Abstract: To solve the Location-Routing Problem(LRP),a bi-objective LRP model with low carbon emissions,distribution center location planning and vehicle route planning was proposed.To solve the disadvantages of poor generality and low efficiency for traditional heuristic algorithms in solving large-scale LRP,a hyper-heuristic algorithm based on selection function method as the selection strategy and the evaluation index based on global margin ranking as the acceptance strategy was designed.By solving a benchmark test instance,the algorithm could accurately,efficiently,and intelligently design a scheduling scheme when solving the proposed LRP model.By comparing with the traditional heuristic algorithm and the well-performing hyper-heuristic algorithm,the overall quality of the solution and the convergence efficiency of single solution were compared,which verified the feasibility and effectiveness of the proposed method.

Key words: carbon emission, logistics, bi-objective optimization, global margin ranking, hyper-heuristic algorithm

摘要： 为了解决目前物流选址—路径优化问题(LRP),提出一种以低碳排放量、配送中心选址规划和车辆路径规划为目标的双目标LRP模型。针对传统启发式算法在解决大规模LRP时的通用性差、效率低的缺点,设计出一种以选择函数法作为选择策略、以基于全局边缘排序的评价指标作为接受策略的超启发算法。通过求解基准测试实例,该算法在解决所提LRP模型时,能准确、高效、智能地设计出调度方案。与传统启发式算法以及性能良好的超启发算法在解的整体质量、单个解收敛效率等方面进行对比,验证了所提方法的可行性和有效性。

关键词: 碳排放, 物流配送, 双目标优化, 全局边缘排序, 超启发算法