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针对面向空间众包平台的多工作者多任务路径规划问题,以求解时间成本和路程成本最小的全局最优路径规划方案为目标,提出了基于改进狮群进化算法的路径规划方法.首先,结合现实问题场景,提出带有任务开始点和结束点的路径规划模型;其次,借鉴狮群进化算法的思想,改进狮群智能行为,引入驱逐行为,针对求解问题设计染色体编码方式、交叉、变异操作等,提出了面向空间众包平台的多工作者多任务路径规划的改进狮群进化算法;最后,运用改进狮群进化算法求解面向空间众包平台的多工作者多任务路径规划模型,并根据真实数据集制作问题算例进行测试.实验结果表明了算法的可用性和有效性. 相似文献
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在第四方物流(4PL)承担多个供需点对之间物流任务的情况下,针对处理时间的不确定性,研究考虑正态分布处理时间的4PL多到多网络设计问题。建立了以最小化总物流成本为目标,带有随机配送时间约束的4PL多到多网络设计机会约束规划模型。根据问题特点,设计差分进化算法进行求解,并对其进行改进。最后,通过对不同规模的问题进行仿真实验来证明模型的合理性及算法的有效性。 相似文献
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面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力. 相似文献
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为了降低物流系统的运营成本,提高物流系统的运作效率,构建了物流系统运营成本最小以及顾客时间满意度最大的多目标物流节点选址模型,并在模型求解过程中针对多目标粒子群算法的不足,从外部存档的更新、粒子学习样本的选择以及粒子的变异三个方面进行改进,将改进的多目标粒子群算法用于物流节点选址模型的求解。仿真结果表明,改进的算法相较于其他优化算法,具有较好的分布性和收敛性。 相似文献
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EMOEA/D-DE算法在卫星有效载荷配置中的应用 总被引:1,自引:0,他引:1
针对卫星有效载荷配置问题,提出了一种基于差分进化分解的改进多目标优化算法(EMOEA/D-DE)的有效载荷配置模型。该模型将配置问题转化为以卫星数、卫星冗余度为目标的多目标优化问题(MOP),并采用EMOEA/D-DE进行求解。此外,针对随机均匀初始化会导致种群在目标空间分布过于集中的问题,采用与优化目标相结合的随机初始化方法进行改进。实验结果表明,该模型所求解集的平均差异性在0.05以内,分布度值在0.9以上,具有较好的稳定性及分布性,且改进后的算法收敛速度提升近1倍,所求解的近似Pareto前沿相对更优。 相似文献
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针对考虑残次品的多生产商选择多商品多阶段库存配送问题,建立了一个基于动态规划的双层库存配送模型。高端物流服务集成商以整个供应链网络成本最小为目标制定采购决策;库存配送服务商以运营成本最小为目标,在集成商决策下制定库存和配送决策。设计了模糊随机环境下基于动态规划的双层全局-局部-邻域粒子群算法(Bi-DPGLNPSO)对模型进行求解。并通过算例验证模型和算法的有效性和合理性。通过参数测试和算法对比检验算法的优越性。 相似文献
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针对目前对于动态车辆调度问题的研究仅集中于考虑时间依赖或依概率变化的情形,在对原有动态车辆调度问题模型进行总结的基础上,综合考虑了时间依赖且网络依概率变化,以及结合带有时间窗和随机需求的情况,提出了新的问题模型,并提出求解该问题模型的多目标随机机会约束规划模型,设计了用遗传算法解决该模型的方案与步骤。实验结果表明,所提出的模型可有效地拟合交通状况,设计的算法可以有效地求解该模型。 相似文献
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针对大规模救援物资调运的多目标中转运输网点定位问题,考虑运输费用、中转网点的作业变动费用和运输时间,建立一个救援物资中转运输网点的非线性多目标混合整数规划模型。为有效求解该模型,提出一种基于矩阵编码的遗传算法,利用费用矩阵标杆的寻优导向信息提高遗传变异算子的局部搜索能力,提高全局收敛速度。通过算例分析验证该模型和算法的有效性。 相似文献
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Behnam Vahdani Saleh Dehbari Mahdi Naderi-Beni Esmaeil Zeinali Kh 《Neural computing & applications》2014,25(7-8):1887-1902
In this paper, a logistics network is investigated which includes multi-suppliers, collection centers, transfer stations, treatment stations, and products. For this purpose, a multi-objective mathematical programming model is proposed that minimizes the total costs including the fixed costs for opening facilities and transportation costs between facilities, minimizes the distance between each waste-generating facilities and transfer stations, maximizing the distance between treatment and disposal stations and customer zones, and maximizes the sum of the reliability of coverage for the potential facilities which will be open. In order to make the results of this paper more realistic, a case study in the iron and steel industry has been investigated. Besides, a new solution approach is proposed by combining fuzzy possibilistic programming, stochastic programming, and fuzzy multi-objective programming. Moreover, an imperialist competitive algorithm is proposed to obtain near optimal solution in comparison with other evolutionary algorithms. Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model and solution approaches. 相似文献
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针对电热综合能源系统由于风电出力的随机性和波动性而难以有效调度的问题,提出了以成本最小化和弃风最小化为目标的一种多目标两阶段随机规划方法(multi-objective and two-stage stochastic programming,MOTSP),其中采用两阶段的随机规划模型对成本最小化部分进行建模分析,第一阶段以火电机组的启停成本为调度目标,第二阶段以机组运行成本为调度目标。最后采用多目标算法NSGA-Ⅱ中对解的筛选机制求解随机规划问题。该方法利用高斯分布描述负荷和风力发电预测误差来解决风电出力的不确定性,采用蒙特卡罗方法生成随机场景,并采用反向缩减技术对场景进行削减。仿真结果表明,所提的MOTSP算法比其他多种智能算法的解集更均匀广泛,收敛性更好,能够最大限度地减少弃风并使机组运营成本最小。 相似文献
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针对农村快递网点运营成本高、网点建设滞后导致的电商物流配送成本高问题,提出了城乡客运班车+无人机的快递配送模式。在考虑了配送过程中路网交通的时变特性的情况下,以无人机—车辆配送系统总成本最小为优化目标,建立了时变网络下带时间窗的无人机—车辆路径问题(TDVRPDTW)模型,并提出一个由基于最近邻思想的改进CW算法和动态规划启发式算法构成的两阶段启发式算法来求解TDVRPDTW。最后,通过算例求解验证构建模型的合理性和求解算法的有效性,为制定农村物流配送的城乡客运班车+无人机快递配送方案提供决策支持。 相似文献
