicability of the research results.

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