分车收发车辆路径问题的三个启发式算法之比较

车辆路径问题已经出现了很多的变种.在这些扩展的VRP问题当中,分车收发车辆路径问题就是其中之一.本文针对这一问题在已有的模型上加以改进,并且提出了摆脱车辆数限制的最远点拼车算法和竞争决策算法。最后结合最远点完全拼车算法通过数值实验对三者进行了比较.结果显示竞争决策算法得到的结果好于其他两者,其次是最远点拼车算法。     

物流配送车辆路径问题的鲁棒优化方法

针对物流配送中的不确定性因素,构建车辆路径间题的鲁棒性度量与优化方法,目的是降低不确定性因素对物流配送系统的影响.首先,提出车辆路径问题的鲁棒性度量指标,利用算例对各指标的效果进行分析,选择适用于度量车辆路径方案鲁棒性的指标.在此基础上,设计物流配送车辆路径规划的两阶段优化算法.算法的第一阶段不考虑车辆路径的鲁棒性,以总配送成本最小为目标函数优化配送方案;算法的第二阶段以鲁棒性度量指标最大为目标函数,以第一阶段获得的总成本与车辆数为约束条件,优化鲁棒调度方案.文章为车辆路径问题的鲁棒性度量提供了一种有效方法,同时为如何平衡供应链中的物流配送环节的服务作业成本与调度方案鲁棒性提供了思路.     

堵塞恢复时间未知的可恢复加拿大旅行者问题研究

加拿大旅行者问题是指旅行者针对行走过程中遭遇的突发性道路堵塞,如何设计一个有效路径选择策略,使得旅行者从出发地抵达目的地行走时间尽可能少的问题.从在线问题与竞争策略的角度,考虑每个堵塞恢复时间未知且相互独立的情形,给出了每个堵塞恢复时间为三角模糊数下的等待策略和贪婪策略及竞争比,并对策略执行效果进行了分析与比较.     

基于道路堵塞及顾客时间要求的快件配送最优路径选择

针对道路堵塞如节假日导致的临时最短配送路径失效的问题,提出配送网络最优路径选择模型,并设计了求解快递配送网络关键边和最优路径的算法。首先,计算出整个网络的关键边,掌握配送网络特征;其次,考虑顾客时间要求,研究不完全信息(中断无法提前预知,只有到达中断边的起点处才可知)下的最优路径,根据最短路径上各边新的特点,计算出每条边中断后对应的一组备用路径,再选择运输时间小于或等于顾客可等待时间的路径为有效路径,考虑道路堵塞情况,从有效路径中选择最优路径;最后,结合配送网络的实际情况对最优路径进行了算例分析。     

路径既定条件下有害物品运输策略选择研究

对有害物品运输问题的研究,大多集中在考虑路径风险情形下的路径选择问题方面,对路径既定条件下运输策略的选择问题尚未涉及.利用概率分析方法,比较了路径既定条件下一定量有害物品选择一次运输策略和选择多次运输策略在发生事故的可能性、路径风险两方面的差异.结果发现,一次运输情形下,发生事故的可能性较小,路径风险也较小,要使可能损失赔偿与运输成本之和最小,决策者应尽可能选择大载重量运输车辆进行有害物品运输.     

不可恢复道路堵塞路径选择问题及其算法

针对不可恢复道路堵塞的路径选择问题，分析了堵塞发生的不同起始时间对通过被堵塞边的费用(时间)产生的影响，这种影响引起的后效性使得这个问题不适合用动态规划中逆序推算方法求解。本设计了一种算法(MDA)，对经典的Dijkstra算法中的权值计算进行了修正，证明了算法的复杂性为O(n^2)，并用该算法对模型进行了求解。最后通过一个算例分析，对算法进行了验证。     

局内经济决策问题的竞争分析--一种新的经济数学工具

基于优化领域的热点研究方向之一的局内问题与竞争策略理论,本文提出了局内经济决策问题的一系列概念,说明了处理局内经济决策问题的竞争策略和传统方法的区别以及后者的缺陷.构建了利用局内问题及其竞争策略研究局内经济决策问题的理论框架,并介绍了一个具体研究实例.     

