一种改进的差分进化算法求解同时送货和取货的车辆路径问题

Erbao Cao, M. Lai
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引用次数: 19

摘要

同时送货和取货的车辆路径问题(VRP- sdp)是传统车辆路径问题(VRP)的一种变体,其中客户端需要同时送货和取货。在车辆服务开始时,交货从一个仓库提供,而在服务结束时,取货被带到同一个仓库。该问题的一个重要特征是车辆在任何给定路线上的载荷是运送载荷和取货载荷的混合,同时在任何路线上车辆都不能违反某些约束,例如车辆容量约束和行驶距离约束。本文从物流与逆向物流(双向物流)相结合的战略角度提出了VRP-SDP。详细构造了VRP-SDP问题的通用混合整数规划数学模型,该模型可以通过设置不同的参数转化为其他经典的车辆路径问题。提出了一种改进的差分进化算法(IDE)。在运行过程中,首先采用新颖的十进制编码构造初始种群,然后采用改进的微分进化算子作为主要优化方案,如在突变操作中采用基于整数阶准则的实数编码方法,设计惩罚函数来处理约束,在交叉操作中采用迭代自更新交叉概率。通过计算机仿真,将所提方法与遗传算法进行性能比较,数值结果表明所提方法的性能优于遗传算法。
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An Improved Differential Evolution Algorithm for the Vehicle Routing Problem With Simultaneous Delivery and Pick-up Service
The vehicle routing problem with simultaneous delivery and pick-up (VRP-SDP) is a variant of the classical vehicle routing problem (VRP) where clients require simultaneous delivery and pick-up. Deliveries are supplied from a single depot at the beginning of the vehicle's service, while pick-up loads are taken to the same depot at the conclusion of the service. One important characteristic of this problem is that a vehicle's load in any given route is a mix of delivery and pick-up loads, at the same time in any route the vehicle can not violate some constraints for example the vehicle capacity and traveling distance constraints. In this paper, VRP-SDP is presented from the point of strategic view that combined the logistics and reverse logistics (bidirectional logistics). We constructed a universal mixed integer programming mathematic model of VRP-SDP in detail, it can transform into other classical vehicle routing problems by setting different parameters. An improved differential evolution algorithm (IDE) is proposed. In operation process, we firstly adopted the novel decimal coding to construct initial population, and then some improved differential evolution operators were adopted as the main optimizing scheme, such as adopted a real number coding method based on integer order criterion in mutation operation, a punishment function was designed to dispose constraints, and in crossover operation the crossover probability was self-updated with iteration. The computer simulations are used to compare the performance of the proposed method with genetic algorithm (GA), numerical results show that the performance of the proposed method is better than GA.
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