多需求点间车辆调度模型及优化算法混合求解研究  

Research on Hybrid Solution of Vehicle Scheduling Model and Optimization Algorithm among Multi-demand Points

作　　者：王素欣 熊珺恺 王雷震 卢福强[3] 温恒 司马聪 WANG Suxin;XIONG Junkai;WANG Leizhen;LU Fuqiang;WEN Heng;SIMA Cong(School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China;School of Information Science&Engineering,Northeastern University,Shenyang 110819,China;School of Economics and Management,Yanshan University,Qinhuangdao 066004,China;School of Computer Science and Technology,Jilin University,Changchun 130012,China)

机构地区：[1]东北大学秦皇岛分校控制工程学院,河北秦皇岛066004 [2]东北大学信息科学与工程学院,辽宁沈阳110819 [3]燕山大学经济管理学院,河北秦皇岛066004 [4]吉林大学计算机科学与技术学院,吉林长春130012

出　　处：《湖南大学学报（自然科学版）》2023年第8期194-204,共11页Journal of Hunan University:Natural Sciences

基　　金：国家重点研发计划项目(2020YFB1712802);国家自然科学基金资助项目(71401027);河北省高等学校人文社会科学研究项目(SQ202002)。

摘　　要：为解决多需求点间同时集送货问题,建立考虑需求拆分和转运的车辆路径模型.在模型中,加入车辆装载量动态变化约束、节点可多次访问约束和需求可拆分转运约束,提高问题的普遍性.在模型的优化算法中,算术、蚁群优化算法混合求解.通过算术蚁群算法嵌套优化模式,外层算术优化算法得到配送车辆的任务量,内层蚁群算法优化路径,并将结果反馈给外层算法继续更新求解,直至达到终止条件.同时,添加概率系数、增加算子位置更新公式和更新动态禁忌矩阵对混合算术蚁群算法改进,增加解的多样性,提高算法的求解效率.最后通过实例验证并与混合鲸鱼算法等比较,改进的算法解决本文问题效果更好.To solve simultaneous pickup and delivery among multiple demand points,a vehicle routing model that incorporates demand splitting and transfer is established.The constraint of dynamic variation of vehicle load,the constraint of multiple node access and the constraint of demand split transport are added in the model to improve the universality of the problem.In the optimization algorithm of the model,a hybrid approach combining arithmetic and ant colony optimization algorithm is employed to solve the problem.The algorithm follows a nested optimization structure,where the outer arithmetic optimization algorithm gets the task quantity of the delivery vehicle.The inner ant colony algorithm then optimizes the path,and provides feedback to the outer algorithm to continue to update and solve until the termination condition is met.At the same time,several enhancements are introduced to the hybrid arithmetic ant colony algorithm,such as incorporating probability coefficient,adding operator position update formula and updating dynamic tabu matrix.These enhancements aim to increase the diversity of solutions and improve the efficiency of the algorithm.Finally,the improved algorithm is verified by an example and compared with the hybrid whale algorithm and other algorithms to solve the problem in this paper.

关 键 词：路径规划 同时集送货问题 需求可拆分 随机转运点 算术优化算法 蚁群算法 

分 类 号：N945.1[自然科学总论—系统科学]