共查询到17条相似文献,搜索用时 780 毫秒
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蚁群算法是受自然界蚂蚁觅食过程中,基于信息素的最短路径搜索食物行为启发,提出的一种智能优化算法。在采用蚁群算法求解二次指派问题中,针对蚁群算法存在的过早收敛问题,使用距离及流量作为启发式信息并引入局部优化,对蚁群算法的结果加以改进,计算机仿真结果表明,蚁群算法对求解二次指派问题有较好的效果。 相似文献
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<正>1.基于改进蚁群算法的物流配送路径择优规划方法1.1建立目标函数物流配送路径择优规划问题,是由多个配送中心和多个客户节点组成,此次研究场景为配送中心,根据物流配送路径择优规划需求,对物流配送择优规划问题提出如下假设:假设1:用于物流配送的车辆型号相同,物流配送路程不能超出配送车辆运行的最大行驶距离;假设2:物流配送车辆均从配送中心取货, 相似文献
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应急物资分配和车辆路径选择是灾难救援研究的2个核心问题。通过分类综述国内外学者关于应急物资分配和应急车辆路径研究的模型及结论,重点分析了模型的目标函数、约束条件、算法及优缺点。在灾难救援应急物资配送问题的研究分析中,提出考虑需求不确定性、建立更符合实际的模型、探索启发式算法、结合其他理论研究等研究方向。 相似文献
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对于某一特定源点和目的地之间的车辆运输调度问题,建立基于风险、考虑成本和时变条件下的路径优化模型,采用蚁群算法的信息素更新策略,使边上残留信息素能够正确反映时变网络中边上权值的变化,并结合遗传算法,采取最优个体交叉策略将蚁群每次遍历后形成的解作为初始群种进行单点交叉计算,以避免陷入局部最优解,提高算法的收敛性。通过算例分析验证算法的有效性。 相似文献
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Shan-Huen Huang Pei-Chun Lin 《Transportation Research Part E: Logistics and Transportation Review》2010,46(5):598-611
This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem. 相似文献
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Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach 总被引:2,自引:0,他引:2
Zhixiang Fang Xinlu Zong Qingquan Li Qiuping LiShengwu Xiong 《Journal of Transport Geography》2011,19(3):443-451
Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm. 相似文献
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用蚁群算法求解类TSP问题的研究 总被引:3,自引:0,他引:3
铁路运输调度问题能否很好解决对于铁路运输公司至关重要,旅行商问题(简称TSP)经常被用来研究运输调度问题。根据某化工集团铁路运输公司的生产实际,提出了“类TSP”问题,但由于“类TSP”和TSP有很大区别,以前求解TSP的优化算法不能直接用于“类TSP”的求解。利用蚁群算法是可以较好解决TSP的一类新型模拟进化算法,适应“类TSP”的要求,并通过“蚁后规则”和变异机制的引入,提出了一种改进的人工蚁群算法。计算机仿真结果表明该算法求解“类TSP”是行之有效的。 相似文献
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One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO). 相似文献
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This paper considers the integrated recovery of both aircraft routing and passengers. A mathematical model is proposed based on both the flight connection network and the passenger reassignment relationship. A heuristic based on a GRASP algorithm is adopted to solve the problem. A passenger reassignment solution is demonstrated to be optimal in each iteration for a special case. The effectiveness of the heuristic is illustrated through experiments based on synthetic and real-world datasets. It is shown that the integrated recovery of flights and passengers can decrease both the recovery cost and the number of disrupted passengers. 相似文献
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This paper addresses the routing problem with unpaired pickup and delivery with split loads. An interesting factor of our problem is that the quantity and place for pickup and delivery are decision variables in the network. We develop an easy-to-implement heuristic in order to gain an efficient and feasible solution quickly. Then, a local search algorithm based on the variable neighborhood search (VNS) method is developed to improve the performance of the heuristic. Computational results show that the proposed VNS method is able to obtain an optimal or near optimal solution in reasonable time for the formulated problem. 相似文献
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This paper examines a reliable capacitated location–routing problem in which depots are randomly disrupted. Customers whose depots fail must be reinserted into the routes of surviving depots. We present a scenario-based mixed-integer programming model to optimize depot location, outbound delivery routing, and backup plans. We design a metaheuristic algorithm that is based on a maximum-likelihood sampling method, route-reallocation improvement, two-stage neighborhood search and simulated annealing. Numerical tests show that the heuristic is able to generate results that would keep operating costs and failure costs well balanced. Managerial insights on scenario identification, facility deployment and model simplification are drawn. 相似文献