VRPSTW的混合改进蚁群优化算法*

软时间窗车辆路径问题（VRPSTW）是VRP的一种重要扩展类型,定义了其惩罚函数并建立数学模型。设计用于求解该问题的混合改进型蚁群算法并求解标准数据库中的紧时间窗实例。经过大量数据测试,获得了较好的效果,并验证了蚁群算法用于求解软时间窗车辆路径问题的成功实现。     

优化初始边集提高分支裁剪法求解TSP效率

概要地叙述了NP完全问题的复杂性,并简述了分支裁剪法求解NP问题最优解的策略.以求解欧氏空间的TSP问题为例,分析了利用分支裁剪法求解问题中主要影响算法求解效率的原因在于初始边集中存在大量无用信息,针对该类问题,提出了通过化简初始边集提高算法求解效率的策略,实验验证了这种方法的有效性.     

基于分布式约束满足的多约束单路径路由问题求解

约束路由问题是IP网络的一个核心功能,由于求解多约束路由问题属于NP完全问题,所以大量的研究工作围绕此展开.基于分布式约束满足的思想,设计多约束单路径路由问题求解算法,分析表明该求解算法降低计算复杂度,提高算法的性能.在分布式条件下完成算法的实现,经实验表明,算法近似程度较好,求解速度快.     

一个基于最小冲突修补的动态约束满足求解算法

约束满足问题是人工智能中一个重要的研究方向,近年来,对动态变化的约束满足问题的研究逐渐成为该领域的热点.在目前该领域最流行的LC算法基础上,引入禁忌搜索策略,提出了一个基于最小冲突修补的算法Tabu_LC.算法在每次冲突调整时将所有冲突变量看成一个整体,并采用分支定界搜索策略求解冲突变量组成的子问题,极大地提高了求解效率.同时,在约束求解系统"明月1.0"架构下给出了算法的具体实现,并针对大量随机问题进行了对比实验.结果表明,Tabu_LC算法在求解效率和解的质量上都明显优于LC算法.     

基于正交双轴算法的西瓜子形状辨析研究

在大量实验研究的基础上，提出了颗粒状物体正交双轴求解算法，在西瓜子自动分捡系统的研制中得到实现和验证。该算法实现简单，和其他算法相比，时间复杂度较低且求解精度较高，并可用于其他类似颗粒状物体形状辨析问题的求解，具有广泛的应用价值。     

基追踪问题的近点算法及其应用研究

基追踪问题具有广泛的应用背景,近年来得到了大量的关注和研究。近点算法是解决该问题的一种有效算法,其关键是子问题的求解,利用线性Bregman迭代的求解思想进行Lagrange对偶分析求解子问题,设计了一个新的迭代算法BP-PPA。与线性Bregman算法相比,BP-PPA可避免参数选取对模型的依赖,并用于非压缩感知的稀疏恢复问题求解。同时,为了提高新算法的收敛速度,进一步对新算法进行了Nestrove加速,得到了加速的BP-PPA算法。数值实验中,分别针对压缩感知中的稀疏信号恢复和非压缩感知模型,测试了参数选取对算法效率的影响,实验结果验证了新算法的有效性。     

面向冗余度机械臂QP问题求解的E47和94LVI数值算法

冗余度机械臂的二次规划(QP)问题同时受制于等式约束、不等式约束和双端约束,且面向冗余度机械臂实时控制的该类QP问题的求解对运算实时性有较高要求。考虑同时受制于上述三种约束的二次规划问题的求解,给出并研究两种数值算法(E47和94LVI算法)。这类带约束的二次规划问题被等价转换为分段线性投影方程。应用E47和94LVI算法求解上述分段线性投影方程,从而得到二次规划问题的最优数值解。同时,通过大量的数值实验,研究两种算法面向冗余度机械臂的QP问题求解性能,并给出E47、94LVI算法与经典有效集算法的对比实验结果。最终证实了E47和94LVI两种算法在求解二次规划问题上的高效性和优越性。     

项目优化调度问题的求解新方法

基于N维向量空间的数学表示,对标准PSO算法中速度和位置更新公式的符号及操作符进行了广义定义,进而提出了一种改进PSO算法;并将改进PSO算法应用于更具现实意义项目调度问题的求解。大量实验结果表明,该算法能有效求解的同时,其运行效率和解的性能也都优于相关算法。     

求解旅行商问题的波动温控模拟退火算法

针对传统模拟退火算法在求解旅行商问题时运行时间长,易陷入局部最优,且随着问题规模的增大缺陷愈发明显的问题,对传统算法的内循环过程和退火机制进行改进,使得内循环的搜索强度根据温度的变化自适应调整,同时提出波动温度控制机制,使得算法在保持温度幅值递减的总趋势下实现多次升温过程,增强求解效果,缩短求解时间,并通过TSPLIB数据库提供的大量实例得以验证.     

