“公共自行车服务系统”优秀论文点评

<正>1 2013年"高教社杯"全国大学生数学建模竞赛D题公共自行车作为一种低碳、环保、节能、健康的出行方式,正在全国许多城市迅速推广与普及。在公共自行车服务系统中,自行车租赁的站点位置及各站点自行车锁桩和自行车数量的配置,对系统的运行效率与用     

作业车间调度问题的改进混合遗传算法

作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性.     

混合教与学算法求解多行程车辆路径问题

为了求解带容量约束的车辆路径问题,提出了一种混合教与学优化算法.该算法基于标准的教与学算法,结合基于禁忌搜索算法的局部优化方法,力求进一步强化标准教与学算法的寻优能力.最后通过引入标准数据集,进行了仿真实验并给出了实验分析,测试结果验证了构建的混合教与学优化算法相比其他三种优化算法搜索性能较强,与最优解偏差最小,能够有效地应对离散优化问题.     

基于随机活动工期的现金流动态均衡前摄性与反应性项目调度优化

承包商的现金流动态均衡对不确定条件下项目的顺利实施有重要影响。作者研究基于随机活动工期的现金流动态均衡前摄性及反应性项目调度问题,目标是在随机活动工期条件下,为承包商生成现金流均衡基准进度,并根据执行过程中的实际情况,动态地对其进行反应性调整。首先,通过建立前摄性调度优化模型生成基准进度,并提出两个反应性调度策略对其进行调整。其次,为以上诸模型的求解设计了模拟退火和禁忌搜索相结合的混合算法tabu-SA。最后,针对前摄性调度模型,在随机生成的算例集合上对算法进行测试,并进行大规模仿真实验。研究结果可以为随机活动工期下承包商保持现金流动态均衡、确保项目顺利实施,提供定量化决策支持。     

基于混合算法的实时订货信息下的车辆调度优化

实时订货信息下的车辆调度是随机性车辆调度中货物需求量、需求点均不确定的情况下的车辆调度.针对该问题,本文构建了配送总成本最小的目标函数,提出了采用混合算法求解的思路.即以局部搜索法求得初始解,采用遗传算法优化初始解,并在送货时间更新后,利用禁忌搜索法求解速度快的特点改进调度方案,得到订货信息不断更新的条件下的车辆调度方案.通过实例分析,本方法既可解决电子商务条件下实时订货的车辆调度问题,也具有求解结果可靠、求解过程快速的特点.     

现金押运路线问题——基于多类型的风险

以人民币现金押运为研究背景,考虑了一种基于多类型风险的现金押运路线问题,以在途风险成本、库存现金风险成本以及运输成本为优化目标,建立了混合整数线性规划模型,并提出了一种基于多样化策略和改进邻域搜索的混合遗传算法,其中遗传算法对押运路线进行选择,贪心算法用来求解各类风险指标。数值实验分别对问题特性和算法性能进行了分析。实验结果表明:1)混合遗传算法能求解更大规模的问题,得到较好的解,并很好地平衡了运行时间和求解质量;2)多类型风险影响了行驶路线;3)客户的期望需求影响了库存现金风险。     

大型公交网络线路查询模型与算法

分析了大型城市公交网络的特点,为满足乘客出行时各种不同的需求,综合考虑换乘次数、出行时间与乘车费用等多种不同因素,通过构造线路与站点、站点与站点的连接矩阵,结合矩阵算法与搜索算法的优点,提出了一种分类多目标优化搜索算法.该算法搜索时间较短,能够生成多条备选路径供出行者选择,能基本满足自主查询计算机系统的需要.     

