Optimization of Reliable Routes for Multimodal Transport of
Emergency Supplies Under Dual Uncertainty

doi:10.16097/j.cnki.1009-6744.2023.01.007

Abstract

Abstract: In order to deliver emergency supplies to destinations on time and reliably, a reliable path optimization model for multimodal transportation of emergency supplies with the greatest reliability was constructed. The model takes into account the dual uncertainties of demand and transportation environment, the risk of nodal epidemic infection, cost constraints, shift restrictions, and transshipment capacity constraints. According to the NP-hard characteristics of the problem sought, the Monte Carlo adaptive genetic algorithm and simulated annealing genetic algorithm are designed to solve them, and the superior and inferior solution distance method is introduced to analyze the operation results of the study. The results show that the Monte Carlo adaptive genetic algorithm is better than the simulated annealing genetic algorithm in terms of solution quality and solution time, and the maximum reliability of the optimized path is 85% under the optimal parameter combination of 0.80 cross probability, 0.08 mutation probability and 50 population size, and the optimal route solved does not pass through the nodes with epidemic infection risk, and the solution results are better. The parameter analysis shows that under the condition of the same cross probability, the average running time of the two algorithms decreases with the decrease of the variation probability and increases with the increase of the variation probability. The decision to optimize the route of multimodal transport is influenced by the schedule of the water and rail.

Key words: integrated transportation, emergency supplies, multimodal transportation, reliable route optimization, Monte Carlo genetic algorithm

摘要： 为按时、可靠地将应急物资运达目的地，综合考虑需求和运输环境的双重不确定性、节点疫情感染风险、成本约束、班期限制和转运能力限制等，构建以可靠度最大为目标的应急物资多式联运可靠路径优化模型。同时针对所求问题的NP-难特点，设计蒙特卡洛自适应遗传算法和模拟退火遗传算法进行求解，并引入优劣解距离法对算例的运行结果进行分析。研究结果表明：蒙特卡洛自适应遗传算法较模拟退火遗传算法在求解质量和求解时间方面更优，在交叉概率为0.80，变异概率为0.08，种群大小为50的最佳参数组合下，得到的优化路径最大可靠度为85%，且求解出来的最优路线均未经过存在疫情感染风险的节点，求解结果较好。参数分析表明：在交叉概率相同的条件下，两种算法的平均运行时间均随着变异概率的降低而减少，随着变异概率的增加而增加；多式联运路径优化的决策会受水铁班期的影响。

关键词: 综合运输, 应急物资, 多式联运, 可靠路径优化, 蒙特卡洛遗传算法

CLC Number:

LIU Song, SHU Wen, PENG Yong, SHAO Yi-ming, LE Mei-long. Optimization of Reliable Routes for Multimodal Transport of Emergency Supplies Under Dual Uncertainty[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 58-66.

刘松, 舒文, 彭勇, 邵毅明, 乐美龙. 双重不确定下应急物资多式联运可靠路径优化[J]. 交通运输系统工程与信息, 2023, 23(1): 58-66.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2023.01.007

http://www.tseit.org.cn/EN/Y2023/V23/I1/58

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Optimization of Reliable Routes for Multimodal Transport of Emergency Supplies Under Dual Uncertainty

双重不确定下应急物资多式联运可靠路径优化

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