An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company
An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company
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DOI：
10.1016/j.ijpe.2019.05.017
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发表时间：
2020-01-01
影响因子：
12
通讯作者：
Rekik, Y.
中科院分区：
文献类型：
--
作者：
Mohammadi, S.;Al-e-Hashem, S. M. J. Mirzapour;Rekik, Y.
近年来，许多现代工业都采用了生产和分配决策的联合调度。这种协调对于按订单生产（MTO）业务是必要的，在这种业务中，以最低的总成本实现及时交付并满足高度定制的要求具有挑战性。为了应对这些挑战，除了生产和分销计划之间更紧密的联系之外，还需要实用的生产配置和交付方法。因此，在本研究中，我们解决了具有时间窗口的集成生产调度-车辆路径问题，其中假设生产是在灵活的作业车间系统中进行的。我们的框架被建模为一个新颖的双目标混合整数问题，其中第一个目标函数旨在最小化生产和分配调度成本的总和，第二个目标函数试图最小化交货提前和延迟的加权和。为了实际验证我们的框架的应用，我们考虑了一家生产定制商品的家具制造公司的案例研究，并得出了实验数据。基于真实数据，首先通过 epsilon 约束方法对模型进行优化求解，然后开发混合粒子群优化 (HPSO) 算法，以在合理的时间内解决中型和大型问题的模型。我们通过将所提出的模型的结果与单独方法的结果进行比较来讨论集成的好处。结果表明，公司能够在成本和客户关注点之间建立适当的理性平衡，并且可以利用集成策略作为杠杆来提高客户满意度，而不会导致系统总运营成本显着增加。
Recently, many modern industries have adopted joint scheduling of production and distribution decisions. Such coordination is necessary in make-to-order (MTO) businesses, where it is challenging to achieve timely delivery at minimum total cost and meet the requirements for high customization. To deal with these challenges, a practical production configuration and delivery method is required, in addition to a closer link between production and distribution schedules. Hence, in this study, we address an integrated production scheduling-vehicle routing problem with a time window, where it is assumed that production is performed in a flexible job-shop system. Our framework is modeled as a novel bi-objective mixed integer problem, in which the first objective function aims to minimize a sum of the production and distribution scheduling costs, and the second objective function tries to minimize a weighted sum of delivery earliness and tardiness. To practically validate the application of our framework, a case study from a furniture manufacturing company producing customized goods is considered, and experimental data are derived. Based on the real data, the model is first optimally solved by an epsilon-constraint method, and then a Hybrid Particle Swarm Optimization (HPSO) algorithm is developed to solve the model for medium- and large-sized problems in a reasonable time. We discuss the benefits of integration by comparing the results of the proposed model with that of the separate approach. The results show that the company can establish a proper rational balance between cost and customer concerns, and they can use the integration policy as a lever to improve customer satisfaction without the system experiencing a significant increase in total operational cost.