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首页> 外文期刊>Applied Soft Computing >A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment
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A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

机译：一种基于学习的新颖方法，可解决考虑直接装运的跨入库中的新集成位置路由和调度问题

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摘要

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems' modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms. (C) 2015 Elsevier B.V. All rights reserved.
机译：供应链管理中最重要的问题之一是分配系统的设计，该系统可以减少运输成本并在最短的时间内满足客户的需求。近年来，跨码头（CD）中心被认为是减少运输和库存成本的地方。同时，忽视中心的最佳位置以及车辆的最佳路线和调度，将优化过程误导为局部最优。因此，在本研究中，对交叉对接系统中的集成车辆路径和调度问题进行了建模。在这种新模型中，还包括从制造商到客户的直接发货。此外，将车辆以较低的成本分配给跨坞门。接下来，为解决该模型，开发了一种新颖的基于机器学习的启发式方法（MLBM），其中，客户，制造商和交叉配送中心的位置通过双向聚类的方式进行了分组。实际上，MLBM是基于过滤器的学习方法，它包括三个阶段，包括通过改进的双聚类方法进行客户聚类，子问题的建模以及求解整个模型。此外，为解决交叉对接系统中车辆的调度问题，本文提出了精确的解决方案以及遗传算法。 GA还适用于无法使用精确方法的大规模问题。此外，提出的遗传算法的参数通过田口方法进行调整。最后，为了验证提出的模型，根据基础模型中新引入的假设，从文献中选择并修改了几个基准问题。实现了不同的统计分析方法以评估所提出算法的性能。 （C）2015 Elsevier B.V.保留所有权利。

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