Production planning conflict resolution of complex product system in group manufacturing: A novel hybrid approach using ant colony optimization and Shapley value
Production planning conflict resolution of complex product system in group manufacturing: A novel hybrid approach using ant colony optimization and Shapley value
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DOI：
10.1016/j.cie.2015.12.015
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发表时间：
2016-04
期刊：
影响因子：
--
通讯作者：
B. Du;Shunsheng Guo
中科院分区：
文献类型：
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作者：
B. Du;Shunsheng Guo
生产计划冲突是制造系统中导致生产延迟的关键因素。由于复杂产品系统(COPS)的生产计划比商品复杂，一些制造企业为了减少冲突、扩大资源配置，组建了新的成组制造(GMfg)企业。此外，客户不仅要求集团成员及时提供产品，还要求集团成员对产品成本负责。此外，随着生产计划的冲突消解，冲突消解利润在群体成员之间的分配成为GMfg中一个悬而未决的问题。为此，本文提出了一种新的冲突消解模型--GMfg生产计划冲突消解模型(GPPCR&COPS)。在该方法中，使用多层进度视图(MLPV)模型来检测COPS的冲突。此外，还采用了双目标(进度延误和总费用)，并考虑了群成员之间的CRP分配。提出了一种双路径搜索蚁群算法(DPS-ACO)和Shapley值相结合的双目标优化算法。最后，给出了蚁群优化算法(ACO)和NSGA-II算法的实例研究和性能比较。实验结果表明，该方法在搜索最优解时具有更好的性能。
Production planning conflict (PPC) is a key factor leads to production delay in manufacturing system. As production planning of complex product system (CoPS) is more complex than commodity products, some manufacturing enterprises set up a new group manufacturing (GMfg) enterprise to decrease conflict and expand resources deployment. Besides, group members have been forced by customers not only to supply products timely but also to be responsible for product cost. In addition, with conflict resolution of production planning, allocating the conflict resolution profits (CRP) among group members becomes an unsolved issue in GMfg. Hence, a new variant of conflict resolution model named GMfg production planning conflict resolution of CoPS (GPPCR&CoPS) is addressed in this paper. In the proposed approach, a Multi-level progress view (MLPV) model is used to detect the conflict of CoPS. Also, a bi-objective (schedule delay and total cost) is used and the CRP allocation among group members is considered. Moreover, a novel hybrid approach using dual-path searching ant colony optimization (DPS-ACO) and Shapley value is proposed to solve the bi-objective problem. Finally, a case study and comparison of performances with ant colony optimization (ACO) and NSGA-II are presented. Experimental results show the proposed method is more preferable in optimal solution searching.