物流云服务平台下基于改进蝙蝠算法的任务调度.doc

—————————————— 收稿日期：2014-04-13 作者简介：张晓磊(1989-)，男，河北石家庄人，硕士研究生，主要研究方向为物流与供应链管理、生产系统仿真(E-mail:1193643253@)；马从安(1962-)，男，安徽寿县人，副教授，博士，主要研究方向为物流与供应链管理，工业工程. 物流云服务下基于改进蝙蝠算法的任务调度 张晓磊，马从安，申 晨 （中国矿业大学 矿业工程学院，江苏 徐州 221008） 摘 要：物流调度对提高供应链的有效管理至关重要。针对物流云服务模式中调度任务多、信息量大、需求广的特点，提出了一种改进蝙蝠算法求解物流云服务调度问题的方案，其优化目标为最小化调度时间和最大化资源利用率。根据设计的算法流程，首先基于工件升序排列(ranked order value, ROV)规则对蝙蝠个体进行重新编码，接着调整初始化数据范围来减少分配任务超载和资源闲置现象，并在迭代过程中增加约束条件来均衡任务量，最终实现了资源与任务的智能调度。通过和遗传、粒子群以及基本蝙蝠算法的对比分析，体现了改进算法的优越性。最后利用Witness对方案进行仿真，证明了改进蝙蝠算法在解决物流云服务任务调度中的有效性，同时扩展了蝙蝠算法的应用领域。 关键词：物流云调度；改进蝙蝠算法；均衡任务；智能匹配；仿真模拟；优越性 中图分类号：TP183;TP391.9 文献标志码：A 文章编号： Task scheduling based on improved bat algorithm under logistics cloud service ZHANG Xiao-lei, MA Cong-an, SHEN Chen (School of Mines, China University of Mining Technology, Xuzhou Jiangsu 221008, China) Abstract: Logistics scheduling is crucial to improve the efficiency of the supply chain management. Considering the characteristics of multi-tasks, large amount of information and wide demand, this article put forward a proposal based on improved bat algorithm (IBA) to minimize the scheduling time and maximize the resource utilization. According to the algorithm process, it recodes the bat individuals based on the rules of ROV, adjusts the range of initialized date to reduce the overloaded task assignment and resource idly as well as increases the constraint conditions during the iterative process. All those realize the intelligent scheduling of logistics task. The comparison with genetic algorithm (GA), particle swarm optimization (POS) and standard bat algorithm reflects the excellence of IBA. Finally the successful simulation through witness proves the effectiveness of IBA in task scheduling of logistic cloud service and extends the application field of IBA. Key words: Logistics cloud scheduling; improved bat algorithm; equilibrated assignments; smart matching; witness simulation; excellence 0 引言 进入21世纪，客户的个性化需求越来越多样，不仅促使物流服务日新月异，更要求物流调度对市场变化具有较高的适应性和灵敏性，而物流