警车配置及巡逻方案研究

以警车的配置与巡逻方案为研究对象,建立了一套警车巡逻模型,并提出巡逻效果显著度及隐藏性的评价标准,分别针对警车初始位置配置与巡逻方案的制定,提出警车配置优化选址的贪婪算法与基于多Agent的警车巡逻方案设计方法,给出了不同情景下的配置及巡逻方案:①在只考虑警车选址配置的情况下,配置19辆警车可以使全市路网警车覆盖率达到92.8%;②在顾及巡逻效果显著性与隐藏性的情况下,配置25辆警车使全市路网在整个巡逻过程中平均警车覆盖率达到90.9%;③在配置10辆警车的情况下,使得全市路网在整个巡逻过程中平均警车覆盖率达到61.5%.     

基于时间切片叠加的警车巡逻路线规划

在城市中,有效的安排警车巡逻对于降低犯罪率,预防潜在犯罪案件发生和及时处理案件具有十分重要的意义.通过一些必要简化首先确定了巡逻方案应当满足的条件以及方案的评价体系.通过随机贪心算法求解足够多的可行静态解,并引入时间片叠加的思想在静态解的基础上应用深度优先搜索算法,将求解动态巡逻问题转化为在有向连通图中寻找使目标函数达到最大的约束环路的问题,最终求得动态巡逻方案.最后,通过实例对模型进行了验证和评价.     

110警车配置及确定巡逻方案的问题

主要讨论社会安全系统中警车的优化配置及巡逻方案的合理安排问题.首先对道路和重点区域进行合理离散化,再根据离散化后得到的新地图计算出各个离散道路点的邻域,然后对静态过程使用模拟退火算法得到静态优化值,最后根据不同的目标和需求,通过对动态过程进行仿真,从而得到最后满足要求的动态优化值,并按照问题要求给出所需的评价值和合理的警车巡逻方案.该模型原理清晰易懂,采用启发式算法,计算简单,通用性强,优化性能显著,稳定性好.     

考虑多行程与同时取送货的电动车路径问题研究

针对城市物流配送中的电动车辆路径优化问题,考虑电动汽车的充电特性以及车辆多行程和需求点的双向货流,以最小化车辆成本、行驶成本和充电成本为目标,建立考虑多行程与同时取送货的电动车辆路径问题(EVRPMTSPD)模型,并采用列生成算法进行求解.为提高子问题求解速度,提出了基于蚁群算法的启发式寻路算法用以处理较大规模问题,数值实验验证了模型与算法的有效性,表明了考虑多行程和同时取送货能有效降低成本和提高效率.     

基于基尼系数的停车换乘拥挤收费定价模型

许多大城市提倡停车换乘的组合出行方式,目的是减少出行车辆,缓解交通拥挤状况.应用双层规划模型对停车换乘拥挤收费进行研究.下层模型采用弹性需求SUE模型,上层模型考虑了交通公平因素.以各出行方式占用的道路资源为公平指标,应用基尼系数对传统上层模型进行改造,构建一个用户盈余尽可能大而基尼系数尽可能小的上层模型,并利用基尼系数控制参数,实现在不同公平要求下的拥挤收费设计.算例表明,基于基尼系数的停车换乘拥挤收费设计,能改善道路的拥挤状况,且兼顾了用户盈余与公平.     

应急物流LRP鲁棒双层优化模型研究

考虑到突发事件下受灾点对救灾物资需求的不确定性,针对应急物流设施的定位和车辆运输救灾物资路线进行协同研究,建立了应急物流设施定位-车辆路线选择问题(LRP)鲁棒双层优化模型.运用分散式决策方式下的转化定理,将所建立的含有不确定系数的层次关联协同优化模型进行确定性转化,并设计一种混合遗传算法对转化后的确定性双层规划模型进行求解,最后,通过实例验证了模型的合理性及算法的可行性.     

第三方库存-路线问题的模型及混合遗传算法设计

根据第三方库存-路线问题的特点,以车辆租赁费用和运行费用之和为目标函数,不限制客户每次的配送量小于车辆容量,建立了满载运输和非满载运输混合的整数规划模型.针对第三方库存-路线问题的复杂性,本文设计嵌入禁忌搜索的遗传算法来同时决策库存和路线问题.首先对配送间隔进行编码,然后用禁忌搜索法计算每天需要配送的车辆路线问题.最后与其下界值进行比较,结果表明该算法是一个有效的算法,不但第三方能取得较低的运营总成本和较高的车辆利用率,而且也能为客户节约库存空间.     

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。     

考虑道路可靠性的突发事件资源配送路线优化模型与算法

针对突发事件后道路网络的不确定性,定义了物资配送路线的风险度量值,然后建立考虑道路风险性的物资配送的优化模型以给出最优的路线安排方案,设计了基于禁忌搜索的模型求解算法,以某城市地震灾难后应急资源配送案例进行模型仿真.模型与算法的研究对于突发事件不确定道路网络下应急资源的配送决策具有很好的指导意义和实际意义.     

战时备件配送的MDVRPTW问题研究

战时备件配送的车辆调度是提高装备保障效率的关键因素.以装备效能损失最小化为车辆调度的目标,建立了问题的M DVRPTW模型,并应用蚁群算法对问题进行了求解.算法中,根据问题特征改进了状态转移规则,设计了串行和并行两种路线构造方法,并应用局部搜索模块对蚂蚁构造的路线进行改进.对算例的计算实验表明,串行路线构造方法在精度和速度两方面均优于并行路线构造方法.     

