多车场带时间窗车辆路径问题的变邻域搜索算法

多车场带时间窗车辆路径问题是车辆路径问题集合中的一个极为复杂、且仍未得到较好解决的问题。针对这一问题,建立了它的整数规划数学模型,提出了一种改进型变邻域搜索算法。该算法在初始解的构造阶段采用聚类方法完成客户的分配,运用混合算子进行局部搜索,通过后优化过程增强寻优效果,引入模拟退火模型对新解的接受进行控制。最后,在Cordeau提出的标准用例上对改进型变邻域算法进行了实验,实验结果更新了大部分目前该问题的最优解,并在算法的稳定性和求解时间上体现出一定优势。实验表明,该算法是一种求解多车场带时间窗车辆路径问题的有效方法。     

动态需求下车辆路径问题的周期性优化模型及求解

针对客户点不断更新的动态需求车辆路径问题,依据滚动时域对配送中心工作时间进行划分,提出基于延迟服务的周期性客户点实时重置策略,策略中延迟服务机制能结合车辆启动延迟系数对照当前时域的时间进行检验,满足所有客户点的服务需求,保证车辆满足中心时间窗约束。设计多阶段求解的混合变邻域人工蜂群算法对各时间片内子问题进行连续迭代优化,算法中子路径动态转变的设计能较好平衡原有客户点和新客户点对路径更新和车辆实时信息匹配的要求。算例验证及对比分析表明本文策略和算法在求解动态问题时的有效性和可行性。     

低碳环境下的车辆路径问题及禁忌搜索算法研究

基于节能减排的新视角,本文研究了低碳环境下由第三方提供运输服务的车辆路径问题,在安排车辆路径时,同时考虑了能耗、碳排放和租车费用,而这些费用不仅与距离有关,也与客户点的需求量和车辆速度有关。提出了考虑车辆运量和速度的能耗计算方法,建立了非满载运输方式下的低碳路径模型——LCRP。设计了基于路径划分的禁忌搜索算法RS-TS对问题进行求解,该算法引入了一种新颖的路径编码与解码算法WSS,采用了三种邻域搜索方法。通过基准测试实例验证了算法能有效地找到满意解,并揭示了距离、能耗、行驶时间等参数之间的关系,实验分析表明采用低碳路径安排更加经济环保且选择中低的交通速度更有利于节约能耗和降低碳排放。     

顾客时间窗变化的多车场车辆调度干扰管理模型研究

为解决物流配送过程中某顾客时间窗变化而导致的物流配送难以按照原计划实现的难题,运用干扰管理方法,针对多车场带时间窗的物流配送车辆路径问题,提出包含顾客配送时间总偏离度、配送总成本、新路径方案与初始路径计划偏差量、最长行驶时间违反总量4个因素的扰动程度度量方法;以顾客时间窗变化这类干扰事件发生时的问题状态为基础,以系统整体扰动最小化为目标,建立问题的目标规划数学模型;针对这一复杂模型,提出基于特定邻域结构及其寻优概率的变邻域搜索算法;最后在标准算例上使用变邻域搜索算法与其他文献算法进行比较分析,结果表明该变邻域搜索算法对于干扰事件发生后的车辆路线重排结果在系统整体扰动程度方面明显优于其他文献的算法.     

时变路网下多中心电动车-无人机协同配送路径优化

针对车辆行驶时间依赖配送区域路网速度变化的多中心电动车-无人机协同配送路径问题,本文综合考虑配送区域路网交通信息,无人机最大飞行距离、承重能力,配送过程中电动车电池的荷电状态,以及车辆行驶速度、载重量等对电动车能耗的影响等,以总配送成本最小化为目标建立多中心车辆-无人机协同配送路径优化模型。根据问题特征,本文设计遗传大邻域搜索混合算法求解模型,该算法在传统遗传算法基础上,采用整数编码随机生成初始种群,通过无人机最大承重能力、飞行距离筛选无人机可服务的客户,然后确定车辆及无人机的配送路径生成初始解,并嵌入2组摧毁和重建算子进行进化操作。本文通过多组算例验证了算法及模型的有效性,并分析了车辆搭载的无人机数量以及车辆行驶速度对配送方案制定的影响。研究成果丰富和拓展了车辆路径优化的研究领域,可为交通、物流企业优化决策配送方案提供理论依据。     

