车辆调度问题的混合算法

针对多配送中心车辆调度问题,以及考虑到遗传算法与禁忌搜索算法各自的优缺点建立了多配送中心车辆优化调度的数学模型设计了遗传算法和禁忌搜索算法相结合的混合算法。该算法避免了遗传算法和禁忌搜索算法各自的缺陷,并进行了仿真分析。     

基于改进禁忌搜索算法的车辆路径优化

为解决车辆路径优化问题,提出改进禁忌搜索算法.首先,采用车辆一需求分配结构,将整个车辆路径问题分解成若干子问题,然后用禁忌搜索算法求解每个子问题,最后从所有子问题的最优解中选取全局最优解,并通过具有代表性的算例试验和分析.仿真试验结果表明,该算法拓展了搜索空间,提高了最优解的质量,能够有效地解决车辆路径优化问题.     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

铁路客运站到发线分配问题研究

通过探讨铁路客运站到发线分配问题的有效合理解决方法,以行车交叉干扰小、方便旅客出行和到发线应用均衡性为优化目标,建立客运站到发线分配问题的整数规划模型,并综合考虑捕食搜索算法和禁忌搜索算法的优缺点,设计了基于捕食策略的禁忌搜索算法的求解.通过实例验证分析,得到的分配结果令人满意,表明模型的应用和设计算法具有可行性.     

禁忌搜索算法在编组站调机运用计划中的应用

在分析论述调机运用计划编制方法的基础上,提出应用禁忌搜索算法进行编组站调机运用计划的编制。分别以最小化延迟解体列车和编组列车加权数量为目标建立数学模型,以解编顺序作为优化对象,设计禁忌搜索算法对其进行求解,并以解体顺序为例,采用两两交换(2-opt)方式构建邻域,以该操作前后列车解体顺序的变化作为禁忌对象构建禁忌表,利用软件编程实现模型计算,并通过算例验证该算法的可行性和有效性。     

基于启发式函数蚁群算法的VRP优化研究

车辆路径问题是一个NP难题,蚁群算法是求解诸如车辆路径安排等组合优化问题的有效工具,为此利用启发式函数对传统的蚁群算法进行改进和优化.并通过实例对该方法进行检验,其结果显示,启发式函数蚁群算法的性能,优于传统的蚁群算法.     

成网条件下高速铁路动车组运用计划优化模型研究

通过阐述高速铁路成网后对动车组运用计划的影响,分析成网条件下的动车组运用计划,分步骤构建时空网络,以路径费用最小为目标函数,综合考虑动车组运用计划和检修计划等相关约束,建立动车组运用计划优化模型,选用大规模邻域搜索算法进行模型求解。以京沪、沪宁高速铁路数据为例,验证该算法的可行性和有效性。比较大规模邻域搜索算法与模拟退火算法的优化结果,认为大规模邻域搜索算法能够较快地获取优化结果。     

模糊车辆路径问题模型及算法研究

分析模糊需求信息条件下车辆路径问题,考虑客户对配送时间的要求和车辆行驶时间的不确定性,运用模糊数学的可能性理论建立以车辆配送总行驶距离最小化为目标的机会约束规划模型,并将模糊模拟、神经元网络和遗传算法结合在一起,设计出混合智能算法对该问题进行求解.通过实例说明算法的有效性.     

生活垃圾收运路径优化研究

<正>基于生活垃圾量剧增、城市建成区范围不断扩展、生活垃圾终端处理设施不断外移、收运系统模式重新调整等问题,构建以最小化运输成本和碳排放成本为目标的生活垃圾收运路径优化模型,并通过重构解空间设计了一种改进回溯搜索算法对模型展开求解。最后,通过与遗传算法和模拟退火算法相比,改进回溯搜索算法能获得更高质量的解,收运路径方案的收运距离最短,在生活垃圾收运路径优化中能得到一个较好的应用。     

基于遗传模拟退火算法的战时物流配送路径优化研究

综合考虑战时物流配送车辆路径问题（VRP）的多目标评价，提出多属性道路网络下战时物流配送的VRP算法，并建立完全分层优化模型。将进化算法与传统优化技术相结合，构造了模型的两层求解算法，第一层采用遗传算法和模拟退火算法混合的GASA算法，第二层采用枚举法。并以成品燃油配送为例进行了实验，结果表明算法较标准遗传算法更有效。     

A reactive tabu search algorithm for the multi-depot container truck transportation problem

A container truck transportation problem that involves multiple depots with time windows at both origins and destinations, including the reposition of empty containers, is formulated as a multi-traveling salesman problem with time windows (m-TSPTW) with multiple depots. Since the problem is NP-hard, a cluster method and a reactive tabu search (RTS) algorithm are developed to solve the problem. The two methods are compared with the mixed integer program which can be used to find optimum solutions for small size problems. The computational results show that the developed methods, particularly the RTS algorithm, can be efficiently used to solve the problem.     

