{"title":"用元启发式方法安排交叉装卸设施中移动货物的车辆路线","authors":"S. Gnanapragasam, W. Daundasekera","doi":"10.4038/jnsfsr.v52i2.11576","DOIUrl":null,"url":null,"abstract":"Cross-Docking (CD) is a modern distribution strategy in a supply chain. The optimal scheduling of vehicle routing, known as the Vehicle Routing Problem (VRP), is one of the influential factors of the efficiency of a supply chain. In recent years, researchers and business consultants in different organizations have been interested in integrating the VRP with CD (VRPCD). Since VRPCD is a NP-hard problem, heuristic or meta-heuristic methods are always recommended to solve large-scale VRPCD. The Genetic Algorithm (GA) is a population based meta-heuristic algorithm and also, it is based on the principles of genetic and natural selections. The GA is capable of finding near optimal solutions to large-scale optimization problems which are extremely difficult to solve using traditional optimization algorithms. Therefore, in this study, a meta-heuristic approach based on the GA is proposed to solve the vehicle routing problem with moving shipments at the cross-docking facility (VRPCD&MS). The data are extracted from benchmark instances in the literature. The optimum solutions obtained to small-scale instances by the GA are compared with the exact solutions obtained by the Branch and Bound (BB) algorithm, which is a traditional algorithm to solve problems of this nature. The GA and BB algorithms are respectively coded in MATLAB and LINGO. The results reveal that the relative difference between the exact solution and the near–optimal solution is below 5%. Therefore, it can be concluded that the proposed GA is a better alternative method, considering its overall performance, to solve the VRPCD&MS models. Moreover, since the computational time is low, the proposed GA can be used to schedule the vehicles to the routes of VRPCD&MS at the last moment prior to the start of the time horizon. 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Since VRPCD is a NP-hard problem, heuristic or meta-heuristic methods are always recommended to solve large-scale VRPCD. The Genetic Algorithm (GA) is a population based meta-heuristic algorithm and also, it is based on the principles of genetic and natural selections. The GA is capable of finding near optimal solutions to large-scale optimization problems which are extremely difficult to solve using traditional optimization algorithms. Therefore, in this study, a meta-heuristic approach based on the GA is proposed to solve the vehicle routing problem with moving shipments at the cross-docking facility (VRPCD&MS). The data are extracted from benchmark instances in the literature. The optimum solutions obtained to small-scale instances by the GA are compared with the exact solutions obtained by the Branch and Bound (BB) algorithm, which is a traditional algorithm to solve problems of this nature. The GA and BB algorithms are respectively coded in MATLAB and LINGO. 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引用次数: 0
摘要
交叉对接(CD)是供应链中的一种现代配送策略。车辆路由的优化调度,即车辆路由问题(VRP),是供应链效率的影响因素之一。近年来,不同组织的研究人员和商业顾问都对将 VRP 与 CD(VRPCD)相结合产生了浓厚的兴趣。由于 VRPCD 是一个 NP 难问题,因此总是推荐采用启发式或元启发式方法来解决大规模 VRPCD 问题。遗传算法(GA)是一种基于种群的元启发式算法,它也是基于遗传和自然选择的原理。对于传统优化算法极难解决的大规模优化问题,遗传算法能够找到接近最优的解决方案。因此,本研究提出了一种基于遗传算法的元启发式方法,用于解决交叉对接设施中移动货物的车辆路由问题(VRPCD&MS)。数据来自文献中的基准实例。将利用 GA 获得的小规模实例最优解与利用分支与边界(BB)算法获得的精确解进行了比较,后者是解决此类问题的传统算法。GA 算法和 BB 算法分别用 MATLAB 和 LINGO 编码。结果表明,精确解与近优解之间的相对差异低于 5%。因此,考虑到其整体性能,可以认为所提出的 GA 是解决 VRPCD&MS 模型的一种更好的替代方法。此外,由于计算时间较短,建议的 GA 可用于在时间跨度开始前的最后时刻将车辆调度到 VRPCD&MS 的路线上。
Meta-heuristic method to schedule vehicle routing with moving shipments at the cross-docking facility
Cross-Docking (CD) is a modern distribution strategy in a supply chain. The optimal scheduling of vehicle routing, known as the Vehicle Routing Problem (VRP), is one of the influential factors of the efficiency of a supply chain. In recent years, researchers and business consultants in different organizations have been interested in integrating the VRP with CD (VRPCD). Since VRPCD is a NP-hard problem, heuristic or meta-heuristic methods are always recommended to solve large-scale VRPCD. The Genetic Algorithm (GA) is a population based meta-heuristic algorithm and also, it is based on the principles of genetic and natural selections. The GA is capable of finding near optimal solutions to large-scale optimization problems which are extremely difficult to solve using traditional optimization algorithms. Therefore, in this study, a meta-heuristic approach based on the GA is proposed to solve the vehicle routing problem with moving shipments at the cross-docking facility (VRPCD&MS). The data are extracted from benchmark instances in the literature. The optimum solutions obtained to small-scale instances by the GA are compared with the exact solutions obtained by the Branch and Bound (BB) algorithm, which is a traditional algorithm to solve problems of this nature. The GA and BB algorithms are respectively coded in MATLAB and LINGO. The results reveal that the relative difference between the exact solution and the near–optimal solution is below 5%. Therefore, it can be concluded that the proposed GA is a better alternative method, considering its overall performance, to solve the VRPCD&MS models. Moreover, since the computational time is low, the proposed GA can be used to schedule the vehicles to the routes of VRPCD&MS at the last moment prior to the start of the time horizon.
期刊介绍:
The Journal of National Science Foundation of Sri Lanka (JNSF) publishes the results of research in Science and Technology. The journal is released four times a year, in March, June, September and December. This journal contains Research Articles, Reviews, Research Communications and Correspondences.
Manuscripts submitted to the journal are accepted on the understanding that they will be reviewed prior to acceptance and that they have not been submitted for publication elsewhere.