运力受扰的多车场车辆调度干扰管理问题研究

为解决物流配送系统中因运输车辆毁坏而产生的干扰问题,基于干扰管理思想提出了解决问题的扰动恢复策略与实施方案。在扰动度量的基础上,设计了多车场车辆调度扰动恢复策略,建立相应的干扰管理模型。针对多车场车辆调度干扰管理问题的特有属性,设计了一系列求解简化策略,有效简化了问题的求解空间。结合干扰管理模型的特点,使用改进的遗传算法进行求解。最后给出了一个算例,其结果证明了干扰管理模型与算法的有效性。     

基于资源共享和温度控制的生鲜商品多中心车辆路径优化问题

针对生鲜商品多中心共同配送优化研究在资源共享和温度控制有效结合方面存在的不足,提出研究集成资源共享和温度控制的生鲜商品多中心车辆路径优化问题。首先,结合生鲜商品的易腐性和配送过程的及时性特征,构建了包含生鲜商品多中心间的运输成本、配送成本、车辆温控成本、违反时间窗惩罚成本、生鲜商品价值损失和车辆租赁成本的物流运营成本最小和配送车辆使用数最小的双目标优化模型。然后,设计了结合生鲜需求客户地理位置、需求商品温控区间和时间窗约束的K-means多维聚类算法,进而提出一种TS-NSGA-II算法,该算法设计了禁忌搜索算法(TS)和非支配排序遗传算法(NSGA-II)间有效的选择性赋予机制,进而增强了解空间的搜索和寻优能力,并通过与MOGEA和MOPSO算法的对比分析,进一步验证了模型和TS-NSGA-II算法的有效性。最后,通过实例分析探讨了不同资源共享模式和温控区间的多中心车辆路径优化方案,研究结果可为生鲜商品物流配送企业进行资源共享模式选择和温控区间设计提供方法支撑和决策支持。     

时间依赖型多配送中心带时间窗的开放式车辆路径问题研究

本文研究了时间依赖型多配送中心带时间窗的开放式车辆路径问题,基于道路通行情况,建立车辆行驶速度时间函数;考虑车辆载重、里程限制与客户点服务时间窗的约束,建立混合整数规划模型,目标函数为最小化车辆总成本,具体包括车辆行驶时间成本和车辆固定发车成本;提出了一种二维编码方式的混合遗传算法,针对混合遗传算法设计了多分区段单点交叉策略、动态插入算子及局部搜索策略;最后,基于Solomon VRPTW基准数据集生成27个测试算例,使用混合遗传算法进行求解。数值实验结果表明,考虑道路通行情况,选择合理的出发时间,避免拥堵时段进行配送服务,能明显减少车辆的总行驶时间,且验证了混合遗传算法能够获得问题的满意解。     

同时集散货物的开放式车辆路径问题研究

本文研究了车辆工作时间限制下同时集散货物的多配送中心开放式车辆路径问题,以车辆数和运输里程最小为目标,建立了多目标规划模型,提出了基于拉格朗日松弛技术和禁忌搜索算法的混合求解算法。 该算法首先求出最优解的最大下界,然后采用客户点的分配和调整策略实现解的可行化,其中禁忌搜索引入了4种领域搜索方法,采用了随机变领域搜索方法和重起策略。算例分析表明,该算法能有效地找到满意解,且采用开放式安排路线比闭合式安排路线更加经济合理。     

基于电商承诺送达机制的多目标低碳MDVRP优化研究

针对存在多配送站的电商物流配送问题,首先,考虑实际装载量对物流配送过程中车辆燃料消耗量的影响,建立燃料消耗量模型,并结合电商平台的承诺送达机制,构建配送延迟时间函数。随后,提出了以最小化物流成本和延迟收货时间的多目标多配送站车辆路径规划问题,建立该问题的混合整数规划模型。再次,采用基于分解的多目标遗传求解算法对问题进行求解。该算法采用矩阵编码的方式,设计了基于贪婪搜索策略的启发式初始化方法,考虑到贪婪搜索策略容易陷入局部最优的劣势,在算法迭代过程中,允许部分不可行解存在以扩大解空间的搜索范围,并进一步设计了遗传算法的交叉和变异算子。最后,以具体物流配送案例进行数值实验,实验结果表明所设计的算法对求解本文模型是有效的。     