基于实际路网情境的配送车辆调度优化

在实际路网情境下结合车道数、车道宽度、路口信号灯设置等路网物理特性,构建了考虑综合交通阻抗的多车型车辆调度模型,提出了两阶段求解策略:第1阶段设计了改进A-star精确解算法用于计算客户时间距离矩阵;第2阶段针对实际路网的特征设计了混合模拟退火算法求解调度方案。以大连市某配送中心运营实例进行路网情境仿真试验,结果表明:改进A-star算法较改进Dijkstra算法具有更短的路径搜索时间;混合模拟退火算法求解结果较实际调度方案优化了13.1% 的综合成本;路网增流、区域拥堵和路段禁行三类路网情境均能对配送方案的车辆配置、路径选择、客户服务次序、作业时间和违约费用等5方面内容产生干扰,调度计划的制定需要详细考虑这些因素的变化。     

Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization

The car sequencing problem involves scheduling cars along an assembly line while satisfying capacity constraints. In this paper, we describe an Ant Colony Optimization (ACO) algorithm for solving this problem, and we introduce two different pheromone structures for this algorithm: the first pheromone structure aims at learning for “good” sequences of cars, whereas the second pheromone structure aims at learning for “critical” cars. We experimentally compare these two pheromone structures, that have complementary performances, and show that their combination allows ants to solve very quickly most instances.     

Bus driver duty optimization using an integer programming and evolutionary hybrid algorithm

This paper describes the details of a successful application where an integer programming and evolutionary hybrid algorithm was used to solve a bus driver duty optimization problem. The task is NP-hard, therefore theoretically optimal solutions can only be calculated for very small problem instances. Our aim is to obtain solutions of good quality within reasonable time limits. We first applied an integer programming approach to a set partitioning problem. The model was solved with a column generation algorithm in a branch and bound scheme. In order to solve larger real-life problems, we have combined the integer programming method with a greedy 1+1 steady state evolutionary algorithm. The resulting hybrid algorithm was capable of providing near-optimal solutions within reasonable timescales to larger instances of the bus driver scheduling problem. We present the results and running times of our algorithm in detail, as well as possible directions of future improvements.     

多次分组会议安排的最佳混合方案

本文在仔细分析问题条件和要求的基础上,运用了运筹学、图论、矩阵理论和置换等方面的知识和技巧,建立了一个布尔规划模型。     

An iterated greedy metaheuristic for the blocking job shop scheduling problem

In this paper we consider a job shop scheduling problem with blocking (BJSS) constraints. Blocking constraints model the absence of buffers (zero buffer), whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling with swap allowed (BWS) and the one with no swap allowed (BNS). This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. A comparison with recent published results shows that the iterated greedy algorithm outperforms other state-of-the-art algorithms on benchmark instances. Moreover it is conceptually easy to implement and has a broad applicability to other constrained scheduling problems.     

Contributions to a Theory of Patrol Scheduling

The problem considered in this paper is that of scheduling police patrols in a random pattern. This involves generating patrol routes as well as schedules for dispatching patrol vehicles. A solution to this problem is obtained by specifying minimum average patrol requirements on each route segment in a network and then developing a procedure which meets these requirements while minimizing the total patrol effort. Introducing vehicles into the network in a Poisson stream results in Poisson streams in each route segment and so ensures that an observer cannot use previous history for predicting arrival patterns. This solution also has the property that the number of patrol cars in the network is a Poisson random variable for which the steady-state can be achieved immediately. The steady-state distribution function is also used to determine the number of patrol cars required.     

A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

Rising vehicles number and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility to reduce private car usage around the world. In this paper, we present a multi-agent based self-adaptive genetic algorithm to solve long-term car pooling problem. The system is a combination of multi-agent system and genetic paradigm, and guided by a hyper-heuristic dynamically adapted by a collective learning process. The aim of our research is to solve the long-term car pooling problem efficiently with limited exploration of the search space. The proposed algorithm is tested using large scale instance data sets. The computational results show that the proposed method is competitive with other known approaches for solving long-term car pooling problem.     

考虑占位决策的围堵嫌犯模型

研究了2011年中国大学生数学建模竞赛B题的突发事件中交巡警对在逃嫌犯的围堵问题。不同于对该问题的以往的研究,本文考虑了交巡警在包围圈中可以占据某些路口,使得嫌犯不能通过这些被交巡警占据的路口,从而为形成包围圈的交巡警赢得更多时间。利用两篇相关文献的关于点截集判断的结论和考虑占位决策的建模方法,以不同的目标函数建立了考虑占位决策的围堵嫌犯问题的三个混合0-1非线性整数规划模型。通过选取部分线性约束和目标函数一起组合成混合0-1线性整数规划模型,设计了基于混合0-1线性整数规划方法的算法,并给出了算例。     

An exact algorithm for the fixed-charge multiple knapsack problem

We formulate the fixed-charge multiple knapsack problem (FCMKP) as an extension of the multiple knapsack problem (MKP). The Lagrangian relaxation problem is easily solved, and together with a greedy heuristic we obtain a pair of upper and lower bounds quickly. We make use of these bounds in the pegging test to reduce the problem size. We also present a branch-and-bound (B&B) algorithm to solve FCMKP to optimality. This algorithm exploits the Lagrangian upper bound as well as the pegging result for pruning, and at each terminal subproblem solve MKP exactly by invoking MULKNAP code developed by Pisinger [Pisinger, D., 1999. An exact algorithm for large multiple knapsack problems. European Journal of Operational Research 114, 528–541]. As a result, we are able to solve almost all test problems with up to 32,000 items and 50 knapsacks within a few seconds on an ordinary computing environment, although the algorithm remains some weakness for small instances with relatively many knapsacks.