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Single-item production-delivery scheduling problem with stage-dependen inventory costs and due-date considerations

机译:具有阶段依赖的库存成本和到期日考虑因素的单项生产-交付计划问题

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摘要

This paper studies an integrated scheduling problem for a single-item, make-to-order supply chain system consisting of one supplier, one capacitated transporter and one customer. Specifically, we assume the existence in the production stage of an intermediate inventory that works as a buffer to balance the production rate and the transportation speed. Jobs are first processed on a single machine in the production stage, and then delivered to the pre-specified customer by a capacitated vehicle in the delivery stage. Each job has a due date specified by the customer, and must be delivered to the customer before its due date. Moreover, it is assumed that a job that is finished before its departure date or arrives at the customer before its due date will incur a stage-dependent corresponding inventory cost (WIP inventory, finished-good inventory or customer inventory cost). The objective is to find a coordinated production and delivery schedule such that the sum of setup, delivery and inventory costs is minimised. We formulate the problem as a nonlinear model in a general way and provide some properties. We then derive a precise instance from the general model and develop a heuristic algorithm for solving this precise instance. In order to evaluate the performance of the heuristic algorithm, we propose a simple branch-and-bound (B&B) approach for small-size problems, and a lower bound based on the Lagrangian relaxation method for large-size problems. Computational experiments show that the heuristic algorithm performs well on randomly generated problems.
机译:本文研究了一个单项目,按订单生产的供应链系统的集成调度问题,该系统由一个供应商,一个运输能力强大的运输者和一个客户组成。具体来说,我们假设在生产阶段存在中间库存,该中间库存可作为缓冲以平衡生产率和运输速度。作业在生产阶段首先在单台机器上进行处理,然后在交付阶段由配备能力的车辆交付给预先指定的客户。每个作业都有客户指定的截止日期,并且必须在截止日期之前交付给客户。此外,假设在离开日期之前完成或在到期日之前到达客户的作业将产生阶段依赖的相应库存成本(WIP库存,成品库存或客户库存成本)。目的是找到协调的生产和交货时间表,以使设置,交货和库存成本之和最小。我们以一般方式将问题表示为非线性模型,并提供一些属性。然后,我们从通用模型中导出一个精确实例,并开发一种启发式算法来解决该精确实例。为了评估启发式算法的性能,我们针对小问题提出了一种简单的分支定界(B&B)方法,针对大问题提出了基于拉格朗日松弛法的下界。计算实验表明,启发式算法在随机产生的问题上表现良好。

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