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In this paper, we present a reliable model of multi-product and multi-period Location-Inventory-Routing Problem (LIRP) considering disruption risks. An inventory system with stochastic demand in which the supply of the product is randomly disrupted in distribution centers, is considered in this paper. Partial backordering is used in case stock out occurs by considering the probability of the confronting defects in distribution centers in time of disruption. We presented a bi-objective mixed-integer nonlinear programming (MINLP) model. The first objective minimizes the locating, routing and transportation costs and inventory components which consist of ordering, holding and partial backordering costs. The second objective is to minimize the total failure costs related to disrupted distribution centers that leads to reliability of the supply chain network. Because of NP-hardness of the proposed model, we modified Archived Multi-Objective Simulated Annealing (AMOSA) meta-heuristic algorithm to solve the bi-objective problem in large scales and compared the results with three other algorithms. To improve performance of the algorithms Taguchi method is used to tune parameters. Finally, several numerical examples are generated to evaluate solution methods and five multi-objective metrics are calculated to compare results of the algorithms. 相似文献
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Zijian Guo Wenyuan Wang Guolei Tang Jun Huang 《Frontiers of Computer Science in China》2011,5(4):486-495
Backlogged empty containers have gradually turned into a serious burden to shipping networks. Empty container allocation has
become an urgent settlement issue for the container shipping industry on a global scale. Therefore, this paper proposes an
improved immune algorithm based recursive model for optimizing static empty container allocation which integrates with the
global maritime container shipping network. This model minimizes the operating and capital costs during container shipping
considering 0–1 mixed-integer programming. So an immune algorithm procedure based on a special twodimensional chromosome encoding
is proposed. Finally, computational experiments are performed to optimize a 10-port static empty container shipping system.
The results indicate that the proposed recursive model for static empty container allocation is effective in making an optimal
strategy for empty container allocation. 相似文献
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In this paper, a multi-project scheduling in critical chain problem is addressed. This problem considers the influence of uncertainty factors and different objectives to achieve completion rate on time of the whole projects. This paper introduces a multi-objective optimization model for multi-project scheduling on critical chain, which takes into consideration multi-objective, such as overall duration, financing costs and whole robustness. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of different objectives. To respond to this need, a cloud genetic algorithm is proposed. This algorithm using randomness and stability of Normal Cloud Model, cloud genetic algorithm was designed to generate priority of multi-project scheduling activities and obtain plan of multi-project scheduling on critical chain. The performance comparison shows that the cloud genetic algorithm significantly outperforms the previous multi-objective algorithm. 相似文献
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This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality. 相似文献
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Cheng-Ji Liang Min Chen Mitsuo Gen Jungbok Jo 《Journal of Intelligent Manufacturing》2014,25(5):1013-1024
Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem. 相似文献