基于改进粒子群算法的应急资源动态调度方法

Dynamic Scheduling Method for Emergency Resources Based on Improved Particle Swarm Optimization Algorithm

摘要

由于传统粒子群算法在进化过程中具有较强随机性, 在搜索最优解阶段, 群体里一部分远离全局最优解的粒子会干扰进化过程的收敛走向, 致使算法容易陷入局部最优状态。 为此, 提出了基于改进粒子群算法的应急资源动态调度方法。 其以救灾调度总成本最小以及最大化满足物资实际需求为目标函数, 并建立对应的约束条件, 由此构建应急资源动态调度模型。 使用混沌运动理论对传统粒子群算法进行改进处理, 获取全局最优解, 并使用改进后的粒子群算法对调度模型进行计算, 获取最佳调度方案, 实现受灾点应急资源的精准调度。 实验结果表明, 利用该方法进行应急资源动态调度时, 物资调度数量与实际需求高度匹配, 且调度路径较短, 验证了其在实际应用中的高效性和可靠性。

Abstract

The traditional particle swarm optimization algorithm exhibits strong randomness during evolutionary process. In the stage of searching for the optimal solution, some particles in the population that deviate from the global optimum can interfere with the convergence direction of the evolutionary process, leading the algorithm to easily fall into a local optimal state. To effectively address this issue, an emergency resource dynamic scheduling method based on improved particle swarm optimization algorithm is proposed. The objective function aims to minimize the total cost of disaster relief scheduling and maximize the fulfillment of actual material demands, and corresponding constraint conditions are established to construct a dynamic scheduling model for emergency resources. Chaos motion theory is used to improve the traditional particle swarm algorithm, obtain the global optimal solution, and the improved particle swarm algorithm is used to solve and calculate the scheduling model to obtain the optimal scheduling plan, achieving precise scheduling of emergency resources in disaster stricken areas. The experimental results show that when using this method for dynamic scheduling of emergency resources, the quantity of material scheduling highly matches the actual demand, and the scheduling path length is relatively short, which verifies its efficiency and reliability in practical applications.

关键词

混沌运动理论 / 应急资源动态调度 / 粒子群优化算法 / 动态调度模型

Key words

chaos motion theory / dynamic scheduling of emergency resources / particle swarm optimization algorithm / dynamic scheduling model

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基金资助

陕西省教育厅科学研究计划基金资助项目(22JK0223)

陕西省哲学社会科学重大理论与现实问题研究2022年度一般基金资助项目(2022HZ1307)