考虑箱区利用率的出口箱资源配置启发式算法

作为集装箱运输的枢纽,堆场的管理是整个集装箱码头管理的重中之重。对出口集装箱堆场的资源配置研究可以提高出口集装箱的装载效率,提高集装箱码头堆场的作业水平。针对当前堆场资源配置研究中较少考虑箱区作业量平衡与码头运营成本关系的研究现状,在不同箱区作业量差异水平下,设计了以成本最低为目标的出口箱堆场资源配置启发式算法。进行了不同规模的算例实验,并进行了比较。     

一种带软时间窗的物流配送路径优化遗传算法

本文在建立物流配送路径优化问题模型的基础上,采用最佳保留的轮盘赌复制法、最大保留交叉法、交叉率和变异率自适应调整等技术,设计了基于自然数编码的遗传算法,并进行了实验计算。结果表明,用该算法进行带软时间窗的非满载车辆调度优化问题,可以方便有效地求得满意解。     

基于改进遗传算法的有时间窗车辆调度问题研究*

在分析带有时间窗车辆调度问题的基础上,建立了车辆调度问题的数学模型,并构造了不同时间窗的惩罚函数.设计了针对车辆调度问题基于自然数编码的遗传算法,并改进了传统的交叉运算,避免优秀基因在交叉操作中被破坏,提高了遗传算法的寻优能力.最后,结合算例进行了仿真计算,分析了载重体积约束和时间窗约束对车辆调度的影响,验证了算法的有...     

集装箱码头送箱外集卡预约优化研究

通过预约缓解集装箱码头拥堵是提高港口运作效率的有效途径.考虑集卡公司和码头运营商双方的利益以及码头内部作业系统的复杂性,以减小外集卡在预约时间窗内的平均排队长度和集卡公司期望到达的预约时间窗与被调配到的预约时间窗间的差异为目标,运用排队论相关知识和逐点固定流体近似方法(PSFFA),建立了多目标规划模型,以确定一个使集...     

基于改进遗传算法的带时间窗VRP问题研究

带时间窗约束的VRP问题(VRPTW)属于NP-hard问题,采用改进遗传算法探索最优方案。首先分析了带时间窗VRP问题的一般数学模型,并采用罚函数的方法对时间窗约束进行处理;设计了带权重的适应度函数,并采用了基于基因库的跨世代精英选择算子、PMX交叉算子和局部爬山变异算子;最后通过仿真实验与传统遗传算法和自适应遗传算法进行了对比研究,仿真结果表明改进遗传算法在解决带时间窗VRP问题中具有较高收敛速度和全局搜索能力。     

基于优化时间窗的用户兴趣漂移方法

针对个性化服务系统中如何跟踪用户兴趣变化和调整用户兴趣模型的问题,提出基于优化时间窗的用户兴趣漂移算法。利用分类错误率的变化跟踪用户兴趣的漂移,当用户兴趣发生变化时,通过优化时间窗算法自动调节时间窗的大小,以达到调整用户兴趣模型的目的。实验表明,该方法能较为准确地描述用户的兴趣变化,提高个性化信息服务的效率。     

考虑时间窗的集装箱港口场桥全局调度优化

合理高效的场桥调度计划有助于减少场桥与集卡相互等待的时间从而提高港口的运营效率,考虑到在实际操作中会出现多箱区多场桥同时工作、互相冲突等情况,建立了以场桥移动成本和延误成本最小化为目标的数学模型,利用计划时间段和时间窗的概念对场桥作业进行约束,通过遗传算法编码进行求解,并将计算结果与实际操作及其他算法的优化结果相对比,进而验证该模型和算法的有效性和稳定性。     

基于异时间窗划分的时间序列聚类

针对相同时间窗对时间序列进行子序列划分的缺点,提出一种异时间窗的子序列划分方法。为解决划分得到的子序列长度不同,而使用动态时间弯曲算法进行子序列相似性度量的计算速度慢的问题,给出一种不规则时间序列距离度量算法。对异时间窗的子序列划分方法和不规则时间序列距离度量算法进行了实验,结果证明了二者的优越性。     

