Abstract: To meet the demands of multiple small-batch logistics in spatially distributed,a multi-task scheduling problem oriented to multi-origin and multi-destination was researched.Based on multi-path and multi-consolidation characteristics of distributed logistic tasks,the multi-task logistics service network diagram was designed.For purpose of synthetically balancing the interests between overall multi-task and individual tasks,a mathematical model was proposed to minimize the sum of logistics and penalty cost by considering cost discount,waiting cost and waiting time caused by consolidation.In view of variable length path selection,multi-stage consolidation and resources capability confliction in the process of solving,the genetic algorithm based on priority was designed,and the adaptive variable length chromosome crossover and double mutation mechanisms were constructed to improve the efficiency.The effectiveness of proposed model and algorithm was verified by case study.

Key words: integrated multi-task scheduling, multi-stage consolidation, small-batch logistics, genetic algorithms

摘要： 针对供需呈空间分布的多个小批量物流服务需求,研究了面向多起始地—多目的地的多任务集成调度问题。基于分布式物流任务多路径与多集并的特点,设计了多任务物流服务网络图,综合考虑集并产生的费用折扣、等待成本与等待时间,从系统角度权衡任务个体与多任务整体利益,构建了以物流成本与延迟惩罚成本之和最小为目标的数学模型。针对模型求解过程中存在的变长度路径选择、多级集并、资源能力冲突等特点,设计了基于优先权的遗传算法对模型进行求解,并构造自适应变长度交叉与双变异机制增强求解效率。结合算例验证了模型和算法的有效性。

关键词: 多任务集成调度, 多级集并, 小批量物流, 遗传算法