基于多目标多约束的生产制造业的计划排产问题研究

Research on Planning and Scheduling of Equipment Manufacturing Industry Based on Multi-objective and Multi-constraint

保证交期是生产制造业的生产制造的核心竞争点之一,在这种情况下,制造系统都要求最小化生产完成时间。最小化生产完成时间基本上涉及两个目标,即最小化机器闲置时间和最小化订单提前率/延迟率。必须通过考虑所有约束条件(例如优先级、可用机器、机器转换、机器设置、机器容量、大量库存等)来实现这两个目标的最小化。这需要通过高级计划和排程(APS)来实现。该文提出了一种具有迭代搜索的遗传(GA)算法来找到APS问题的最优解,应用于解决生产制造业多目标多约束的计划排产过程问题。该算法在满足所有约束的情况下最小化机器闲置时间和订单提前率/延迟率,从而最小化生产完成时间,确保产品交期。该文使用Python开发了APS混合整数编程模型,该模型使用迭代搜索技术来提高系统效率并在短时间内产生最佳解决方案。

Guaranteed delivery time is one of the core competition points in the equipment manufacturing industry. In this case,the manufacturing system requires the minimum makespan time. Minimizing makespan time basically involves two goals,namely,minimizing machine idle time and order forward/delay rate. These two goals must be minimized by considering all constraints(such as priority,available machines,machine conversion,machine settings,machine capacity,large inventory,etc.). This needs to be achieved through advanced planning and scheduling(APS). This paper proposes a genetic(GA) algorithm with iterative search to find the optimal solution of the APS problem,which is used to solve the multi-objective and multi-constrained planning and scheduling process of the equipment manufacturing industry. The algorithm minimizes machine idle time and order lead/delay rate while meeting all constraints,thereby minimizing production completion time and ensuring product delivery. This article uses Python to develop an APS mixed integer programming model,which uses iterative search technology to improve system efficiency and produce the best solution in a short time.