摘要
针对各个配送点订单生成时间不确定,且未来生成订单现阶段配送方案制定有影响,本文提出了动态订单匹配策略,将已生成和未生成的订单信息综合考虑,提高匹配效果。然后,本文建立了以最小化运能浪费和订单等待处理时间为目标的二阶段随机规划模型来编制当前和下一阶段的配送方案,并设计了自适应遗传算法进行求解。最后,本文利用某地25个配送点数据信息进行二阶段配送路径规划。与蚁群算法相比,本文算法对两个阶段的运能浪费成本和时间浪费成本求解过程收敛时间分别为:112.3s、102.4s、98.5s、114.7s,均小于蚁群算法收敛时间:117.3s、115.4s、109.6s、118.2s。且本文算法求得的两个阶段总运能浪费成本和时间浪费成本分别为62000t.km、644t.h,远小于传统启发式算法的72400t.km、816t.h。
In view of the uncertain order generation time of each distribution point and the influence of the current distribution plan of the future generation of orders,this paper proposes a dynamic order matching strategy,which comprehensively considers the generated and ungenerated order information to improve the matching effect.Then,this paper establishes a two-stage stochastic planning model with the goal of minimizing transportation energy waste and order waiting processing time to coordinate the current and next stage of the distribution plan and designs an improved differential intelligent heuristic algorithm based on the order matching degree matrix.Finally,this paper uses the data of 25 distribution points in a certain place to carry out two-stage distribution route planning.Compared with the traditional heuristic algorithm,the cost of energy wasted,and the cost of time wasted in the two stages of the algorithm in this paper are:112.3s,102.4s,98.5s,114.7s,respectively,which are shorter than the convergence time of the traditional static heuristic algorithm:117.3s,115.4s,109.6s,118.2s.In addition,the total capacity waste cost and time waste cost of the two stages obtained by the algorithm in this paper are 62000t.km and 644t.h respectively,which are much smaller than the 72400t.km and 816t.h of the traditional heuristic algorithm.
作者
李致远
陈钉均
LI Zhiyuan;CHEN Dingjun(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation,Southwest JiaoTong University,Chengdu 610031,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 610031,China)
出处
《综合运输》
2023年第5期147-153,共7页
China Transportation Review
基金
中国神华能源股份有限公司科技项目(CJNY-20-02)
中国国家铁路集团有限公司科技研究计划项目(P2020X016,2019F002)
国家自然基金项目(52072314,52172321,52102391)
四川省科技计划项目(2020YJ0268,2020YJ0256,2021YFQ0001,2021YFH0175,2022YFH0016)
中国铁路北京局集团有限公司科技研究开发计划课题(2020AY02,2021BY02)
国家重点研发计划资助(2017YFB1200702)
中国工程院战略研究与咨询项目(运营管理方案研究)资助。
关键词
路径优化
时间不确定
动态订单匹配策略
匹配效果
自适应遗传算法
Path optimization
Time uncertainty
Dynamic order matching strategy
Matching effect
Adaptive genetic algorithm
