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Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance
Computers & Operations Research ( IF 4.6 ) Pub Date : 2021-01-05 , DOI: 10.1016/j.cor.2021.105211
Houming Fan , Yueguang Zhang , Panjun Tian , Yingchun Lv , Hao Fan

Reducing distribution costs is one of the effective ways for logistics enterprises to improve their core competitiveness. Aiming at the multi-depot vehicle routing problem under the time-varying road network, this paper proposes an integer programming model with the minimum total costs by comprehensively considering the fixed costs of vehicles, penalty costs on earliness and tardiness, fuel costs and the effects of vehicle speed, load and road gradient on fuel consumption. A hybrid genetic algorithm with variable neighborhood search is developed to solve the problem. In the algorithm, the temporal-spatial distance is introduced to cluster the customers to generate an initial population to improve the quality of the initial solution. Adaptive neighborhood search times strategy and simulated annealing inferior solution acceptance mechanism are used to balance the diversification and exploitation in the algorithm iteration process. Numerical results show that the model and algorithm we proposed are rather effective. The research results not only deepen and expand the vehicle routing problem (VRP) theory research, but also provide a scientific and reasonable method for logistics enterprises to make the vehicle scheduling plan.



中文翻译:

考虑时空距离的带时间窗的时变多仓库绿色车辆路径问题

降低分销成本是物流企业提高核心竞争力的有效途径之一。针对时变路网下的多点车辆路径问题,综合考虑车辆的固定成本,提早,拖尾的罚款成本,燃油成本及其影响,提出了一种总费用最小的整数规划模型。速度,负载和道路坡度对燃油消耗的影响。为了解决该问题,开发了一种具有可变邻域搜索的混合遗传算法。在该算法中,引入了时空距离以对客户进行聚类以生成初始种群,从而提高初始解决方案的质量。在算法迭代过程中,采用了自适应邻域搜索次数策略和模拟退火次优接受机制来平衡多样性和开发性。数值结果表明,本文提出的模型和算法是有效的。研究结果不仅深化和扩大了车辆路径问题理论研究,而且为物流企业制定车辆调度计划提供了科学合理的方法。

更新日期:2021-01-18
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