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Parallel Machine Scheduling with Batch Delivery to Two Customers

机译：批量调度到两个客户的并行机器调度

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摘要

In some make-to-order supply chains, the manufacturer needs to process and deliver products for customers at different locations. To coordinate production and distribution operations at the detailed scheduling level, we study a parallel machine scheduling model with batch delivery to two customers by vehicle routing method. In this model, the supply chain consists of a processing facility with m parallel machines and two customers. A set of jobs containing n(1) jobs from customer 1 and n(2) jobs from customer 2 are first processed in the processing facility and then delivered to the customers directly without intermediate inventory. The problem is to find a joint schedule of production and distribution such that the tradeoff between maximum arrival time of the jobs and total distribution cost is minimized. The distribution cost of a delivery shipment consists of a fixed charge and a variable cost proportional to the total distance of the route taken by the shipment. We provide polynomial time heuristics with worst-case performance analysis for the problem. If m = 2 and (n(1) - b)(n(2) - b) < 0, we propose a heuristic with worst-case ratio bound of 3/2, where.. is the capacity of the delivery shipment. Otherwise, the worst-case ratio bound of the heuristic we propose is 2-2/(m + 1).
机译：在某些按订单生产的供应链中，制造商需要为不同位置的客户加工和交付产品。为了在详细的调度级别协调生产和分销操作，我们研究了一种并行的机器调度模型，该模型通过车辆调度方法向两个客户批量交付。在此模型中，供应链包括一个具有m台平行机和两个客户的加工设施。首先在处理设备中处理包含来自客户1的n（1）个作业和来自客户2的n（2）个作业的一组作业，然后将其直接交付给客户，而无需中间库存。问题是找到生产和分配的联合时间表，以使工作的最大到达时间与总分配成本之间的权衡最小化。交货货物的分配成本包括固定费用和与货物所走路线的总距离成比例的可变成本。我们为多项式时间启发式算法提供了针对该问题的最坏情况性能分析。如果m = 2且（n（1）-b）（n（2）-b）<0，则我们提出一种启发式方法，其最坏情况比率范围为3/2，其中..是交付货物的容量。否则，我们建议的启发式方法的最坏情况比率范围为2-2 /（m + 1）。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第16期|247356.1-247356.6|共6页
  • 作者

    Zhong Xueling; Jiang Dakui;

  • 作者单位

    Guangdong Univ Finance, Dept Internet Finance & Informat Engn, Guangzhou 510520, Guangdong, Peoples R China.;

    Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China.;

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