A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem

This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the demands of customers are fulfilled by a fleet of fixed number of vehicles with various capacities and related costs. Moreover, the vehicles start at the depot and terminate at one of the customers. This problem is an important variant of the classical vehicle routing problem and can cover more practical situations in transportation and logistics. We propose a multistart adaptive memory programming metaheuristic with modified tabu search algorithm to solve this new vehicle routing problem. The algorithmic efficiency and effectiveness are experimentally evaluated on a set of generated instances.     

An integrated inbound logistics mode with intelligent scheduling of milk-run collection,drop and pull delivery and LNG vehicles

Although it provides a feasible inbound logistics solution for steady production and low inventory management, the Milk-run mode inevitably leads to a high transportation costs due to the features of small-batch and high-frequency delivery. In order to break through the defections of the existing inbound logistics mode, an integrated inbound logistics (IIL) mode with low-carbon and high efficiency is established. An intelligent scheduling method combines Milk-run collection with drop and pull delivery together. Moreover, the LNG vehicles are simultaneously used in the whole process. With AJ company’s auto-parts inbound logistics as a case, the IIL mode is formulated with a mixed integer mathematical model. The genetic algorithm coded with Matlab is used to find the optimal solution. The results show that when compared with the original Milk-run mode, the IIL mode brings massive reductions in driving mileage, wait time and waste gas emission. It can make significant benefits in both economic and social sense. Therefore, it is entirely reasonable for management of industries to believe that the IIL mode will be a feasible and promising alternative for inbound logistics.

     

考虑拥堵区域的多车型绿色车辆路径问题优化

针对降低物流配送过程中产生的碳排放问题,从绿色环保角度出发,提出一种考虑交通拥堵区域的多车型物流配送车辆的绿色车辆路径问题（GVRP）。首先分析不同类型车辆、不同拥堵状况对车辆行驶路线规划的影响,然后引入基于车辆行驶速度和载重的碳排放速率度量函数;其次以车辆管理使用费用和油耗碳排放成本最小作为优化目标,构建双目标绿色车辆路径模型;最后根据模型的特点设计一种融合模拟退火算法的混合差分进化算法对问题进行求解。通过实验仿真验证模型和算法可以有效规避拥堵区域,与只使用单一4 t车型配送相比,所提模型总成本降低了1.5%,油耗碳排放成本降低了4.3%;和以行驶距离最短为目标的模型相比,所提模型的总配送成本降低了8.1%。说明该模型提高物流企业的经济效益也促进了节能减排。同时所提算法与基本差分算法相比,总配送成本可以降低3%~6%;与遗传算法相比,优化效果更明显,总配送成本可以降低4%~11%,证明该算法更具有优越性。综上所提模型和算法可以为物流企业城市配送路径决策提供良好的参考依据。     

考虑时变速度的多车场绿色车辆路径模型及优化算法

针对多车场绿色车辆路径问题,根据顾客的坐标位置,采用K-means聚类方法将顾客分配给不同的车场;考虑时变速度和实时载重对车辆油耗和碳排放的影响,确定车辆油耗和碳排放的度量函数;在此基础上,以车辆油耗成本、碳排放成本、车辆使用成本、驾驶员工资以及时间窗惩罚成本之和最小化作为优化目标,构建多车场绿色车辆路径模型,并根据模...     

基于STM32嵌入式系统的无人物流车运输控制系统设计

摘要：传统的运输控制系统在控制无人物流车时,控制精度较大。为解决上述问题,利用STM32嵌入式系统设计了一种新的无人物流车运输控制系统,使用STM32F103VE作为核心处理器,在传感器模块中同时加入了多个传感器,并使用了MM440变频器负责变频操作,同时设计了电阻反馈模块电路、驱动模块电路、以太网通信模块电路,利用Visual C++软件实现无人物流车位置控制功能、无人物流车行驶方向保持程序、无人物流车跟踪功能。与传统控制系统进行实验对比,结果表明,基于STM32嵌入式系统的无人物流车运输控制系统控制精度明显高于传统系统。     

An integrated local search method for inventory and routing decisions

The present article studies an inventory routing model which integrates two important components of the supply chain: transportation logistics and inventory control. The distribution system examined consists of customers that face product demand at a deterministic and constant rate. Customer demand is satisfied by a fixed vehicle fleet located at the central depot. The aim of the problem is to determine the timing and size of the replenishment services together with the vehicle routes, so that the total transportation and inventory holding cost of the system is minimized. In methodological terms, we propose a solution approach applying two innovative local search operators for jointly dealing with the inventory and routing aspects of the examined problem, and Tabu Search for further reducing the transportation costs. The proposed algorithmic framework was tested on a set of new benchmark instances of various scales. It produced satisfactory results both in terms of effectiveness and robustness.     

