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    文仁强, 钟少波, 袁宏永, 黄全义. 应急资源多目标优化调度模型与多蚁群优化算法研究[J]. 计算机研究与发展, 2013, 50(7): 1464-1472.
    引用本文: 文仁强, 钟少波, 袁宏永, 黄全义. 应急资源多目标优化调度模型与多蚁群优化算法研究[J]. 计算机研究与发展, 2013, 50(7): 1464-1472.
    Wen Renqiang, Zhong Shaobo, Yuan Hongyong, Huang Quanyi. Emergency Resource Multi-Objective Optimization Scheduling Model and Multi-Colony Ant Optimization Algorithm[J]. Journal of Computer Research and Development, 2013, 50(7): 1464-1472.
    Citation: Wen Renqiang, Zhong Shaobo, Yuan Hongyong, Huang Quanyi. Emergency Resource Multi-Objective Optimization Scheduling Model and Multi-Colony Ant Optimization Algorithm[J]. Journal of Computer Research and Development, 2013, 50(7): 1464-1472.

    应急资源多目标优化调度模型与多蚁群优化算法研究

    Emergency Resource Multi-Objective Optimization Scheduling Model and Multi-Colony Ant Optimization Algorithm

    • 摘要: 大规模自然灾害发生后,极易出现多地同时提出多类型资源需求的局面.基于灾后应急资源调度的特点,建立了考虑多需求点、多供应点、多资源类型、且多个资源供应点能为多个资源需求点协同配备资源的多目标优化调度模型.模型中对调度路线的可靠度进行了考虑,增强了实用性.设计了求解模型的多蚁群优化算法,在全局信息素更新规则中引入精英策略,指导多蚁群间相互交换与共享信息,加快全局非劣解搜索效率.多目标多蚁群优化算法将资源定位配置与路线安排问题进行了集成解决.算例分析表明该算法能够很好地处理大型复杂网络.

       

      Abstract: Multi-types of emergency resource requirements have been put forward from many disaster-stricken areas after large-scale natural disaster broke out. A multi-objective optimization scheduling model is proposed, which takes into account multiple demand centers, multiple supply centers, multi-types of resources, and supply centers cooperating with each other in providing resources to demand centers. The reliability of scheduling routs is taken into account in the model to enhance the practicability. An optimization algorithm based on multiple ant colony system is designed to solve the model. Then the elite strategy is introduced into the globe pheromone update strategy to guide exchanging and sharing information among multiple ant colony systems, and improve the effect in searching globe no-inferior solutions. Next, a practical approach is provided to solve resources location-allocation problem and scheduling routes planning problem as one integrated problem. Finally the practical example is presented to verify the validity of the model and algorithm, and it is shown that the algorithm can deal with large complex networks well.

       

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