求解车辆路径问题的免疫算法

将免疫算法用于求解车辆路径问题,并根据车辆路径问题的具体情况提出了一种基于分组匹配的亲和力计算方法.实验结果表明,免疫算法能有效地应用于车辆路径问题.     

节点具有双重需求的车辆路径问题及其性质

在原有同时收发车辆路径问题定义的基础上,将节点需求与车辆容量的关系拓展到允许节点需求大于车辆容量的情形.接着对集送货需求可拆分车辆路径问题和同时收发车辆路径问题的可简化性进行了研究.给出了两类问题可简化的定义,并得到了当距离满足三角不等式,车辆容量为1时集送货需求可拆分车辆路径问题可简化并与同时收发车辆路径问题等价,而当容量大于等于2时两类问题都不可以简化的结论.同时也对两类问题当车辆容量等于1时,以及大于等于3时的计算复杂性给出了证明.最后通过一个实例说明了集送货需求可拆分车辆路径问题与同时收发车辆路径问题在最优解的结构性质方面存在着明显差异.     

基于低碳排放的车辆路径优化问题研究

研究了基于低碳排放的锦州JS配送公司的车辆路径优化问题.首先通过分析目前锦州JS配送公司的车辆路径规划方案发现该公司在规划路径时只关注路径最短,而忽视了碳排放成本.然后通过具体分析配送过程中能源消耗的来源,考虑车辆自重和载重、车辆出行距离等对配送能源消耗的影响,建立了以极小化碳排放成本为目标的车辆路径优化问题的混合整数规划模型,通过求解模型得到新的配送路径优化方案.优化后的配送方案比原先的配送方案减少了14.16万元的碳排放成本.研究结果对物流企业降低碳排放具有指导意义.     

Open vehicle routing problem with driver nodes and time deadlines

In this paper, we consider a variant of the open vehicle routing problem in which vehicles depart from the depot, visit a set of customers, and end their routes at special nodes called driver nodes. A driver node can be the home of the driver or a parking lot where the vehicle will stay overnight. The resulting problem is referred to as the open vehicle routing problem with driver nodes (OVRP-d). We consider three classes of OVRP-d: with no time constraints, with a maximum route duration, and with both a maximum route duration as well as time deadlines for visiting customers. For the solution of these problems, which are not addressed previously in the literature, we develop a new tabu search heuristic. Computational results on randomly generated instances indicate that the new heuristic exhibits a good performance both in terms of the solution quality and computation time.     

基于可靠性弹性指标的系统弹性度量方法

为深入研究系统弹性问题,首先分析系统中不同节点和边的可靠性弹性指标。通过引入节点弹性与边弹性,并考虑节点弹性与边弹性二者的联系以及对系统弹性的影响,建立了能够反映系统拓扑结构变化的弹性度量方法模型。最后,在对弹性度量方法的验证环节中,引入了具有分层特性的交通系统,按照所述弹性度量方法对分层交通系统中的节点弹性以及边弹性进行了分析,发现该种分层交通系统的整体弹性程度一般,任何小的干扰或者故障都有可能造成该交通系统拥堵或瘫痪。     

Vehicle routing with dynamic travel times: A queueing approach

Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.     

A cutting-plane algorithm for solving a weighted influence interdiction problem

We consider a two-stage defender-attacker game that takes place on a network, in which the attacker seeks to take control over (or “influence”) as many nodes as possible. The defender acts first in this game by protecting a subset of nodes that cannot be influenced by the attacker. With full knowledge of the defender’s action, the attacker can then influence an initial subset of unprotected nodes. The influence then spreads over a finite number of time stages, where an uninfluenced node becomes influenced at time t if a threshold number of its neighbors are influenced at time t?1. The attacker’s objective is to maximize the weighted number of nodes that are influenced over the time horizon, where the weights depend both on the node and on the time at which that is influenced. This defender-attacker game is especially difficult to optimize, because the attacker’s problem itself is NP-hard, which precludes a standard inner-dualization approach that is common in many interdiction studies. We provide three models for solving the attacker’s problem, and develop a tailored cutting-plane algorithm for solving the defender’s problem. We then demonstrate the computational efficacy of our proposed algorithms on a set of randomly generated instances.     