分层混合局部搜索策略异构多核系统调度

针对遗传算法解决异构多核系统的任务调度问题容易产生早熟现象及其局部寻优能力较差的缺点,将局部搜索算法与遗传算法相结合,创新性地提出一种求解异构多核系统的任务调度问题的分层混合局部搜索遗传算法。该算法提出一种新的分层优化策略以产生初始种群,在变异操作中,对部分个体设计3-opt优化变异,对种群中的优秀个体用改进的Lin-Kernighan算法进行优化。仿真实验结果表明,分层混合局部搜索遗传算法求解异构多核系统的任务调度问题时可以高效获得高质量的解。     

带时间窗的三维装载物流配送优化方法研究

针对城市物流配送优化研究在客户服务时间窗和货物装载方式合理结合方面存在的不足,考虑物流配送车厢货物装载方式与客户访问序列相关的特征对车厢空间进行合理的区域划分。首先,构建了包含配送中心的固定成本、配送车辆的运输成本、维修成本、租赁成本和违反时间窗惩罚成本的物流运营成本最小化和配送车辆空间利用率最大化的双目标优化模型;然后,提出一种结合遗传算法(GA)全局搜索能力和禁忌搜索算法(TS)局部搜索能力的GA-TS混合算法求解模型;最后,结合重庆市某配送中心的三维装载物流配送实例数据进行了优化计算,实验结果给出了带时间窗的三维装载物流配送路径优化方案,并进行了不同车厢空间分区模式下平均装载率、物流运营成本和车辆使用数的比较分析。研究表明,当客户需求货物种类数与车辆的空间区域划分数相等且按货物类型进行区域划分时,物流运营成本最小,配送车辆使用数最少和车辆平均装载率最高。     

多车型多品种货物配装优化问题的混合启发式算法

为满足客户多样化和个性化的需求,建立能充分、均衡利用装载工具的载重和容积的多品种、多车型货物配装模型,并从全局、整体最优上设计混合启发式算法求解。首先,采用实数序列编码,使问题变得更简洁;基于容重比平衡法构建初始解,提高了解的可行性;用基于排序的选择与最佳保留相结合策略,保证群体的多样性;采用改进的非一致变异,加强染色体的局部搜索能力;其次,对遗传算法求得的精英种群再进行禁忌搜索,提高了搜索效率;最后,通过实例计算证明了上述模型和算法的有效性,并为大规模解决实际问题提供思路。     

考虑多仓库的共享单车重新配置问题研究

共享单车是我国大力提倡的低碳交通出行模式,加快共享单车发展是解决最后一公里、城市拥堵和环境污染等问题的重要途径。由于人们停放共享单车的无规律性,使得共享单车系统中各车桩的单车库存量存在不平衡。如何合理的对车桩中的单车进行重新调配,来满足用户的需求,是相关企业亟待解决的问题。共享单车的调配路线优化是优化车桩库存量的重要手段之一。本文研究多仓库条件下的货车调配路线优化问题,建立了一个混合整数非线性规划模型。不同于传统的路径优化问题的研究大多是以成本或时间为目标,本文采用基于车桩库存量的非线性惩罚函数来表示用户需求,从而使得所研究的问题是一个凸函数优化问题。为了简化本文的问题,将目标函数分段线性化。基于车桩网络的特点,设计了变邻域搜索算法,以及构建初始解的贪婪算法。最后,以某共享单车公司为例,进行算例分析,来说明模型和算法的合理性和有效性。     

Scatter tabu search for multiobjective clustering problems

We propose a hybrid heuristic procedure based on scatter search and tabu search for the problem of clustering objects to optimize multiple criteria. Our goal is to search for good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple application areas. Our procedure can be viewed as an extension of SSPMO (a scatter search application to nonlinear multiobjective optimization) to which we add new elements and strategies specially suited for combinatorial optimization problems. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the operational research literature. Our scatter tabu search implementation is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation to show that our method is capable of delivering good approximations of the efficient frontier for improved analysis and decision making.     

A novel metaheuristics approach for continuous global optimization

This paper proposes a novel metaheuristics approach to find the global optimum of continuous global optimization problems with box constraints. This approach combines the characteristics of modern metaheuristics such as scatter search (SS), genetic algorithms (GAs), and tabu search (TS) and named as hybrid scatter genetic tabu (HSGT) search. The development of the HSGT search, parameter settings, experimentation, and efficiency of the HSGT search are discussed. The HSGT has been tested against a simulated annealing algorithm, a GA under the name GENOCOP, and a modified version of a hybrid scatter genetic (HSG) search by using 19 well known test functions. Applications to Neural Network training are also examined. From the computational results, the HSGT search proved to be quite effective in identifying the global optimum solution which makes the HSGT search a promising approach to solve the general nonlinear optimization problem.     

