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Parts loading scheduling in a flexible forging machine using an advanced genetic algorithm

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Abstract

A flexible forging machine (FFM) is one of the most important machines in a general flexible manufacturing system. The scheduling problem of parts loading in FFM is to reduce or preferably eliminate the changeover cost, and is an NP (Nondeterministic Polynomial solvable)-hard combinatorial optimization problem. The genetic algorithm (GA) is known to be a modern heuristic search algorithm, and is suitable for solving such a problem. When applying GA to the scheduling problem, we frequently obtain a local optimal solution rather than a best approximate solution. The goal of this paper is to solve the above-mentioned problem of falling into a local optimal solution by introducing a measure of diversity of population using the concept of information entropy. Thus, we can obtain a best approximate solution of the parts loading scheduling problem of FFM by using an advanced GA.

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Authors and Affiliations

  1. Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology, 268 Ohmae-cho, Ashikaga, 326, Japan

    YASUHIRO TSUJIMURA & MITSUO GEN

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  1. YASUHIRO TSUJIMURA
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  2. MITSUO GEN
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TSUJIMURA, Y., GEN, M. Parts loading scheduling in a flexible forging machine using an advanced genetic algorithm. Journal of Intelligent Manufacturing 10, 149–159 (1999). https://doi.org/10.1023/A:1008920519970

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