Distribution planning of bulk lubricants at BP Turkey

We address the distribution planning problem of bulk lubricants at BP Turkey. The problem involves the distribution of different lube products from a single production plant to industrial customers using a heterogeneous fleet. The fleet consists of tank trucks where each tank can only be assigned to a single lube. The objective is to minimize total transportation related costs. The problem basically consists of assigning customer orders to the tanks of the trucks and determining the routes of the tank trucks simultaneously. We model this problem as a 0–1 mixed integer linear program. Since the model is intractable for real-life industrial environment we propose two heuristic approaches and investigate their performances. The first approach is a linear programming relaxation-based algorithm while the second is a rolling-horizon threshold heuristic. We propose two variants of the latter heuristic: the first uses a distance priority whereas the second has a due date priority. Our numerical analysis using company data shows that both variants of the rolling horizon threshold heuristic are able to provide good results fast.     

Multi-depot vehicle routing problem with time windows under shared depot resources

A new variant of multi-depot vehicle routing problem with time windows is studied. In the new variant, the depot where the vehicle ends is flexible, namely, it is not entirely the same as the depot that it starts from. An integer programming model is formulated with the minimum total traveling cost under the constrains of time window, capacity and route duration of the vehicle, the fleet size and the number of parking spaces of each depot. As the problem is an NP-Hard problem, a hybrid genetic algorithm with adaptive local search is proposed to solve it. Finally, the computational results show that the proposed method is competitive in terms of solution quality. Compared with the classic MDVRPTW, allowing flexible choice of the stop depot can further reduce total traveling cost.     

有顾客时间窗和发货量变化的紧急车辆调度研究

对紧急车辆调度系统进行了研究,探讨了紧急车辆调度问题实现的关键技术.对有顾客时间窗和发货量变化的紧急车辆调度问题,运用了禁忌算法(TS)进行优化.算法基于实数编码,应用GENI插入法产生初始解和进行邻域操作,设计了三种邻域,利用容量约束控制单条路径配送点数,采用惩罚函数处理时间窗约束,通过设计虚拟车场等方法实现了车辆的紧急调度.本文给出了一个具有代表性的算例试验结果,算例结果及其分析表明了此方法对优化紧急车辆调度问题的有效性.     

A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries

The vehicle routing problem with pickups and deliveries (VRPPD) extends the vehicle routing problem (VRP) by allowing customers to both send and receive goods. The main difficulty of the problem is that the load of vehicles is fluctuating rather than decreasing as in the VRP. We design a reactive tabu search metaheuristic that can check feasibility of proposed moves quickly and reacts to repetitions to guide the search. Several new best solutions are found for benchmark problems.     

Cross dock scheduling: Classification,literature review and research agenda

At cross docking terminals incoming deliveries of inbound trucks are unloaded, sorted, moved across the dock and finally loaded onto outbound trucks, which immediately leave the terminal towards their next destination in the distribution chain. Accordingly, a cross dock is a consolidation point in a distribution network, where multiple smaller shipments can be merged to full truck loads in order to realize economies in transportation. In this context, the truck scheduling problem, which decides on the succession of truck processing at the dock doors, is especially important to ensure a rapid turnover and on-time deliveries. Due to its high real-world significance, several truck scheduling procedures have been introduced during recent years, which all treat specific cross dock settings. In order to structure and promote scientific progress, this paper introduces a classification of deterministic truck scheduling. With the help of this classification, existing literature is reviewed and future research needs are identified. Moreover, we represent a yet unexplored class of truck scheduling problems which is highly relevant in real-world distribution networks.     