物流车辆监控系统建模与仿真

物流的运输系统常常需要考虑交通堵塞、路径选择、能源危机、成本控制等各种复杂因素,对物流车辆调度人员提出了很高的要求,因此该文提出建立物流车辆监控仿真系统来模拟物流运输过程中对车辆实时动态的调度。该文结合GIS／GPS／GSM车辆监控系统的工作原理,设计了物流车辆监控系统模型,并构建了基于HLA（High Level Architecture）的物流车辆监控仿真系统框架。从应用的角度分析了HLA仿真开发过程,初步实现物流车辆监控模拟系统联邦,为车辆调度员进行教学和培训提供了一个模拟现实的环境,对物流车辆监控系统的实现起到了一定的辅助作用。     

Analysis of the maximum level policy in a production-distribution system

We consider a production-distribution system, where a facility produces one commodity which is distributed to a set of retailers by a fleet of vehicles. Each retailer defines a maximum level of the inventory. The production policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The overall cost is composed by fixed and variable production costs at the facility, inventory costs at both facility and retailers and routing costs. We study two different types of replenishment policies. The well-known order-up to level (OU) policy, where the quantity shipped to each retailer is such that the level of its inventory reaches the maximum level, and the maximum level (ML) policy, where the quantity shipped to each retailer is such that the inventory is not greater than the maximum level. We first show that when the transportation is outsourced, the problem with OU policy is NP-hard, whereas there exists a class of instances where the problem with ML policy can be solved in polynomial time. We also show the worst-case performance of the OU policy with respect to the more flexible ML policy. Then, we focus on the ML policy and the design of a hybrid heuristic. We also present an exact algorithm for the solution of the problem with one vehicle. Results of computational experiments carried out on small size instances show that the heuristic can produce high quality solutions in a very short amount of time. Results obtained on a large set of randomly generated problem instances are also shown, aimed at comparing the two policies.     

Incorporating lateral transfers of vehicles and inventory into an integrated replenishment and routing plan for a three-echelon supply chain

This paper focuses on developing an integrated replenishment and routing plan that takes into account lateral transfers of both vehicles and inventory for a three-echelon supply chain system including a single plant, multiple distribution centers and multiple retailers. A mixed integer programming model to the overall system is formulated first, and then an optimization-based heuristic consisting of three major components is proposed. The purpose of the first component is to assign retailers to distribution centers, and determine routing schedules for each distribution center. And the remaining two components are corresponding to two smaller optimization models – a variant of the classical transportation problem modeled for determining vehicle transfer between distribution centers, and a variant of the conventional minimum cost network flow problem modeled for determining inventory replenishment and transfer. Experimental results reveal that the proposed algorithm is rather computational effectiveness, and the pooling strategy that considers both vehicles and inventory transfers is a worthy option in designing supply chain operations.     

Multi-depot vehicle routing problem: a one-stage approach

This paper introduces multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD) which is one important and useful variant of the traditional multi-depot vehicle routing problem (MDVRP) in the supply chain management and transportation studies. After modeling the MDVRPFD as a binary programming problem, we propose two solution methodologies: two-stage and one-stage approaches. The two-stage approach decomposes the MDVRPFD into two independent subproblems, assignment and routing, and solves them separately. In contrast, the one-stage approach integrates the assignment with the routing where there are two kinds of routing methods-draft routing and detail routing. Experimental results show that our new one-stage algorithm outperforms the published methods. Note to Practitioners-This work is based on several consultancy work that we have done for transportation companies in Hong Kong. The multi-depot vehicle routing problem (MDVRP) is one of the core optimization problems in transportation, logistics, and supply chain management, which minimizes the total travel distance (the major factor of total transportation cost) among a number of given depots. However, in real practice, the MDVRP is not reliable because of the assumption that there have unlimited number of vehicles available in each depot. In this paper, we propose a new useful variant of the MDVRP, namely multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD), to model the practicable cases in applications. Two-stage and one-stage solution algorithms are also proposed. The industry participators can apply our new one-stage algorithm to solve the MDVRPFD directly and efficiently. Moreover, our one-stage solution framework allows users to smoothly add new specified constraints or variants.     

A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries

The vehicle routing problem with simultaneous pick-up and deliveries, which considers simultaneous distribution and collection of goods to/from customers, is an extension of the capacitated vehicle routing problem. There are various real cases, where fleet of vehicles originated in a depot serves customers with pick-up and deliveries from/to their locations. Increasing importance of reverse logistics activities make it necessary to determine efficient and effective vehicle routes for simultaneous pick-up and delivery activities. The vehicle routing problem with simultaneous pick-up and deliveries is also NP-hard as a capacitated vehicle routing problem and this study proposes a genetic algorithm based approach to this problem. Computational example is presented with parameter settings in order to illustrate the proposed approach. Moreover, performance of the proposed approach is evaluated by solving several test problems.     

An integrated inventory problem with transportation in a divergent supply chain under service level constraint

This study investigates an integrated inventory problem with transportation in a single-vendor and multi-buyer divergent supply chain. The vendor manufactures a product and delivers the product to the buyers located in different locations by a fleet of vehicles of identical capacity. The external demands per unit time on the buyers are independent and normally distributed. The lead time components of the buyers, excluding transportation time, can be reduced at an added crash cost. A model has been formulated to minimize the total expected cost of the system associated with the production, inventory, transportation and lead time reduction to find the optimal production, inventory and routing decisions while satisfying the service level constraint of the buyers. We propose a coordinated two-phase iterative approach to solve the model, which has been illustrated through a numerical example.     

