Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
10 Effective Designing Chromosome for Optimizing Advanced Planning and Scheduling
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Advanced Planning and Scheduling (APS) includes a range of capabilities from finite-capacity scheduling at the shop floor level through to constraint-based planning in a multi-plant chain. It mainly supports the integrated, constraint-based and optimal planning of the manufacturing system to reduce lead times, lower inventories, and to increase throughput. 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. The experimental results of various APS problems have offered to demonstrate the efficiency of moGA by comparing with the previous methods.