Construction method of genetic algorithms for large-scale complex production scheduling problems

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1. Proposition of a new selection procedure capable of keeping a diverse populationIt was shown that the proposed selection procedure (called Partial Enumeration Selection Method : PESM) can keep a high diversity population through the generations without deteriorating the accuracy of the solution. The robustness of the algorithm was also shown for variations of several schedule parameters.2. Proposition of decomposition and search space reduction methodsIt was shown from computational experiments for large-scale scheduling problems that the proposed decomposition and search space reduction methods work well with genetic algorithms.3. Design of genetic algorithms capable of dealing flexibly with complex constraintsThe design of genetic algorithms was proposed in such a way that the scheduling problems can be solved by only adding a module related to the added constraints. The scheduling problems of a job shop process with parallel machines and the worker allocation problem in a job shop process were solved by the proposed algorithm. Moreover a new decoding method was proposed for no-buffer job shop problems.4. Application to multi-objective optimization problemsThe PESM-based genetic algorithm was applied to solving multi-objective flowshop problems and smooth Pareto fonts were obtained.