强化学习在车辆路径问题中的研究综述

车辆路径问题是物流运输优化中的核心问题,目的是在满足顾客需求下得到一条最低成本的车辆路径规划。但随着物流运输规模的不断增大,车辆路径问题求解难度增加,并且对实时性要求也不断提高,已有的常规算法不再适应实际要求。近年来,基于强化学习算法开始成为求解车辆路径问题的重要方法,在简要回顾常规方法求解车辆路径问题的基础上,重点总结基于强化学习求解车辆路径问题的算法,并将算法按照基于动态规划、基于价值、基于策略的方式进行了分类;最后对该问题未来的研究进行了展望。     

多物流中心共同配送的车辆路径问题研究

研究多物流中心共同配送的车辆路径问题。首先考虑客户服务关系变化与客户需求的异质性情况,设计一种共享客户需求、配送车辆与物流中心的共享物流模式;再综合考虑车辆容量、油耗、碳排放、最长行驶时间、客户需求量与服务时间等因素,以总成本最小为目标构建多物流中心共同配送的车辆路径规划模型,并设计一种改进蚁群算法进行求解;最后采用多类型算例进行仿真实验,结果表明共享物流模式能有效避免交叉配送与迂回运输等不合理现象,降低物流成本,缩短车辆行驶距离,减少车辆碳排放,促进物流与环境的和谐发展。     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制,在满足商用要求的前提下,纯电动汽车用于长途运输服务在短期内难以实现。不过,城市物流因其配送区域较小、货物的批量较小、批次较多的特点,可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响,建立了考虑车辆负载对实时能耗影响的配送模型,以及时满足客户的服务时间要求。并以城市A为例,设计了蚁群算法对模型进行求解,为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后,通过与使用燃油车辆运营相比较,分析未来纯电动汽车在城市配送物流中的可行性。     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制，在满足商用要求的前提下，纯电动汽车用于长途运输服务在短期内难以实现。不过，城市物流因其配送区域较小、货物的批量较小、批次较多的特点，可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响，建立了考虑车辆负载对实时能耗影响的配送模型，以及时满足客户的服务时间要求。并以城市A为例，设计了蚁群算法对模型进行求解，为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后，通过与使用燃油车辆运营相比较，分析未来纯电动汽车在城市配送物流中的可行性。     

A planning model and solution algorithm for multi-trip split-delivery vehicle routing and scheduling problems with time windows

This study proposes a daily vehicle routing model for minimizing the total cost of replenishing inventory within a supply chain. The first major contribution of this research is to allow multiple use of vehicles in a split delivery vehicle routing problem with time windows (SDVRPTW), which is more realistic for various real-life applications. The multi-trip SDVRPTW (MTSDVRPTW) is formulated using the time–space network technique, which provides greater flexibility for formulating the complicated interactions between vehicles and products when multi-trip, split delivery, and delivery time windows are simultaneously considered. The resulting formulation of the MTSDVRPTW can be categorized as an integer multi-commodity network flow problem with side constraints. A two-step solution algorithm is proposed to solve this NP-hard problem, which is the second major contribution of this research. Finally, a real-world scale numerical example is performed to demonstrate and to test the methodology. The results indicate that these vehicle routing problems can be solved effectively and efficiently and that the proposed methodology has great potential for inventory replenishment scheduling where split deliveries and multiple trips for a single vehicle are allowed and time window constraints are imposed.     

The green vehicle routing problem: A heuristic based exact solution approach

This paper develops a simulated annealing heuristic based exact solution approach to solve the green vehicle routing problem (G-VRP) which extends the classical vehicle routing problem by considering a limited driving range of vehicles in conjunction with limited refueling infrastructure. The problem particularly arises for companies and agencies that employ a fleet of alternative energy powered vehicles on transportation systems for urban areas or for goods distribution. Exact algorithm is based on the branch-and-cut algorithm which combines several valid inequalities derived from the literature to improve lower bounds and introduces a heuristic algorithm based on simulated annealing to obtain upper bounds. Solution approach is evaluated in terms of the number of test instances solved to optimality, bound quality and computation time to reach the best solution of the various test problems. Computational results show that 22 of 40 instances with 20 customers can be solved optimally within reasonable computation time.     

基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题,提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题,并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划,而后在每个时域末,综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明,相较于传统的静态收运方案,动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时,显著降低由于清运不及时造成的环境二次污染的风险。     

考虑载重影响耗电率的电动车车辆路径问题

随着新能源和绿色物流等政策的出台,电动车逐渐成为物流配送的主要运输工具.考虑到电动车的电池容量、充电时间、耗电率和充电站位置等因素,研究载重影响耗电率的电动车车辆路径问题,建立以总成本最小化为目标的混合整数规划模型.结合禁忌搜索算法的思想,设计改进的自适应大邻域搜索算法对其求解,在该算法中,开发多种基于模型特性的破坏算子和修复算子以提高求解效率.通过算例求解验证模型和算法的有效性,为物流企业电动车配送方案的规划提供一定的决策依据.