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.     

Two new algorithms for the Min-Power Broadcast problem in static ad hoc networks

In this paper we address the Min-Power Broadcast problem in wireless ad hoc networks. Given a network with an identified source node w, the Min-Power Broadcast (MPB) problem is to assign transmission range to each node such that communication from w to other nodes is possible and the total energy consumption is minimized.

As the problem is NP-Hard we first propose a simulated annealing algorithm for the MPB problem. Utilizing a special node selection mechanism in its neighborhood structure the algorithm is designed in a way enabling an efficient power consumption evaluation and search for neighboring solutions. We then combine the algorithm with a decomposition approach to enhance its performance. This is achieved by decomposing the master problem and performing metropolis chain of the simulated annealing only on the much smaller subproblems resulting from decomposition. Results from a comprehensive computational study indicate the efficiency and effectiveness of the proposed algorithms.     

Queue length and occupancy in discrete-time cyclic networks with several types of nodes

For a discrete-time, closed, cyclic queueing network, where the nodes have independent, geometric service times, the equilibrium rate of local progress is determined. Faster nodes are shown to have a capacity depending only on the service probabilities. A family of such networks, each with the same number of types of nodes, is analyzed. If the number of nodes approaches infinity, and if the ratio of jobs to nodes has a positive limit and each node type has an asymptotic density, then for a given node type, the limits of the proportion of occupied nodes and the expected queue length are calculated. These values depend on the service parameter and on the asymptotic rate of local progress. The faster nodes can attain their capacity only when the limiting density of nodes of slowest type is zero. This revised version was published online in June 2006 with corrections to the Cover Date.     

A dynamic programming operator for tour location problems applied to the covering tour problem

This paper presents an evaluation operator for single-trip vehicle routing problems where it is not necessary to visit all the nodes. Such problems are known as Tour Location Problems. The operator, called Selector, is a dynamic programming algorithm that converts a given sequence of nodes into a feasible tour. The operator returns a subsequence of this giant tour which is optimal in terms of length. The procedure is implemented in an adaptive large neighborhood search to solve a specific tour location problem: the Covering Tour Problem. This problem consists in finding a lowest-cost Hamiltonian cycle over a subset of nodes such that nodes outside the tour are within a given distance from a visited node. The metaheuristic proposed is competitive as shown by the quality of results evaluated using the output of a state-of-the-art exact algorithm.     

有服务时长和服务可选择性的快递车辆调度在线策略研究

针对快递揽件需求出现无法提前获知、服务每一个快递需求需要一定的服务时长,且无法接受全部请求的情形,提出带有服务时长和服务可选择性的快递车辆在线调度问题,证明了该问题在线策略竞争比的下界。在正半轴上提出Replan策略,在直线上提出ReOPT策略,在一般网络上提出GRH策略,证明了上述在线策略的竞争比。结果表明,考虑服务时长能够改善在线策略的竞争性能,所提在线策略在实际应用中具有实用性。结论将为快递车辆的科学调度提供理论依据。     

A branch and cut algorithm for nonconvex quadratically constrained quadratic programming

We present a branch and cut algorithm that yields in finite time, a globally ε-optimal solution (with respect to feasibility and optimality) of the nonconvex quadratically constrained quadratic programming problem. The idea is to estimate all quadratic terms by successive linearizations within a branching tree using Reformulation-Linearization Techniques (RLT). To do so, four classes of linearizations (cuts), depending on one to three parameters, are detailed. For each class, we show how to select the best member with respect to a precise criterion. The cuts introduced at any node of the tree are valid in the whole tree, and not only within the subtree rooted at that node. In order to enhance the computational speed, the structure created at any node of the tree is flexible enough to be used at other nodes. Computational results are reported that include standard test problems taken from the literature. Some of these problems are solved for the first time with a proof of global optimality. Received December 19, 1997 / Revised version received July 26, 1999?Published online November 9, 1999