求解Flow-shop调度问题的HSGATS算法研究

通过研究流程企业典型生产加工方式的Flow-shop加工调度问题,分析了求解这一调度问题的各种算法的性能,特别是混合遗传算法中的混合策略,进而提出了一种以启发式搜索算法为前置算法、遗传算法为主体算法、禁忌搜索算法为后置算法的新的混合遗传算法-HSGATS算法,并针对置换Flow-shop调度问题提出了具体的算法设计和实现方法;通过对大量置换Flow--ShopBenchmark问题进行实验求解和其他算法的比较分析,验证了此算法是十分有效的。     

Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem

Multi Compartment Vehicle Routing Problem is an extension of the classical Capacitated Vehicle Routing Problem where different products are transported together in one vehicle with multiple compartments. Products are stored in different compartments because they cannot be mixed together due to differences in their individual characteristics. The problem is encountered in many industries such as delivery of food and grocery, garbage collection, marine vessels, etc. We propose a hybridized algorithm which combines local search with an existent ant colony algorithm to solve the problem. Computational experiments are performed on new generated benchmark problem instances. An existing ant colony algorithm and the proposed hybridized ant colony algorithm are compared. It was found that the proposed ant colony algorithm gives better results as compared to the existing ant colony algorithm.     

蚂蚁算法在车辆路径问题中的应用研究

本文将蚂蚁算法这种新型的生物优化思想扩展到物流管理中的车辆路径问题,从数值计算上探索了蚂蚁算法的优化能力,获得了满意的效果.     

带时间窗车辆路径问题的改进蚁群算法研究

针对带时间窗车辆路径问题，论文通过增加虚拟配送中心的数量，改进蚁群算法，从而将VRPTW问题转化为TSP问题进行求解，使每只蚂蚁都可以构建一条可行路径，避免在该问题中以往常由多只蚂蚁协同合作来构造解的低效性，通过实验计算表明该方法是可行的。     

有软时窗多车场开放式车辆路径及其禁忌搜索

有软时窗约束多车场开放式车辆路径问题是在基本的车辆路径问题上增加了时间窗约束和多车场作业的一种变化形式,是一个典型的NP-难问题。建立了问题模型,运用改进的禁忌搜索算法测试了算例。快速获得的高质量解验证了模型的正确性和算法性能的优良性。     

The Flexible Periodic Vehicle Routing Problem

This paper introduces the Flexible Periodic Vehicle Routing Problem (FPVRP) where a carrier has to establish a distribution plan to serve his customers over a planning horizon. Each customer has a total demand that must be served within the horizon and a limit on the maximum quantity that can be delivered at each visit. A fleet of homogeneous capacitated vehicles is available to perform the services and the objective is to minimize the total routing cost. The FPVRP can be seen as a generalization of the Periodic Vehicle Routing Problem (PVRP) which instead has fixed service frequencies and schedules and where the quantity delivered at each visit is fixed. Moreover, the FPVRP shares some common characteristics with the Inventory Routing Problem (IRP) where inventory levels are considered at each time period and, typically, an inventory cost is involved in the objective function. We present a worst-case analysis which shows the advantages of the FPVRP with respect to both PVRP and IRP. Moreover, we propose a mathematical formulation for the problem, together with some valid inequalities. Computational results show that adding flexibility improves meaningfully the routing costs in comparison with both PVRP and IRP.     

Adaptive memory programming for the vehicle routing problem with multiple trips

The Vehicle Routing Problem with Multiple Trips is an extension of the classical Vehicle Routing Problem in which each vehicle may perform several routes in the same planning period. In this paper, an adaptive memory algorithm to solve this problem is proposed. Computational experience is reported over a set of benchmark problem instances.     

蚁群优化算法及其应用

蚂蚁算法是由意大利学者M．Dorigo等人提出的一种新型的模拟进化算法。该算法首先应用于旅行商问题并获得了极大的成功，其后，又被用于求解指派问题、Job—shop调度问题、图着色问题和网络路由问题等。实践证明，蚂蚁算法是一种鲁棒性强、收敛性好、实用性广的优化算法，但同时也存在一些不足，如收敛速度慢和容易出现停滞现象等。     

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.     

A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows

This paper presents a new hybrid variable neighborhood-tabu search heuristic for the Vehicle Routing Problem with Multiple Time windows. It also proposes a minimum backward time slack algorithm applicable to a multiple time windows environment. This algorithm records the minimum waiting time and the minimum delay during route generation and adjusts the arrival and departure times backward. The implementation of the proposed heuristic is compared to an ant colony heuristic on benchmark instances involving multiple time windows. Computational results on newly generated instances are provided.     

避免车辆路径拥塞的动态蚁群算法

运用蚁群算法,对非闭合线路避免拥塞现象的车辆路径问题进行研究,提出了深度为1的树结构指针推进策略和相应的路径优化算法,为交通管理和车辆导航提供决策依据。仿真结果表明该算法具有较强的道路拥塞识别能力,能够有效缓解道路拥塞现象。