集装箱码头作业系统堆场作业管理策略分析

提升集装箱的装卸效率是当前集装箱码头发展中所面临的主要问题。文中简要阐述了加强集装箱码头堆场管理所具有的重要意义,并对当前集装箱码头作业系统堆场作业管理策略和提升集装箱码头作业系统堆场作业管理效率的有效策略进行了详细分析。     

考虑实时预倒箱的出口箱堆场多场桥调度优化

在出口集装箱堆场的实际作业过程中，倒箱是制约场桥作业效率的瓶颈之一。为提高出口箱堆场的作业效能，减少船舶装船作业时间，采用实时预倒来降低倒箱的影响，研究出口箱堆场多场桥调度优化问题。考虑待提箱作业次序固定、场桥间保持安全距离及不可跨越的现实约束，兼顾内集卡的等待上限，侧重场桥作业过程中的实时预倒箱，构建了以带惩罚因子的内集卡总等待时间最少为目标的混合整数线性规划模型。基于问题自身的特点设计了混合和声模拟退火算法，得出了各场桥的行走路径与实时预倒箱方案。在算例实验中，通过与不考虑实时预倒箱的方案、FCFS方案以及下界进行对比，验证了考虑实时预倒箱的场桥调度模型及算法的有效性，为集装箱码头出口箱堆场的场桥调度提供参考。     

集装箱枢纽港主辅码头间靠泊决策的协同优化

近年来集装箱枢纽港码头拥挤问题日益突出,船舶等泊时间的延长导致船公司经营成本增加,尤其对于经营支线运输的船公司而言更是如此.鉴于此,船公司在拥挤的集装箱枢纽港码头附近设置趸船或驳船作为辅助码头来停泊箱量较小的船舶,通过建立缓冲区以减少等泊时间及降低停泊费用.本文从集装箱支线运输公司经营人的角度,首次提出了集装箱枢纽港主辅码头间的靠泊决策的协同问题,基于泊位调度理论建立了靠泊决策的协同优化模型,并用改进的遗传算法求解.仿真实验定量分析并证实了设置浮动码头对集装箱支线运输公司减少等泊时间及降低成本的有效性.     

一种基于闭排队网络的集装箱码头设备配置优化模型

将集装箱码头龙门吊装卸工艺抽象为闭排队网络模型,采用估算均值法计算了顾客到达和服务时间分布为一般情况的闭排队网络系统性能指标.仿真结果表明,闭排队网络模型计算结果可以为集装箱码头设备配置提供一定的决策支持.     

基于混合流水作业组织的集装箱码头装卸作业集成调度优化

集装箱码头集疏运资源调度的对象是由岸桥、集卡、场桥所构成的多阶段一体化的集装箱装、卸、运操作系统,将该系统的调度优化基于多阶段混合流水线调度问题建立混合整数规划模型,同时考虑集装箱码头现实作业中预定义顺序、避免岸桥交叉作业、以及取决于作业顺序的切换时间等现实约束,针对问题自身的特点设计了两阶段启发式算法,得出各阶段设备的指派结果及作业顺序。通过与基于现行调度规则的调度方案以及与目标函数理论下界值的对比实验,显示了所提出的集成调度模型及求解算法能够有效降低船舶在港时间并实现集卡资源的共享,为集装箱码头集疏运资源的集成调度提供了新的思路。     

时间依赖型绿色车辆路径模型及改进蚁群算法

绿色车辆路径规划对物流配送领域的节能减排具有重要的现实意义.针对时间依赖型绿色车辆路径问题(time-dependent green vehicle routing problem,TDGVRP),考虑车辆不同出发时刻对行驶时间的影响,分析车辆时变速度、载重与碳排放率之间的关系,确定基于车辆时变速度和载重的碳排放率度量函数;在此基础上,以车辆油耗和碳排放成本、使用时间成本和固定成本、等待成本与人力成本之和作为目标函数,构建TDGVRP模型,并根据模型特点设计基于路段划分策略的车辆行驶时间计算方法,提出了改进蚁群算法.算例仿真结果表明,构建的模型和提出的算法能合理规划车辆出发时刻,有效规避交通拥堵时间段,降低配送总成本,减少油耗和碳排放.     

