IEEE Trans Cybern. 2016 Mar;46(3):582-94. doi: 10.1109/TCYB.2015.2409837. Epub 2015 Mar 18.
This paper investigates a practical variant of the vehicle routing problem (VRP), called VRP with simultaneous delivery and pickup and time windows (VRPSDPTW), in the logistics industry. VRPSDPTW is an important logistics problem in closed-loop supply chain network optimization. VRPSDPTW exhibits multiobjective properties in real-world applications. In this paper, a general multiobjective VRPSDPTW (MO-VRPSDPTW) with five objectives is first defined, and then a set of MO-VRPSDPTW instances based on data from the real-world are introduced. These instances represent more realistic multiobjective nature and more challenging MO-VRPSDPTW cases. Finally, two algorithms, multiobjective local search (MOLS) and multiobjective memetic algorithm (MOMA), are designed, implemented and compared for solving MO-VRPSDPTW. The simulation results on the proposed real-world instances and traditional instances show that MOLS outperforms MOMA in most of instances. However, the superiority of MOLS over MOMA in real-world instances is not so obvious as in traditional instances.
本文研究了物流行业中车辆路径问题（VRP）的一种实用变体，称为带同时交付和取货及时间窗的车辆路径问题（VRPSDPTW）。VRPSDPTW 是闭环供应链网络优化中的一个重要物流问题。在实际应用中，VRPSDPTW 具有多目标属性。本文首先定义了一个通用的多目标 VRPSDPTW（MO-VRPSDPTW），具有五个目标，然后引入了一组基于实际数据的 MO-VRPSDPTW 实例。这些实例代表了更现实的多目标性质和更具挑战性的 MO-VRPSDPTW 案例。最后，设计、实现并比较了两种算法，即多目标局部搜索（MOLS）和多目标遗传算法（MOMA），用于解决 MO-VRPSDPTW。对所提出的实际实例和传统实例的仿真结果表明，在大多数实例中，MOLS 优于 MOMA。然而，MOLS 在实际实例中相对于 MOMA 的优势并不像在传统实例中那么明显。
