Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system | Zendy

Haipeng Zhang | Zendy; Mitsuo Gen | Zendy

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Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system

Author(s) -

Haipeng Zhang,

Mitsuo Gen

Publication year - 2006

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

ISBN - 1-59593-186-4

DOI - 10.1145/1143997.1144293

Subject(s) - workload , computer science , job shop scheduling , scheduling (production processes) , genetic algorithm , mathematical optimization , genetic algorithm scheduling , dynamic priority scheduling , flexible manufacturing system , selection (genetic algorithm) , distributed computing , chromosome , schedule , flow shop scheduling , artificial intelligence , mathematics , machine learning , biochemistry , chemistry , gene , operating system

In this paper, a novel approach for designing chromosome has been proposed to improve the effectiveness, which called multistage operation-based genetic algorithm (moGA). The objective is to find the optimal resource selection for assignments, operations sequences, and allocation of variable transfer batches, in order to minimize the total makespan, considering the setup time, transportation time, and operations processing time. The plans and schedules are designed considering flexible flows, resources status, capacities of plants, precedence constraints, and workload balance in Flexible Manufacturing System (FMS). The experimental results of various Advanced Planning and Scheduling (APS) problems have offered to demonstrate the efficiency of moGA by comparing with the previous methods.

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