An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”
An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”
复制标题
DOI：
10.1109/access.2020.3010176
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
J. Pasha;M. Dulebenets;M. Kavoosi;Olumide F. Abioye;Hui Wang;W. Guo
中科院分区：
文献类型：
--
作者：
J. Pasha;M. Dulebenets;M. Kavoosi;Olumide F. Abioye;Hui Wang;W. Guo
“箱中工厂”概念包括在集装箱中组装生产模块(即工厂)，并将集装箱运输到不同的客户地点。在紧急情况下，当对产品有紧急需求时(例如，新冠肺炎大流行)，这种概念可能非常有效。“盒装工厂”的规划问题可以分为两个子问题。第一个子问题涉及将原材料分配给供应商、分配子装配分解、分配子装配模块给制造商以及将任务分配给制造商。第二个子问题集中在供应商和制造商之间的子装配模块的运输，方法是将车辆分配到地点，确定供应商、制造商和客户的访问顺序，并在运输网络中选择适当的路线。第二个子问题类似于车辆路径问题，本文通过建立一个优化模型和求解算法来解决第二个子问题，以优化“箱式工厂”供应链。提出了一种混合整数线性规划模型，目标是最小化“箱式工厂”供应链的总成本。CPLEX算法用于求解模型的全局最优解，而进化算法、变邻域搜索算法、禁忌搜索算法和模拟退火法等四种元启发式算法用于大规模问题实例的求解。以“盒子里的工厂”为例进行的一组数值实验表明，进化算法的性能优于为该模型开发的其他元启发式算法。在数值实验中也概述了一些管理见解。
The “factory-in-a-box” concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The “factory-in-a-box” planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of sub-assembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the “factory-in-a-box” supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the “factory-in-a-box” supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of “factory-in-a-box”, demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well.