基于众包物流配送模式的生产配送协同调度多目标优化

Bi-objective Optimization Problem of Integrated Production and Transportation Schedulingwith Crowdsource Deliveries

众包物流配送模式是应对“最后一公里物流”问题的有效途径。本文将众包物流配送模式引入供应链生产配送协同调度问题中,通过把“最后一公里物流”外包给社会资源,以降低企业物流成本,实现更好的供应链运营管理绩效。论文同时考虑客户服务水平和物流成本,建立了基于众包物流配送模式的生产配送协同调度双目标优化模型。分别设计了Epsilon约束算法和非支配排序遗传算法(NSGA-II)用于求解问题的帕累托前沿,并构造数值算例测试算法的求解效果。测试结果显示了NSGA-II具有良好的求解效率与求解质量。最后,通过灵敏度分析验证了众包物流在供应链协同调度管理中的优势,为制造企业应对“最后一公里物流”困境提供了理论依据和算法支持。

Crowdsource delivery has gradually become an important strategy to deal with the “last-mile delivery” problem. This paper adopts the crowdsource delivery into the integrated production and transportation scheduling problem by outsourcing the “last-mile delivery” to social resources, in order to reduce the logistics costs and achieve a better operation performance in supply chain management. We investigate an integrated production and transportation scheduling problem with crowdsource deliveries, and construct a mixed-integer linear program with the bi-objective function considering both customer service level and total transportation costs simultaneously. To solve the problem, both epsilon-constraint algorithm and NSGA-II algorithm are developed to obtain Pareto frontiers of the proposed model. Numerical examples are constructed to verify the performance of the developed algorithms. The results verify the effectiveness and efficiency of the NSGA-II algorithm. Besides, sensitivity analysis indicates the advantage of introducing crowdsource delivery into supply chain scheduling, which provides theoretical basis and algorithm support for manufacturing enterprises to cope with the “last-mile logistics” problem.