A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems’ modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms.     

Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.     

考虑随机旅行时间与二维装载约束的越库配送车辆路径优化

面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力.     

动态车辆调度系统设计与开发

针对市内货物配送和收集这一典型的VRPB问题,在Dijstra算法结合A*算法计算距离矩阵的基础上,以混合禁忌搜索算法为理论基础进行静态调度求解,并以局部调整策略实现VRPB的动态调度计算。开发基于GPS/GIS/GPRS技术的动态车辆调度系统。该系统能监测新增的客户发货需求,实时跟踪车辆位置,以这些参数为输入动态地优化车辆行驶路径,并通过GPRS将调度结果快速准确地传送给车辆。     

车辆路径优化问题及求解方法研究综述

车辆路径优化问题一直以来是物流研究领域的一个热点和难点.现实生活的许多问题都可看作是车辆路径问题(VRP),因此国内外学者近年来不断提出多种车辆路径优化问题及求解方法以解决愈加复杂的问题.为进一步理清国内外研究现状,对如半开放式VRP、多级VRP、多目标VRP、绿色VRP等车辆路径优化问题,进行了总结分析,然后对车辆路径求解方法进行了介绍,特别地是对元启发式算法进行了较为详细的综述.最后,面向车辆路径优化问题和求解方法在当前形势下面临的新挑战,展望了一些新研究方向,如多目标优化、多级配送网络、绿色VRP、新型交通工具VRP和算法的通用性.     

A tabu search approach to the truck scheduling problem with multiple docks and time windows

While organizing the cross-docking operations, cross-dock managers are confronted with many decision problems. One of these problems is the truck scheduling problem. This paper presents a truck scheduling problem that is concerned with both inbound and outbound trucks at multiple dock doors. The objective is to minimize the total travel time and the total tardiness. The truck scheduling problem under study is described in detail and a mathematical model of the problem is provided which can be solved to optimality with a mixed integer programming solver, at the expense of a high computation time. Next, a tabu search approach is presented. Experimental results on new benchmark instances indicate that the proposed tabu search is able to find good quality results in a short time period, thus offering potential for integration in cross-docking decision support systems.     

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

The dynamic vehicle routing problems (DVRP) is an extension of vehicle routing problems (VRP) in order to consider possible variations of travel times in the network. In this research, a two-stage framework for solving dynamic vehicle routing problem is proposed. In the first stage, the sweep method is adopted in vehicle assignment. In the second stage, a tabu search algorithm is implemented to improve routes under real-time information. The framework is implemented in an object-oriented approach and possible benefit from real-time information is illustrated through numerical simulation. The simulation-assignment model, DynaTAIWAN is applied in numerical simulation to evaluate real-time routing strategies in a traffic network. Numerical experiments are conducted in a 50 Nodes Network and a Taichung City. The results show that positive benefits could be achieved through utilization of real-time information with careful design.     

供应链环境下带越库配送的多配送中心车辆路径问题

从零售业纵向供应链管理入手,考虑由供应商、零售商和多个配送中心构成的城市物流协同配送网络,研究带有越库配送的多配送中心车辆路径问题.分析越库配送的实施要求和操作准则,将配送过程分为集货、送货阶段.应对产品种类多样化需求,设置集货过程车辆协同作业返回配送中心,送货过程需求可拆分的运作机制.以最小化车辆运输成本和操作成本为目标,建立多配送中心车辆路径问题优化模型.针对模型特性设计改进遗传算法进行求解.最后通过仿真实例验证模型的可行性和算法的有效性, 结果表明,越库配送模式能有效服务城市区域零售门店的及时供货,在配送时间和运输成本方面具有显著优势.     

基于场景动态度的两级配送路径问题

针对城市配送过程中出现的交通限行和需求不确定性等问题,将配送周期划分为初始配送阶段和动态补货阶段,路径中包含枢纽型物流中心、配送型物流中心和客户,研究其共同构成的两级车辆配送路径优化问题.考虑到问题的动态性,提出前摄性需求配额策略及响应性补货策略,构建基于场景动态度的两级动态车辆路径问题数学模型.设计融合扫描算子的禁忌搜索算法,完成车辆初始阶段的配送路径优化;根据场景动态度,设计修复/更新性动态客户的响应策略,快速响应动态需求.最后,通过仿真算例验证模型和算法的有效性,实验结果表明,所提出的设计策略能够有效降低动态客户对低动态度应用场景初始路径的干扰,并简化高动态度场景下的路径优化复杂度.     

考虑订单发货区域的物流配送调度问题研究

研究了一种考虑订单发货区域的物流配送问题,针对问题建立了两阶段多目标整数规划模型：订单车辆指派模型和车辆配送调度模型。基于对模型及问题特点的分析,构造了一种两阶段优化算法：第一阶段,基于聚类-约束满足优化算法为待发货订单指派最佳车辆;第二阶段,基于单亲遗传算法为车辆指定最佳的物流配送方案。通过数据仿真验证,模型和算法是可行且有效的。     

Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem

In this paper, we develop an extended guided tabu search (EGTS) and a new heuristic packing algorithm for the two-dimensional loading vehicle routing problem (2L-CVRP). The 2L-CVRP is a combination of two well-known NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. We propose a meta-heuristic methodology EGTS which incorporates theories of tabu search and extended guided local search (EGLS). It has been proved that tabu search is a very good approach for the CVRP, and the guiding mechanism of the EGLS can help tabu search to escape effectively from local optimum. Furthermore, we have modified a collection of packing heuristics by adding a new packing heuristic to solve the loading constraints in 2L-CVRP, in order to improve the cost function significantly. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances.     

