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Managing a new multi-objective model for the dynamic facility layout problem

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Abstract

In today’s manufacturing environment, the facility layout needs to be adaptable to changes. This situation requires the solution of the dynamic layout problem. But in previous studies of dynamic facility layout optimization, the main objective is to minimize the sum of the re-arrangement and material handling costs. To be more realistic, each of these cost terms in objective function might be of different importance to decision makers. In this research, the objective function has been considered as two distinct functions. This formulation enables decision makers to apply their own views. On the other hand, in the proposed model the adjacency-based objective aims at maximizing adjacency scores between the facilities in a facility layout has also been used. The proposed multi-objective model was defined as a complex combinatorial optimization problem. It has also been the objective of the present study to evaluate some of the known methods that have been proposed to solve the multi-objective problem. The results for test problems showed that the population based metaheuristic methods are useful tools in solving proposed model.

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Authors and Affiliations

  1. Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Saeed Emami & Ali S. Nookabadi

Authors
  1. Saeed Emami
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  2. Ali S. Nookabadi
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Correspondence to Saeed Emami.

Appendix A

Appendix A

A dynamic facility layout consists of six departments and three time periods is considered. The same departments are to be arranged or rearranged across three time periods. The re-arrangement cost is 10. The material flow cost matrices (from–to charts), distance matrix, and closeness rating matrices are shown as below (Tables 11, 12, and 13):

Table 11 Material flow matrix of the three time periods
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Table 12 Closeness rating matrix of the three time periods
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Table 13 Distance matrix
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Table 15 Comparison results of the methods used for this problem using TOPSIS
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  • Step 1 of methodology:

    Table 14 shows the nondominated solutions obtained from the different methods employed (Table 15).

    Table 14 Non-dominated solutions for each method used
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  • Step 2 of methodology:

    For choosing the best solution, the set of nondominated solutions related to the NSGA-II algorithm (Table 14) is considered. By applying the TOPSIS method and based on the explained assumptions in Section 4, the nondominated solution 6 from NSGA-II column is selected as the best solution.

    The solutions obtained using both proposed and Chen and Rogers’s methodology are shown in Table 16. As it can be seen, the solution of the proposed methodology dominates the solution of Chen and Rogers’ method.

    Table 16 The solutions provided by proposed methodology and Chen and Rogers’s methodology
    Full size table

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Emami, S., S. Nookabadi, A. Managing a new multi-objective model for the dynamic facility layout problem. Int J Adv Manuf Technol 68, 2215–2228 (2013). https://doi.org/10.1007/s00170-013-4820-5

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  • DOI: https://doi.org/10.1007/s00170-013-4820-5

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