In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and ...In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.展开更多
针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRP...针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。展开更多
针对多仓库异质车队带时间窗的车辆路径问题(Multi-Depot Heterogeneous Fleet Vehicle Routing Problem with Time Windows,MDHFVRPTW),以车辆数费用和物流成本最小为目标,综合客户需求、时间约束等因素构建数学模型,并提出改进智能水...针对多仓库异质车队带时间窗的车辆路径问题(Multi-Depot Heterogeneous Fleet Vehicle Routing Problem with Time Windows,MDHFVRPTW),以车辆数费用和物流成本最小为目标,综合客户需求、时间约束等因素构建数学模型,并提出改进智能水滴算法(Improved Intelligent Waterdrop Algorithm,IIWD)求解。引入大邻域搜索方法及模拟退火可接受概率准则,重新定义了算法的水滴路径,有效优化智能水滴算法的局部搜索能力。Cordeau标准测试算例和实际算例的求解结果显示,算法在寻优能力上较其他算法更强,求解时间也有明显提升,充分验证了算法的有效性与可行性。展开更多
基金supported by the Science Challenge Project(TZ2018007)the National Natural Science Foundation of China(71671009+2 种基金 61871013 61573041 61573043)
文摘In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.
文摘针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。
文摘针对多仓库异质车队带时间窗的车辆路径问题(Multi-Depot Heterogeneous Fleet Vehicle Routing Problem with Time Windows,MDHFVRPTW),以车辆数费用和物流成本最小为目标,综合客户需求、时间约束等因素构建数学模型,并提出改进智能水滴算法(Improved Intelligent Waterdrop Algorithm,IIWD)求解。引入大邻域搜索方法及模拟退火可接受概率准则,重新定义了算法的水滴路径,有效优化智能水滴算法的局部搜索能力。Cordeau标准测试算例和实际算例的求解结果显示,算法在寻优能力上较其他算法更强,求解时间也有明显提升,充分验证了算法的有效性与可行性。