有时间窗约束车辆路径问题的改进遗传算法

将遗传算法与禁忌搜索结合起来,设计了一种改进的遗传算法求解有时间窗约束车辆路径问题。采用启发式插入算法产生较优良的遗传操作初始种群,通过改进的逆转变异算子更多继承父代的优良性能,以提高遗传算法的计算效率。引入海明距评估遗传进化中种群的多样性。当种群多样性低到一定程度时转入禁忌搜索,以避免遗传算法早熟的缺陷,最终实现全局优化。通过算例验证了该算法的优越性。     

灾变遗传算法求解带时间窗的车辆调度问题

提出一种可以有效求解带时间窗的车辆调度问题的灾变遗传算法.遗传算法作为一种高效的启发式算法被用于解决这类组合优化问题,但是该算法存在过早收敛、易陷入局部最优等缺陷.针对此问题,在搜索过程中采用灾变算子使遗传算法跳出局部最优,并针对车辆调度问题设计一种可以直接产生可行解的交叉算子,避免染色体交叉过程中产生不可行的子代.通过仿真算例验证了所提出的算法求解带时间窗的车辆调度问题的有效性;通过与标准遗传算法、改进遗传算法和粒子群算法的比较,进一步验证了灾变遗传算法在优化性能以及算法鲁棒性方面的优势.     

Re-marshaling export containers in port container terminals

In order to speed up the loading operation of export containers onto a ship, the re-marshaling operation is an usual practice in port container terminals. It is assumed that the current yard map for containers is available and a desirable bay layout is provided. A methodology is proposed to convert the current bay layout into the desirable layout by moving the fewest possible number of containers and in the shortest possible travel distance. The problem is decomposed into three sub-problems such as the bay matching, the move planning, and the task sequencing. The bay matching is to match a specific current bay with a bay configuration in the target layout. In the move planning stage, the number of containers to be moved from a specific bay to another is determined. The completion time of the re-marshaling operation is minimized by sequencing the moving tasks in the final stage. A mathematical model is suggested for each sub-problem. A numerical example is provided to illustrate the solution procedure.     

Schedule design for sustainable container supply chain networks with port time windows

This paper studies a practical liner shipping schedule design problem with port time windows for container supply chain networks. A mixed-integer nonlinear non-convex model that incorporates the availability of ports is proposed to minimize the sum of ship cost and fuel cost (and thereby pollutant emission). In view of the structure of the problem, we reformulate it as an integer linear optimization model and propose an iterative optimization approach. The proposed solution method is applied to two liner networks operated by a global shipping line.     

Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm

The present study investigates the cost concerns of distribution centers and formulates a vehicle routing problem with time window constraints accordingly. Based on the embedded structure of the original problem, a decomposition technique is employed to decompose the original problems to a clustering problem (main problem) and a set of traveling salesman problems (sub-problems) with time window constraints. This decomposition not only reduces the problem size but also enable the use of simpler solution procedures. A genetic algorithm is developed to solve the clustering problem, while a simple heuristic algorithm is formulated to solve the set of traveling salesman problems. The solution of the original problem is obtained through iterative interactions between the main problem and the set of sub-problems. The performance of the proposed approach is compared with the well-known insertion method and a manual scheduling of a distribution center.     

A hybrid genetic algorithm for multi-depot homogenous locomotive assignment with time windows

This paper presents a hybrid genetic algorithm to solve a multi-depot homogenous locomotive assignment problem with time windows. The locomotive assignment problem is to assign a set of homogeneous locomotives locating in a set of dispersed depots to a set of pre-schedules trains that are supposed to be serviced in pre-specified hard/soft time windows. A mathematical model is presented, using vehicle routing problem with time windows (VRPTW) for formulation of the problem. A cluster-first, route-second approach is used to inform the multi-depot locomotive assignment to a set of single depot problems and after that we solve each problem independently. Each single depot problem is solved heuristically by a hybrid genetic algorithm that in which Push Forward Insertion Heuristic (PFIH) is used to determine the initial solution and λ-interchange mechanism is used for neighborhood search and improving method. A medium sized numerical example with different scenarios is presented and examined to more clarification of the approach as well as to check capabilities of the model and algorithm. Also some of the results are compared with the solutions produced by branch & bound technique to determine validity and quality of the model. The experiments with a set of 15 completely random generated instance problems indicate that this algorithm is efficient and solves the problem in a polynomial time.     