A simple and effective algorithm for the MaxMin diversity problem

The challenge of maximizing the diversity of a collection of points arises in a variety of settings, including the setting of search methods for hard optimization problems. One version of this problem, called the Maximum Diversity Problem (MDP), produces a quadratic binary optimization problem subject to a cardinality constraint, and has been the subject of numerous studies. This study is focused on the Maximum Minimum Diversity Problem (MMDP) but we also introduce a new formulation using MDP as a secondary objective. We propose a fast local search based on separate add and drop operations and on simple tabu mechanisms. Compared to previous local search approaches, the complexity of searching for the best move at each iteration is reduced from quadratic to linear; only certain streamlining calculations might (rarely) require quadratic time per iteration. Furthermore, the strong tabu rules of the drop strategy ensure a powerful diversification capacity. Despite its simplicity, the approach proves superior to most of the more advanced methods from the literature, yielding optimally-proved solutions for many problems in a matter of seconds and even attaining a new lower bound.     

Complete Local Search with Memory

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic that makes effective use of memory structures in a way that is different from that in common implementations of tabu search. We report computational experiments with this heuristic on the traveling salesperson problem and the subset sum problem.     

On the Hardness of the Quadratic Assignment Problem with Metaheuristics

Meta-heuristics are a powerful way to approximately solve hard combinatorial optimization problems. However, for a problem, the quality of results can vary considerably from one instance to another. Understanding such a behaviour is important from a theoretical point of view, but also has practical applications such as for the generation of instances during the evaluation stage of a heuristic.In this paper we propose a new complexity measure for the Quadratic Assignment Problem in the context of metaheuristics based on local search, e.g. simulated annealing. We show how the ruggedness coefficient previously introduced by the authors, in conjunction with the well known concept of dominance, provides important features of the search space explored during a local search algorithm, and gives a rather precise idea of the complexity of an instance for these heuristics. We comment previous experimental studies concerning tabu search methods and genetic algorithms with local search in the light of our complexity measure. New computational results with simulated annealing and taboo search are presented.     

A Hybrid Particle Swarm Optimization Algorithm for the Redundancy Allocation Problem

The Redundancy Allocation Problem generally involves the selection of components with multiple choices and redundancy levels that produce maximum system reliability given various system level constraints as cost and weight. In this paper we investigate the series–parallel redundant reliability problems, when a mixing of components was considered. In this type of problem both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximise the reliability of system. A hybrid algorithm is based on particle swarm optimization and local search algorithm. In addition, we propose an adaptive penalty function which encourages our algorithm to explore within the feasible region and near feasible region, and discourage search beyond that threshold. The effectiveness of our proposed hybrid PSO algorithm is proved on numerous variations of three different problems and compared to Tabu Search and Multiple Weighted Objectives solutions.     

P-枢纽航线网络设计问题的一种启发式算法

针对无容量限制的多重分派枢纽中位问题(UMApHMP),提出了一种基于禁忌搜索和最短路算法的新的启发式算法。利用CAB基准数据对该算法进行了验证,计算结果表明所提算法具有较强寻优能力和较快的求解效率。     

Metaheuristics approach for solving personalized crew rostering problem in public bus transit

The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.     

基于禁忌搜索算法的枢纽航线网络优化设计研究

首先建立了非严格意义上的无容量限制的多重分派p-枢纽中位问题(NSUMApHMP)的混合整数线性规划模型.然后提出了一种基于禁忌搜索和最短路算法解决NSUMApHMP的新的启发式算法.最后利用基准数据对该算法进行了验证.计算结果表明,该算法具有较强寻优能力和较快的求解效率.