集装箱码头泊位计划干扰恢复多目标模型

研究集装箱码头中干扰事件发生后泊位计划的调整问题,目的是降低干扰事件对集装箱码头作业系统的干扰.基于干扰管理方法,建立泊位计划干扰恢复多目标、多阶段模型,该模型考虑码头不同客户的特点以及多方利益的平衡,从码头作业成本、船舶延误以及计划偏离度三个方面度量系统扰动.为求解模型,提出了基于字典续的求解方法,并利用算例对模型与算法的有效性进行了验证.结果表明:该模型与算法可以有效解决泊位计划调整问题,模型能够考虑各方的利益以及码头各类客户的特点,因此得到的泊位调整方案更科学,同时,模型各目标的重要顺序可根据情况进行调整,实用性与可操作性更高.     

考虑充电策略与电池损耗的电动汽车路径优化问题研究

针对目前研究电动物流车辆路径问题的文章未考虑电池损耗对运营成本的影响，且多在充电速率为恒定值的情况下对充电策略进行优化，本文将电动物流车辆在配送货物途中的充电时间和电池损耗成本纳入目标函数并建立了线性规划数学模型，统筹安排车辆行驶路径和充电策略使得物流企业整体运营成本最低。其次，提出了求解该问题的多阶段启发式算法MCWIGALNS。随后，通过多组算例验证了模型和算法的准确性。实验结果表明，考虑充电时间与深度放电成本的模型可以在配送距离不变或略有增加的情况下，较大幅度减少充电时间与电池损耗成本，到达降低运营成本的目的。最后，将算法实验结果与本领域已发表的成果进行比较，证明了MCWIGALNS算法对车辆路径问题具有出色的求解能力，提升了该问题理论成果的实用性。可以为物流企业电动汽车路径策略提供良好借鉴与帮助。     

基于Multi-Agent的集装箱码头物流作业重构

在介绍集装箱码头物流作业流程的基础上,通过引入计算Agent和作业角色Agent构建了基于Multi_Agent的集装箱码头物流作业重构系统结构,并说明了重构中作业角色Agent的工作过程,用强化学习算法设计了作业角色Agent.接着阐述了在基于Multi_Agent的集装箱码头物流作业重构中计算Agent重构模型,以及用对策论和社会规则相结合实现相邻作业角色Agent的协调.最后,以某集装箱码头泊位作业和岸桥作业两相邻作业重构进行了数据分析,证明了重构的有效性.     

集装箱码头外部集卡预约优化模型与算法

针对集装箱码头闸口拥堵问题,建立集卡预约优化模型,目的是减少集卡排队等待时间和拥堵期间的碳排放。该模型在给定的集卡到达调整量水平的限制下,确定每个时段最优的预约份额,同时利用非平稳排队模型描述集卡到达随时间到达的特点。为求解模型,设计基于遗传算法与逐点固定流体近似算法(PSFFA)的求解方法,该算法利用遗传算法搜索最优解,基于PSFFA算法计算集卡排队时间。最后,利用算例对模型和算法的有效性进行了验证。结果表明,集卡预约优化模型可以有效地减少集卡排队时间,PSFFA方法可以较好地求解到达过程不平稳的排队问题。     

Scheduling material handling vehicles in a container terminal

Increasing global trade has created the need for efficient container ports. The goal of the port is to move containers as quickly as possible and at the least possible cost. Goods that are delayed at the port are inevitably tardy when delivered to the customer, and thus sanctioned by late charges. Two key activities in the port are (i) unloading of containers from truck and then storage in the export area, and (ii) removal of containers from import storage and then loading onto the trucks. Since containers are large and heavy, specialized material handling vehicles are required for transporting them within the terminal. The focus of this paper is on port terminals where straddle carriers are primarily used to move containers. Container terminals typically have well developed computer and communication networks. Through these networks a terminal scheduler will control and schedule the movement of the straddle carrier fleet in real time. The objective of the terminal scheduler is to minimize the empty travel of straddle carriers, while at the same time minimizing any delays in servicing customers. This paper presents a straddle scheduling procedure that can be used by a terminal scheduler to control the movement of straddle carriers. At its core, the procedure is driven by an assignment algorithm that dynamically matches straddle carriers and trucks, as each becomes available. The procedures were developed and tested in collaboration with the largest container terminal operator in the Port of New York and New Jersey. Using a simulation model of the real system, the superiority of the proposed procedure over two alternative scheduling strategies is illustrated.     

