基于改进狮群算法的低碳冷链配送中心选址研究

Research on location selection of low-carbon cold chain distribution center with improved lion swarm algorithm

受新冠疫情影响,冷链药品需求激增,为了准时完成医药物资交付,配送中心选址问题显得尤为重要。以物流服务响应概率为约束条件,构建了一种综合考虑碳排放成本和物流成本的物流中心选择模型。引入调节因子和高斯变异对基础狮群算法(LSO)进行改进,并用改进狮群算法(ILSO)对其进行求解。函数测试结果表明,ILSO相比LSO和遗传算法(GA)具有较好的寻优能力和稳定性。相关的案例仿真分析显示,ILSO算法的选址结果总成本相比LSO算法和GA算法分别减少14.63%、17.11%,有效地节约了选址成本。最后,通过对物流服务响应概率进行敏感度分析表明,随着响应概率的增加,碳排放成本呈现先降低、后快速增加的情况。

Due to the impact of COVID-19, the demand for cold chain drugs is surging. In order to complete the delivery of medical supplies on time, the location of distribution center is particularly important. Taking logistics service response probability as constraint condition, a logistics center selection model considering carbon emission cost and logistics cost is constructed. The basic lion swarm algorithm(LSO) is improved by introducing regulatory factors and Gaussian variation, and solved by improved lion swarm algorithm(ILSO). The function test results show that ILSO has better searching ability and stability than LSO and GA. Relevant case analysis shows that the total cost of site selection results of this algorithm is reduced by 14.63% and 17.11% respectively compared with LSO algorithm and GA algorithm, which greatly saves the cost of site selection. Finally, the sensitivity analysis of logistics service response probability shows that with the increase of response probability, carbon emission cost decreases first and then increases rapidly.