摘要
如何在顾客下单后协调好拣选和配送环节,在最短的时间、以较低的成本将商品从货架上拣出、打包后配送到顾客手中,已成为B2C电子商务物流管理中亟待解决的问题。本文尝试以最小化订单履行时间为目标,构建非线性拣选与配送联合调度模型,以解决订单拣选顺序、拣选作业方式、车辆行驶线路等联合决策。为求解此NP难问题,设计了三阶段启发式算法:首先采用"聚类-路径优化"思想,依据顾客位置进行配送方案确认;然后采用基于相似度聚类的订单分批规则对每条配送线路的订单进行分批合并;最后调整拣选任务与配送线路顺序。通过数据实验对模型进行验证,并与传统拣选与配送分开优化的结果进行对比。结果表明,三阶段算法能够有效缩短订单完成时间、降低配送车辆等待时间、改善配送资源利用率。
It is an important issue to integrate the order picking with delivery problem under shorter time and lower cost by picking the items from the shelves,packaging them and delivering to customers.A nonlinear mathematical model is proposed to minimize the time required to complete picking the orders,delivering to customer and returning to the distribution center,which solves the joint decision-making problem such as order picking sequence,picking process method and vehicle routing.For this NP-hard problem,a three-phase heuristic algorithm is designed.Firstly,the＂clustering-vehicle routing＂method is used to get delivery solutions.Secondly,the similarity-based order batching rules are used to optimize each route＇s orders.Thirdly,picking sequence is sorted based on the descending order of each route＇s delivery time.The experiments are proposed to test the efficiency of the model.The results are compared with the traditional optimization algorithm,which show that the three-phase algorithm can reduce the throughput time,decrease the vehicle＇s wait time and improve the delivery resource utilization.integrated scheduling;order picking;vehicle route;three-phase algorithm;genetic algorithmAbstract：With the development and wide-spread use of mobile technology,customers can shop anytime and anywhere through a business-to-consumer（B2C）e-commerce shopping platform.However small lot-size and high frequency customer orders make order picking and delivery difficult to implement.In order to accelerate the whole order fulfillment process,orders should be picked and delivered to customers in a very short lead time.It is therefore critical to integrate scheduling order picking and distribution under B2Ce-commerce.Research on order picking problems,however,seldom takes delivery constraints into consideration.The integrated order picking and distribution scheduling（IOPDS）problem is studied to minimize the time required to complete picking the orders,delivering to customer and returning to the distribution center to meet the demand of a given set of customers.The picking processing method is order bathing optimization and distribution characteristic is batching delivery with vehicle routing problem.The problem is NPhard in strong sense.A three-phase heuristic algorithm is proposed,analyze upper bounds and low bounds of the algorithm are analyzed.The first phase uses the＂clustering-vehicle routing＂method to get delivery solutions;the second phase uses the similarity-based order batching rules to optimize each route＇s orders;the third one sorts picking sequence based on the descending order of each route＇s delivery time.The traditional sequential approach is also proposed,which optimizes order picking and delivery processes separately.In order to verify the effectiveness of the proposed model and algorithms for IOPDS,several examples are tested.The locations for 300 customers are randomly generated in the 100＊100square,where the warehouse is in the center of the square.The three-phase algorithm＇s relative difference from the lower bounds is good.The results are also compared with the traditional algorithm,which show several enlightening findings：1）the throughput time of the three-phase algorithm is 17.11% shorter than the one of the traditional algorithm,which means it is significant to integrate order picking and distribution;2）the average improvement of the threephase algorithm is 13.03%,shows that it is helpful to improve the whole efficiency of the picking and distribution system;3）it decreases the vehicle＇s wait time and improve the delivery resource utilization.Theoretically the IOPDS model and algorithm in the work expand the order picking optimization theory and improve the scheduling of production and distribution problem.Moreover,it is beneficial to the ecommerce shopping platform,which can promote the shipping efficiency,save vehicle resources and improve customer satisfaction.
出处
《中国管理科学》
CSSCI
北大核心
2016年第7期101-109,共9页
Chinese Journal of Management Science
基金
国家自然科学基金面上资助项目(71471025
71171029)
国家自然科学基金重点资助项目(71531002)
关键词
联合调度
订单拣选
路径优化
三阶段算法
遗传算法
integrated scheduling
order picking
vehicle route
three-phase algorithm
genetic algorithm
