多行程带时间窗口的车辆调度问题研究北大核心

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

多行程带时间窗口的车辆调度问题研究

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

考虑均衡性的不确定时间车辆调度问题研究

本文针对车辆调度实际运行过程中时间的不确定性问题,提出了包含时间窗口、车辆容量约束的配送服务线路随机规划模型,以最小化调用的车辆数目和运行距离,降低顾客的不满意度并且尽可能保证每条路线的均衡性。结合模型,给出了基于禁忌搜索的混合启发式算法,并且生成多个算例,依据算例结果说明模型和算法优越性,同时说明可以在不降低顾客满意度和不提高总运输成本的基础上,降低各条线路之间的时间差异。     

一种市内邮件转运问题的模型与算法

邮件转运是邮政局的一项日常工作．本文提出了一种市内邮件转运问题的数学模型，并构造了这个问题的一种列生成算法．市内邮件转运问题与一般带时间窗口车辆路线问题（VRPTW）的不同之处在于，前者车辆到达客户的时刻即是对该客户开始服务的时刻．     

带凸资源和恶化效应的单机松弛窗口排序

研究带凸资源和恶化效应的单机窗口指派排序问题,其中窗口指的是松弛窗口,凸资源和恶化效应指的是工件的实际加工时间是其开始加工时间的线性函数,是其资源消耗量的凸函数。目标是确定工件的加工顺序,资源分配量以及窗口的开始加工时间和长度使其在总资源消耗费用(与窗口有关的排序费用)有上界限制的条件下,极小化与窗口有关的排序费用(总资源消耗费用)。获得了求解上述问题的最优算法,证明了该问题是多项式时间可解的。     

工期窗口安排与具有退化效应和维修活动的单机排序

考虑具有工件相关的退化效应和维修活动的单机排序模型,讨论了工期窗口安排问题.在这一模型中,机器在加工过程中产生退化使效率降低,工件的实际加工时间不仅与其所在排序中的位置有关并且与其本身的退化率有关;然而,维修活动能使机器的加工效率得到恢复.工期窗口的开始时间是已给定的常量,而工期窗口的结束时间是需要确定的变量.目标是得到安排维修活动的最佳时间、最佳工期窗口的大小和最优排序以便最小化流时间、提早、延误和工期窗口大小的总处罚函数.对这一问题,给出了一多项式算法.     

具有退化工件和工期窗口安排的排序问题

针对具有退化工件的排序模型,考虑了单机排序和两台机器流水作业的工期窗口安排问题,在这一模型中,工件的加工时间是与其开工时间和退化率有关的一个线性函数。目标是找到一个最优排序和确定工期窗口的开始时间及大小以便最小化所有工件的费用函数,费用函数由四部分组成:提前、延误、工期窗口开始时间和工期窗口大小。对所研究的单机问题,详细地讨论了符合现实情况的几种类型问题,并得到了问题的最优解;对两台机器流水作业问题,给出了多项式算法。     

一类有时间窗口约束的多资源动态调度模型与方法

含时间窗口的多资源调度，是一个包括资源分配和时间窗口分配的两阶段优化过程。在初始调度方案执行过程中，由于新的任务需求的到达，需要对初始方案进行调整．以使整个调度方案最优。本针对这种情况，分析了该问题中的主要约束条件．建立了含时间窗口的多资源动态调度模型，给出了一种启发式迭代修改求解方法；并以含时间窗口的多机调度问题为例．对模型和算法进行了验证。     

一种部分约束满足车辆路线问题及其求解算法

描述了一类过度约束车辆路线问题，其中可用车辆数较少而时间窗口等其它约束又不允许放松，因而导致不存在满足所有约束的可行解。此时问题求解可以转化为一类部分约束满足问题来处理，相应的优化目标是最小化未访问顾客的损失和。本给出了求解这类特殊问题的一种禁忌搜索算法设计，并通过规模不同的几个算例与其它常用方法进行了比较。最后分析了模型和算法的实用意义。     