A visual interactive approach to classical and mixed vehicle routing problems with backhauls

In this paper a new visual interactive approach for the classical vehicle routing problem with backhauls (VRPB) and its extensions is presented. The classical VRPB is the problem of designing minimum cost routes from a single depot to two type customers that are known as Backhaul (pickup) and Linehaul (delivery) customers where deliveries after pickups are not allowed. The mixed VRPB is an extension of the classical VRPB where deliveries after pickups are allowed.     

Hierarchical production planning for multi-product lines in the beverage industry¶

Multi-commodity production and distribution scheduling is one of the most complex and crucial problems facing many manufacturing companies. For a major European manufacturer specialising in bottling juices and drinks, we have designed and developed a hierarchical decomposition approach to the solution of the multi-commodity production planning problem. In this paper we focus our attention on the coarsest decomposition level, called multi-commodity aggregate production planning (MCAP). It concerns the choice of the best feasible production plan for a set of products (commodities) over an extended time horizon so as to meet forecast aggregate demands throughout the horizon. At this level, the problem constraints include hard constraints (such as production lines having a maximum capacity and products having short life-times), and soft constraints (budgetary concerns.) The objective is to determine the production plan that covers each period's demands as best as possible, while minimizing all relevant costs. Our method for solving MCAP produces optimal plans in negligible times in commodity PC workstations.     

Exact approaches for the pickup and delivery problem with loading cost

In this paper, we propose a branch-and-cut algorithm and a branch-and-price algorithm to solve the pickup and delivery problem with loading cost (PDPLC), which is a new problem derived from the classic pickup and delivery problem (PDP) by considering the loading cost in the objective function. Applications of the PDPLC arise in healthcare transportation where the objective function is customer-centric or service-based. In the branch-and-price algorithm, we devise an ad hoc label-setting algorithm to solve the pricing problem and employ the bounded bidirectional search strategy to accelerate the label-setting algorithm. The proposed algorithms were tested on a set of instances generated by a common data generator in the literature. The computational results showed that the branch-and-price algorithm outperformed the branch-and-cut algorithm by a large margin, and can solve instances with 40 requests to optimality in a reasonable time frame.     

蓄冷式多温集配路径优化模型

现实中,由于配送中心选址对占地面积、土地成本等要求较高,加之物流企业资金有限或交通拥堵等限制,配送中心的设置通常会远离市中心。为了减少集货与配送车辆往返配送中心的次数以及车辆行驶距离,本文以总成本最小化,包括车辆派遣成本、行驶成本、碳排放成本、违背时间窗的惩罚成本、保温柜/箱成本、非整箱货物的配送成本,构建了1个配送中心由大型车辆为m个供应商采用蓄冷柜/蓄冷箱集货,由1个虚拟接驳点(中型车辆)结合小型车辆为n个零售门店采用蓄冷箱多温共配的路径优化模型,并设计了基于蚁群算法的混合更新信息素策略。以20个零售门店与6个海鲜供应商的三种品温海鲜品为仿真算例,验证了模型的有效性。实例分析发现：引入虚拟接驳点的配送线路数低于单纯的配送线路数;时间窗限制越宽松,引入虚拟接驳点的优越性越突出;具有虚拟接驳点的多温集配总成本低于自配送中心的集配总成本;具有虚拟接驳点的蓄冷式多温集配尤其利于低碳环保的冷链系统构建。     

Coordinating Sales and Raw Material Discounts in a Global Supply Chain

In this paper, the supplier of a key component to a global manufacturer offers a one‐time price discount; we study the firm's optimal response to the discount under two different strategies. In the first strategy, the firm does not pass along the discount to its customers (sales subsidiaries); the firm simply coordinates purchasing and production among the different factories to take advantage of this one‐time price discount. In the second strategy, the firm offers price discounts for its most profitable products in different sales subsidiaries to increase their demand. We carried out experiments for the two strategies based on a mathematical programming model, built around Toshiba's global notebook supply chain. Model constraints include, among others, material constraints, bill‐of‐materials, capacity and transportation constraints, minimum lot size constraints, and a constraint on minimum fill rate (service level constraint). Unlike most models of this type in the literature, which define variables in terms of single arc flows, we employ path variables, which allow for direct identification and manipulation of profitable and non‐profitable products.