Designing robust routes for demand-responsive transport systems

In this study, we propose an innovative concept for robust demand-responsive transportation (DRT) systems where vehicles may deviate from the planned route to accept late requests, which are unknown during the planning stage. We propose a new formulation of the problem as a stochastic mixed integer program and describe an efficient heuristic procedure that embeds a tabu search approach in a scheme for merging different scenario solutions. The computational results demonstrate the validity of the heuristic and provide useful managerial insights into DRT systems, thereby showing the value of incorporating uncertainty into the planning process.     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

We study a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance. We formulate the problem as a mixed-integer linear programming model and develop a tabu search heuristic that efficiently addresses computational challenges due to parallel arcs. Numerical experiments show that the heuristic is highly effective and that freight operators can achieve advantages in cost and customer service by considering alternative paths, especially when route duration limits are restrictive and/or when vehicles of smaller capacity are dispatched to serve remote customers.     

Demand-responsive transit circulator service network design

Commuter rail systems are being introduced into many urban areas as an alternative mode to automobiles for commuting trips. It is anticipated that the shift from the auto mode to rail mode can greatly help alleviate traffic congestion in urban road networks. However, the right-of-way of many existing commuter rail systems is usually not ideally located. Since the locations of rail systems were typically chosen long ago to serve the needs of freight customers, the majority of current commuter rail passengers have to take a non-walkable connecting trip to reach their final destinations after departing the most conveniently located rail stations. To make rail a more viable commuting option and thus more competitive to the auto mode, a bus feeder or circulator system is proposed for transporting passengers from their departing rail stations to final work destinations in a seamless transfer manner. The key research question with operating such a bus circulator system is how to optimally determine a bus route and stopping sequence for each circulating tour by using the real-time demand information. In this paper, we name this joint routing and stopping optimization problem the circulator service network design problem, the objective of which is to minimize the total tour cost incurred to bus passengers and operators with respect to minimizing the walk time of each individual bus passenger. A bi-level nonlinear mixed integer programming model is constructed and a tabu search method with different local search strategies and neighborhood evaluation methods is then developed for tackling the circulator service network design problem.     

Multi-period heterogeneous vehicle routing considering carbon emission trading

This study considers the problem of determining heterogeneous vehicle routes in each period of a given planning horizon while satisfying service combinations, customer demands and vehicle capacities. The objective is to minimize the sum of vehicle operation costs and carbon emission trading cost/benefit, where the trading cost is incurred to purchase the carbon emission right if the total emission exceeds an upper limit in each period, while the trading benefit can be obtained by selling the right in each period, otherwise. A mixed integer programming model is developed to formulate the problem mathematically. Then, a tabu search algorithm is proposed that incorporates the characteristics of the heterogeneous and the period vehicle routing problems while considering the amount of carbon emission in each period. Computational experiments were done on modified benchmark instances and additional random instances, and the results show that the multi-period approach outperforms the existing single-period one in overall average. In particular, the test results show that the multi-period approach can reduce carbon emission more significantly than the single-period one without sacrificing the total cost.     

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.     

Scheduling technicians for planned maintenance of geographically distributed equipment

A real-world planned maintenance scheduling problem that exists at several business units within United Technologies Corporation (UTC) is addressed in this paper. The scheduling problem is formulated as a multiple tour maximum collection problem with time-dependent rewards and an adaptive memory tabu search heuristic is developed to solve it. The effectiveness of the proposed solution approach is examined using real-world problem instances supplied by UTC. Relevant upper bounds are derived for the application. Results of numerical experiments indicate that the proposed tabu search heuristic is able to obtain near optimal solutions for large-size (i.e., actual) problem instances in reasonable computation time.