摘要
具有时间、车容量和后进先出约束的多车辆取送货路径优化问题在现实中具有广泛的应用,针对现实问题中规模性和模型的复杂性使其在短时间内无法精确求解的问题,提出了候鸟群自适应变邻域搜索算法(MBO_AVNS)。以三种贪婪插入算法构建初始种群,确保种群的多样性和初始解的质量,对传统候鸟优化算法的邻域搜索算法进行改进,加强了算法的局部搜索能力。通过真实数据进行实验分析表明,与模拟退火算法(SA)和一般自适应变邻域搜索算法相比,MBO_AVNS算法在求解具有时间和后进先出约束的取送货路径问题上更具有优越性。
This paper proposed a migratory bird optimization adaptive variable neighborhood search algorithm(MBO_AVNS),where the initial population was built with three greedy insertion algorithms to ensure the diversity of the population and the quality of the initial solution.By improving on the traditional migratory bird optimization algorithm,the MBO_AVNS had better local search ability.At the end,through an empirical experiment,it was shown that compared with the simulated annealing algorithm(SA)and general adaptive variable neighborhood search algorithm,the MBO_AVNS was superior in solving the pickup and delivery routing problem with time and last-in-firstout constraints.
作者
崔沐涵
CUI Muhan(School of Business,Nanjing Audit University,Nanjing 211815,China)
出处
《物流技术》
2023年第3期48-53,共6页
Logistics Technology
基金
2021江苏省研究生科研创新计划项目“在线零售层级仓配网络中商品配置决策优化模型的开发与应用”(SJCX21_0882)。
关键词
取送货路径问题
自适应变邻域搜索算法
候鸟优化算法
后进先出约束
pickup and delivery routing problem
adaptive variable neighborhood search algorithm
migratory bird optimization algorithm
last-in-first-out constraint