分块分层优化的旅游路线规划问题研究

针对旅游路线规划决定着自驾旅游者的旅游成败问题,利用分块分层优化的思想解决了旅游路线规划这一网络优化问题。用赋权图和近邻聚类的思想构建分块网络加权图,建立考虑旅游时间、行车时间和游览时间的改进旅行商优化模型,规划区块内景点的自驾旅游路线;然后将各区块视为节点、区块间旅游时间作为时间权值之一,建立改进的多旅行商优化模型,并用模拟退火算法规划出区块间的自驾旅游路线;其次,用类比一维装箱问题的思想,建立了求最少旅游年数的一维装箱模型,并用交叉装填算法求得其最小值;最后,应用提出的方法为西安市的自驾旅游爱好者规划出了满足多种约束的游遍全国201个5A级景区的最佳旅游路线。     

A goal programming approach to vehicle routing problems with soft time windows

The classical vehicle routing problem involves designing a set of routes for a fleet of vehicles based at one central depot that is required to serve a number of geographically dispersed customers, while minimizing the total travel distance or the total distribution cost. Each route originates and terminates at the central depot and customers demands are known. In many practical distribution problems, besides a hard time window associated with each customer, defining a time interval in which the customer should be served, managers establish multiple objectives to be considered, like avoiding underutilization of labor and vehicle capacity, while meeting the preferences of customers regarding the time of the day in which they would like to be served (soft time windows). This work investigates the use of goal programming to model these problems. To solve the model, an enumeration-followed-by-optimization approach is proposed which first computes feasible routes and then selects the set of best ones. Computational results show that this approach is adequate for medium-sized delivery problems.     

A hybrid generational genetic algorithm for the periodic vehicle routing problem with time windows

We propose a new population-based hybrid meta-heuristic for the periodic vehicle routing problem with time windows. This meta-heuristic is a generational genetic algorithm that uses two neighborhood-based meta-heuristics to optimize offspring. Local search methods have previously been proposed to enhance the fitness of offspring generated by crossover operators. In the proposed method, neighborhood-based meta-heuristics are used for their capacity to escape local optima, and deliver optimized and diversified solutions to the population of the next generation. Furthermore, the search performed by the neighborhood-based meta-heuristics repairs most of the constraint violations that naturally occur after the application of the crossover operators. The genetic algorithm we propose introduces two new crossover operators addressing the periodic vehicle routing problem with time windows. The two crossover operators are seeking the diversification of the exploration in the solution space from solution recombination, while simultaneously aiming not to destroy information about routes in the population as computing routes is NP-hard. Extensive numerical experiments and comparisons with all methods proposed in the literature show that the proposed methodology is highly competitive, providing new best solutions for a number of large instances.     

An exact algorithm for a single-vehicle routing problem with time windows and multiple routes

This paper describes an exact algorithm for solving a problem where the same vehicle performs several routes to serve a set of customers with time windows. The motivation comes from the home delivery of perishable goods, where vehicle routes are short and must be combined to form a working day. A method based on an elementary shortest path algorithm with resource constraints is proposed to solve this problem. The method is divided into two phases: in the first phase, all non-dominated feasible routes are generated; in the second phase, some routes are selected and sequenced to form the vehicle workday. Computational results are reported on Euclidean problems derived from benchmark instances of the classical vehicle routing problem with time windows.     

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. In practice, however, temporary traffic congestion make such solutions non-optimal with respect to minimizing the total duty time. Furthermore, the VRPTW does not account for driving hours regulations, which restrict the available travel time for truck drivers. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing of a VRPTW. We propose an ILP formulation that minimizes the total duty time. The results of a case study indicate that duty time reductions of 15% can be achieved. Furthermore, computational experiments on VRPTW benchmarks indicate that ignoring traffic congestion or driving hours regulations leads to practically infeasible solutions. Therefore, new vehicle routing methods should be developed that account for these common restrictions. We propose an integrated approach based on classical insertion heuristics.     

