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考虑转运的应急物资两阶段优化调度研究
首发时间：2024-06-28
摘要：供应物资受限下多供应点、多需求点、多种类的应急物资调度需要保障配送高效的同时提升各需求点的满足度。因此，通过建立以运输成本和需求满足度为目标的调度模型，设计了具有学习算子和竞争算子来动态调整参数值的进化学习算法（ela）从而提升模型的求解效果和精度，同时考虑转运来优化调度方案降低各需求点的运输成本。最后，为解决实际调度需求提出了两种不同的调度方案。实验分析表明，第一阶段提出的ela算法能较大程度的降低运输成本并提高需求满足度，与传统gsa相比，双目标优化后的调度方案使得运输成本降低13.6%，需求满足度提高18.4%。第二阶段运用节约法优化后，双目标优化下将调度方案的运输成本再降低11.1%，最大需求满足度下运输成本可降低19.5%。
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study on two-phase optimal scheduling of emergency supplies considering transshipment
abstract：the dispatching of emergency supplies with multiple supply points, multiple demand points, and multiple types of supplies under supply constraints needs to ensure efficient distribution while improving the satisfaction of each demand point. therefore, an evolutionary learning algorithm (ela) with learning and competition operators to dynamically adjust the parameter values is designed to improve the model\'s solution effect and accuracy by establishing a scheduling model with the objectives of transportation cost and demand satisfaction, while considering transshipment to optimize the scheduling scheme to reduce the transportation cost of each demand point. finally, two different scheduling schemes are proposed to address the actual scheduling needs. the experimental analysis shows that the ela algorithm proposed in the first stage can reduce the transportation cost and improve the demand satisfaction to a larger extent, and the optimized bi-objective scheduling scheme reduces the transportation cost by 13.6% and improves the demand satisfaction by 18.4% compared with the traditional gsa. in the second stage, after the optimization using the savings method, the transportation cost of the scheduling scheme is further reduced by 11.1% under the bi-objective optimization, and the transportation cost can be reduced by 19.5% under the maximum demand satisfaction.????
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考虑转运的应急物资两阶段优化调度研究
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