Multi-factory scheduling considering the trade-offs between production efficiency and risks

Parallel decentralized production, in which multiple factories share production sorts, is on the rise to achieve a stable supply. We propose multi-factory and multi-objective production scheduling with resource allocation to improve multiple metrics such as quality throughput, setup time, and Q-time compliance rate. First, proposed P3D-QAP2MF of a machine allocation algorithm beyond factories is verified. Second is to integrate it with a multi-objective multi-factory hybrid flow-shop scheduling DABC-TRANS method based on DABC (a discrete artificial bee colony) and to verify those synergistic effect. Compared to Critical Ratio-based conventional method, P3D-QAP2MF improves rates of Q-time compliance, on-time delivery and setup time. Furthermore, the algorithm that combines P3D-QAP2MF and DABC-TRANS achieve highly performance than P3D-QAP2MF in terms of on-time delivery and setup time. Our proposal can achieve improvements of production efficiency and risk distribution in Multi-factory.