有时间窗车辆路径问题的捕食搜索算法

有时间窗车辆路径问题是当前物流配送系统研究中的热点问题,该问题具有NP难性质。难以求得最优解或满意解,在建立有时间窗车辆路径问题数学模型的基础上。设计了一种模仿动物捕食策略的捕食搜索算法．该算法利用控制搜索空间的限制大小来实现算法的局域搜索和全局搜索,具有良好的局部集中搜索和跳出局部最优的能力．通过实例计算,并与相关启发式算法比较．取得了满意的结果．     

带时间窗车辆路径问题的多目标文化基因算法

针对物流配送中带时间窗的车辆路径问题,以最小化车辆使用数和行驶距离为目标,建立了多目标数学模型,提出了一种求解该问题的多目标文化基因算法。种群搜索采用遗传算法的进化模式和Pareto排序的选择方式,局部搜索采用禁忌搜索机制和存储池的结构,协调两者得到的Pareto非占优解的关系。与不带局部搜索的多目标遗传算法和单目标文化基因算法的对比实验表明,本文算法的求解质量较高。     

求解带软时间窗车辆路径问题的融合算法

设计了遗传算法与变异蚂蚁算法的一个融合算法,该算法采用优良基因保护策略,引入蚂蚁寻径变异机制,并改进了信息素的更新方式,提高了寻径速度以及寻径的全局性。经过对比实验,验证了本融合算法可以有效而快速地获得问题模型的最优解或近似最优解。     

蜂群算法在带时间窗的车辆路径问题中的应用*

根据带时间窗车辆路径问题的实际情况,通过考察车辆数和总行程两个目标函数,给出了该问题的一种新的算法——蜂群算法。通过计算若干benchmark问题,并将结果与其他算法相比较分析,验证了算法的有效性。目前关于蜂群算法的文献较少,故不仅是拓宽蜂群算法应用范围的有效尝试,同时也给带时间窗车辆路径问题提供了一种新的解决方法。     

Variable neighborhood search algorithm for heterogeneous traveling repairmen problem with time windows

The focus of this paper is generalized traveling repairman problem (TRP), a special case of the well known and well studied traveling salesman problem (TSP). Because of its specific objective function, that minimizes the sum of overall time all clients wait for until the end of a service, TRP has great applicability potential in client oriented practical problems. Therefore it has been known in literature as traveling deliveryman problem, minimum latency problem and cumulative capacitated vehicle routing problem. However, most studies that have treated TRP related problems have implied that only one repairman is present in the system and/or that all clients are available for service at the beginning of the planning horizon. In this paper we consider a TRP with a heterogeneous fleet of repairmen serving a set of clients whose arrival times are distributed over a planning horizon, i.e. heterogeneous TRPTW (hetTRPTW). For the hetTRPTW we present a mixed integer linear programming model, and a heuristic algorithm based on a variable neighborhood search (VNS) framework. Additionally, we propose a reduction strategy for neighborhoods in the VNS algorithm and test efficiency of implemented algorithms on four benchmark sets of problem instances. Results show that proposed algorithms could be used in real systems for solving small and moderate problem instances.     

求解VSPSTW问题的混合差分演化算法

在描述带软时间窗车辆调度问题数学模型基础上,提出将模拟退火算法与差分演化算法相结合的混合优化算法求解该问题。该算法利用了模拟退火算法具有的较强局部搜索能力和差分演化算法的强全局搜索能力,克服了差分演化算法的“早期收敛”问题。实验结果表明,该算法比单一的差分演化算法计算效率高,收敛速度快,计算结果也比较稳定,是解决车辆调度问题的有效方法。