Solving the vehicle routing problem with time windows and multiple routes exactly using a pseudo-polynomial model

In this paper, we address a variant of the vehicle routing problem called the vehicle routing problem with time windows and multiple routes. It considers that a given vehicle can be assigned to more than one route per planning period. We propose a new exact algorithm for this problem. Our algorithm is iterative and it relies on a pseudo-polynomial network flow model whose nodes represent time instants, and whose arcs represent feasible vehicle routes. This algorithm was tested on a set of benchmark instances from the literature. The computational results show that our method is able to solve more instances than the only other exact method described so far in the literature, and it clearly outperforms this method in terms of computing time.     

A robust optimization approach to dispatching technicians under stochastic service times

We consider the problem of dispatching technicians to service/repair geographically distributed equipment. This problem can be cast as a vehicle routing problem with time windows, where customers expect fast response and small delays. Estimates of the service time, however, can be subject to a significant amount of uncertainty due to misdiagnosis of the reason for failure or surprises during repair. It is therefore crucial to develop routes for the technicians that would be less sensitive to substantial deviations from estimated service times. In this paper we propose a robust optimization model for the vehicle routing problem with soft time windows and service time uncertainty and solve real-world instances with a branch and price method. We evaluate the efficiency of the approach through computational experiments on real industry routing data.     

A new branching strategy for time constrained routing problems with application to backhauling

In this paper, we explore a new branching strategy for branch-and-bound approaches based on column generation for the vehicle routing problems with time windows. This strategy involves branching on resource variables (time or capacity) rather than on network flow variables. We also examine criteria for selecting network nodes for branching. To test the effectiveness of the branching strategy, we conduct computational experiments on time window constrained vehicle routing problems where backhauling is permitted only after all the shipments to clients have been made. The branching method proved very effective. In cases where time was the more binding constraint, time-based branching succeeded in decreasing the number of nodes explored by two thirds and the total computation time by more than half when compared to flow-based branching. The computational results also show that the overall algorithm was successful in optimally solving problems with up to 100 customers. It produced an average cost decrease of almost 7% when backhauling was permitted as compared to the cost involved when the client and the distributor routes were distinct.     

An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles

The vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem. It arises when each vehicle performs several routes during the workday due to strict time limits on route duration (e.g., when perishable goods are transported). The routes are defined over customers with a revenue, a demand and a time window. Given a fixed-size fleet of vehicles, it might not be possible to serve all customers. Thus, the customers must be chosen based on their associated revenue minus the traveling cost to reach them. We introduce a branch-and-price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation, using column generation. The pricing subproblems are elementary shortest path problems with resource constraints. Computational results are reported on euclidean problems derived from well-known benchmark instances for the vehicle routing problem with time windows.     

The open vehicle routing problem with time windows

In this paper, we consider the open vehicle routing problem with time windows (OVRPTW). The OVRPTW seeks to find a set of non-depot returning vehicle routes, for a fleet of capacitated vehicles, to satisfy customers’ requirements, within fixed time intervals that represent the earliest and latest times during the day that customers’ service can take place. We formulate a comprehensive mathematical model to capture all aspects of the problem, and incorporate in the model all critical practical concerns. The model is solved using a greedy look-ahead route construction heuristic algorithm, which utilizes time windows related information via composite customer selection and route-insertion criteria. These criteria exploit the interrelationships between customers, introduced by time windows, that dictate the sequence in which vehicles must visit customers. Computational results on a set of benchmark problems from the literature provide very good results and indicate the applicability of the methodology in real-life routing applications.     

Map-Route: a GIS-based decision support system for intra-city vehicle routing with time windows

This paper presents a Decision Support System (DSS) that enables dispatchers–schedulers to approach intra-city vehicle routing problems with time windows interactively, using appropriate computational methods and exploiting a custom knowledge base that contains information about traffic and spatial data. The DSS, named Map-Route, generates routes that satisfy time and vehicle capacity constraints. Its computational engine is based on an effective heuristic method for solving the underlying optimization problem, while its implementation is developed using MapInfo, a popular Geographical Information System (GIS) platform. Map-Route provides very efficient solutions, is particularly user-friendly, and can reach answers for a wide variety of ‘what if’ scenarios with potentially significant cost implications. We have implemented Map-Route in an actual industrial environment and we report on the experience gained from this real-life application.