A double-layered optimisation approach for the integrated due date assignment and scheduling problem

A double-layered optimisation approach for the integrated due date assignment and scheduling problem
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综合到期日分配和调度问题的双层优化方法

DOI:
10.1080/00207543.2011.571440
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发表时间:
2012-01
影响因子:
9.2
通讯作者:
张瑞
张瑞
中科院分区:
工程技术2区
文献类型:
--
作者:
张瑞

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随着市场竞争的日益激烈,现代按订单生产的企业同时面临交货期报价和生产调度问题。一方面,为了吸引顾客,公司需要报一个较短的交货期;另一方面,一旦承诺了交货期,公司必须不遗余力地在不迟于这一日期交货。如果交货期分配和车间调度由两个系统分别处理,则总体性能不太可能令人满意,因为这两个任务实际上是相互关联的(例如,尽管对即将到来的客户有吸引力,但更严格的交货期设置将增加迟到的机会)。因此,我们考虑的问题,集成到一个优化模型的交货期分配和车间调度。提出了一种求解该问题的双层启发式优化算法。在执行粗粒度优化的上层遗传算法中,使用贝叶斯网络来学习最优交货期值的分布。作为第二层算法,参数摄动方法被应用于更细粒度的邻域搜索。计算实验证明了该算法的有效性和效率。
As the market competition becomes fiercer, contemporary make-to-order firms are confronted with both due date quotation and production scheduling problems at the same time. On the one hand, in order to attract customers, the firm needs to quote a short lead time; on the other hand, once a due date has been promised, the firm must spare no effort to deliver the goods no later than this date. If due date assignment and shop scheduling are processed separately by two systems, the overall performance is unlikely to be satisfactory because the two tasks are actually interrelated (e.g. a tighter due date setting will increase the chances of tardiness despite its appeal for the incoming customer). Therefore, we consider the problem by integrating due date assignment and shop scheduling into one optimisation model. A double-layered heuristic optimisation algorithm is presented for solving this problem. In the upper-layer genetic algorithm which performs coarse-granularity optimisation, Bayesian networks are used to learn the distribution of optimal due date values. As the second-layer algorithm, a parameter perturbation method is applied for a finer-granularity neighbourhood search. Computational experiments prove the efficacy and efficiency of the proposed algorithm.
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