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。     

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。     

Optimum number of Kanbans between two adjacent stations

A model and solution strategies are developed for determining the optimum number of Kanbans between two adjacent workstations. The total cost is expressed as a function of the number of Kanbans, and the parameters, shortage cost of materials and holding cost of containers. Since the number of Kanbans obtained has to be an integer, an incremental search procedure is used, which leads to the construction of a strong bound on the optimal number of Kanbans required.     

Anticipatory shipment for pickup point supply

Many retailers allow customers to shop online and collect their orders at a pickup point nearby. In this paper, we study the anticipatory shipment of items to such pickup points in order to improve service and operational efficiency. We formulate a stochastic programming model to support the selection of products and associated quantities to ship to the pickup points in anticipation of customers’ demand. The results of our numerical experiments suggest that anticipatory shipments can have substantial benefits both in terms of cost and lead-time. The benefits increase with the storage space at the pickup point. The anticipatory shipment strategy is especially beneficial in a setting which requires short delivery lead-times and when the e-fulfilment warehouse is further away.     

Adapting operations to new information technology: A failed “internet of things” application

Our setting is the secure data destruction industry in which company trucks travel to distant customer sites to pick up outdated documents containing sensitive information (e.g. social security numbers). Outdated documents are stored in locked containers to be shredded, rather than simply recycled, by the secure data destruction service provider. Currently, no client site inventory level information is available. Due to the lack of inventory information, nearly a quarter of client site visits are wasted as there is no inventory to pick up. Another quarter of client site visits arrive too late. To improve operational efficiency, the firm invented an electronic device to make the containers at customer sites a part of the “Internet of Things” - the containers would be able to remotely transmit inventory level information, thus providing the opportunity for just-in-time inventory pickup. Utilizing this information requires a re-structuring of industry operations from fixed scheduled pickups to dynamic routing. Through heuristic creation and robust testing, we examine the conditions in which the Internet of Things application under consideration would be useful or yield minimal gains. Ultimately, the firm decided not to implement the application as the costs of maintenance and the operational re-structuring outweigh potential gains. Our study has implications for firms that are considering to explore Internet of Things to improve their operations.     

供需未匹配的多车场多车型多货品可拆分取送货车辆路径问题

取送货车辆路径问题在多点调拨系统普遍存在,属于NP-hard问题。该问题研究通常需考虑车场数、车型种类、货品种类、客户服务次数等诸多因素,已有文献多假设客户间供需已匹配、单车场、单车型、单货品和取送货需求一次性满足,优化目标多设置为总路径最短。而从企业运营实践来看,运输方案不仅要规划客户间访问路径,还需进行客户间供需匹配;运输成本不仅与运距有关,还涉及运输货品的重量和派车成本;运输网络中多车场、多车型和多货品更加常见,且客户需求量往往大于车辆装载能力。因此,本文首次尝试基于"运距×运量"的车辆路径成本和基于额定吨位的固定派车成本之和最小为优化目标,建立同时考虑多车场、多车型、多货品、客户间供需未匹配和需求可拆分的取送货车辆路径问题模型,并设计基于大规模邻域搜索的迭代局部搜索求解算法。该算法采用基于运输效率提升的贪婪思想来快速构建高质量初始可行解,并通过引入四种移除算子、两种修复算子和车场车型调整优化策略形成大规模可行搜索邻域以增强全局寻优能力。基于18个文献算例和6个企业实例的数值实验结果表明：1）本文提出的算法在求解质量和求解效率方面均优于相关文献中的算法;2）相对人工调拨方案,本文设计的方案能够帮助企业节约33%的运输成本和21%的车次。本研究不仅可拓展取送货车辆路径问题现有理论,而且可为企业实际运营提供决策支持。     

The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach

In this paper, we consider a variant of the Location-Routing Problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). The LRPSPD seeks to minimize total cost by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of each customer at the same time. We propose two polynomial-size mixed integer linear programming formulations for the problem and a family of valid inequalities to strengthen the formulations. While the first formulation is a node-based formulation, the second one is a flow-based formulation. Furthermore, we propose a two-phase heuristic approach based on simulated annealing, tp_SA, to solve the large-size LRPSPD and two initialization heuristics to generate an initial solution for the tp_SA. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the performance of the proposed heuristic approach. Computational results show that the flow-based formulation performs better than the node-based formulation in terms of the solution quality and the computation time on small-size problems. However, the node-based formulation can yield competitive lower bounds in a reasonable amount of time on medium-size problems. Meantime, the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSPD.