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Application of genetic algorithm to a large-scale scheduling problem for a metal mold assembly process

A genetic algorithm is applied to an optimal scheduling problem for a metal mold assembly process. The process is operated basically in a job shop mode with additional constraints, such as the precedence constraints among jobs and the allocation of each operation to different kinds of parallel machines. The objective of the problem is to minimize the sum of the tardiness of each job. Furthermore, the problem is large-scale because of a long scheduling period. In the design of genetic algorithm an individual description and genetic operators are proposed to satisfy the constraints. A method for decomposing the set of jobs is also proposed to cope with the large-scale problem. In order to examine the effectiveness of the algorithm, a simulation is carried out on the basis of large-scale operation data.