具有可调时间窗的动态车辆调度问题研究

提出一种新的时间窗可调整的动态车辆调度模型,设计求解该问题的算法。算法能够有效地处理预约需求和实时需求,给出时间窗的调整策略、初始路径的禁忌搜索改进策略以及实时需求的插入算法。实验计算结果表明,该算法与时间窗硬约束算法相比能够大量减少被拒绝服务的顾客数量,高效地处理实时产生的动态需求。提出的禁忌搜索算法能够显著改进初始解的质量,有效减少行驶费用,降低运输成本。     

A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service

On-time shipment delivery is critical for just-in-time production and quick response logistics. Due to uncertainties in travel and service times, on-time arrival probability of vehicles at customer locations can not be ensured. Therefore, on-time shipment delivery is a challenging job for carriers in congested road networks. In this paper, such on-time shipment delivery problems are formulated as a stochastic vehicle routing problem with soft time windows under travel and service time uncertainties. A new stochastic programming model is proposed to minimize carrier’s total cost, while guaranteeing a minimum on-time arrival probability at each customer location. The aim of this model is to find a good trade-off between carrier’s total cost and customer service level. To solve the proposed model, an iterated tabu search heuristic algorithm was developed, incorporating a route reduction mechanism. A discrete approximation method is proposed for generating arrival time distributions of vehicles in the presence of time windows. Several numerical examples were conducted to demonstrate the applicability of the proposed model and solution algorithm.     

面向全局搜索的自适应领导者樽海鞘群算法

针对实际配送过程中客户需求、车辆服务时间随机可变,提出带软时间窗的随机需求和随机服务时间的车辆路径问题.以配送车辆行驶路径为研究对象,建立基于配送成本、时间惩罚成本、修正成本的配送车辆路径优化模型,并提出一种混合禁忌搜索算法.该算法将最近邻算法和禁忌搜索算法相结合,将时间窗宽度及距离作为最近邻算法中节点选择标准;并对禁忌搜索算法中禁忌长度等构成要素进行自适应调整,引入自适应惩罚系数.实验结果表明,改进后的混合禁忌搜索算法具有较强的寻优能力、较高的鲁棒性,同时算法所得车辆行驶路径受客户需求变动影响较小.     

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.     

求解动态车辆调度问题的混合禁忌搜索算法

对带时间窗的动态车辆调度问题进行分析,引入虚拟点和时间轴概念,建立基于时间轴的动态车辆调度模型,并提出基于C-W节约法和禁忌搜索的混合禁忌搜索算法进行求解.算法中使用动态方法构造候选解和动态禁忌长度的选取策略来提高算法的收敛速度,最后通过测试实例验证了该混合算法解决动态车辆调度问题的有效性和可行性.     

基于GA的时变路网中车辆动态派遣的研究

为了使网络中的车辆调度问题更加符合实际交通状况,针对时变网络中的车辆调度问题进行了研究。将传统车辆调度模型进行了修改,目标函数中考虑了车辆的总行驶费用、总迟到惩罚、车辆总启用费用3种因素,以提高模型的适应性和通用性。由于车辆调度问题属于NP难问题,提出了采用遗传算法对问题进行求解。采用标准的VRP问题进行测试,仿真结果表明该算法简单可行,较BC-Saving启发式算法有更好的求解性能。     

A deterministic tabu search algorithm for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult optimisation problem in vehicle routing with applications where a service must be provided by a set of vehicles on specified roads. A heuristic algorithm based on tabu search is proposed and tested on various sets of benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

基于改进粒子群算法的物流配送车辆调度优化

车辆优化调度是提高物流企业运营效益的重要因素,针对标准粒子群优化算法存在的不足,提出一种改进粒子群算法（IPSO）的物流配送车辆调度优化方法。建立物流配送车辆调度优化的数学模型,将车辆与车辆路径编码成粒子,通过粒子之间的协作找到最优物流配送车辆调度优化方案,并对粒子群算法存在的不足进行了相应的改进,最后给出仿真实验对其性能进行测试。实验结果表明,IPSO算法不仅加快了物流配送车辆调度优化问题求解的速度,而且获得了最优解的概率,具有比其他调度算法更明显的优势。     

A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm

The location of manufacturing facilities is one of the most important strategic decisions considered in the design of logistic systems. Another important strategic decision is the structure and management of the fleets. Most often, even if two types of problem (i.e., location of facilities and vehicle routing) have occurred in a given scenario, they have been studied and solved separately. This paper presents a new integrated mathematical model for a bi-objective multi-depot location-routing problem where the total demand served is to be maximized and the total cost, consisting of start-up of the facility, fixed and variable depots and variable delivery cost, is to be minimized. Since this type of the problem is NP-hard, a new multi-objective scatter search (MOSS) algorithm is proposed to obtain the Pareto frontier for the given problem. To validate the performance of the proposed MOSS algorithm in terms of the solution quality and diversity level, various test problems are carried out and the efficiency of this algorithm based on some comparison metrics is compared with the elite tabu search (ETS). The computational results show that the proposed MOSS outperforms the ETS, especially in large-sized problems.