Parameter Setting for Clonal Selection Algorithm in Facility Layout Problems

The study introduces a Clonal Selection Algorithm (CSA), which depends on Artificial Immune System principles, for traditional facility layout problems. The CSA aims to minimize the total material handling cost between departments in a single manufacturing period. The determination of the optimum parameters for artificial intelligence algorithms is vital. Therefore a design of experiments study is made. The proposed algorithm is coded and tested by means test problems from literature based on the predefined parameters. The optimum solutions for small sized (5-8 department) layout problems are found. For larger (12, 15, 20, and 30 department) problems 1,077%, 5,703%, 1,126% and 3,671% improvements are obtained respectively. Better solutions are attained within shorter times compared with enumeration and CRAFT solutions.

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