计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (14): 30-33.DOI: 10.3778/j.issn.1002-8331.2009.14.009

• 博士论坛 • 上一篇    下一篇

自适应的多目标资源受限的运输任务调度研究

王 剑1,2,3,王红卫1,2,3   

  1. 1.华中科技大学 系统工程研究所,武汉 430074
    2.国家国民经济动员仿真演练研究中心,武汉 430074
    3.图像信息处理与智能控制教育部重点实验室,武汉 430074
  • 收稿日期:2008-12-29 修回日期:2009-02-23 出版日期:2009-05-11 发布日期:2009-05-11
  • 通讯作者: 王 剑

Research on adaptive multi-objective resource-constrained transport task scheduling

WANG Jian1,2,3,WANG Hong-wei1,2,3   

  1. 1.Systems Engineering Institute,Huazhong University of Science and Technology,Wuhan 430074,China
    2.National Simulation Training Center for National Economy Mobilization,Wuhan 430074,China
    3.Key Laboratory of Image Processing and Intelligent Control,Wuhan 430074,China
  • Received:2008-12-29 Revised:2009-02-23 Online:2009-05-11 Published:2009-05-11
  • Contact: WANG Jian

摘要: 针对双目标下的资源受限的运输任务调度问题(RCTTSP),提出一种自适应的多目标混合遗传算法(AMOHGA)。该算法将串行调度启发式方法应用于种群初始化与适应度评估,采用权重求和与分级适应度分配方法进行个体适应度分配,并将基于模糊逻辑控制器的自适应遗传参数调整方法用于提高算法性能。在描述多目标RCTTSP的基础上,给出AMOHGA基本原理,然后针对不同规模测试案例进行实现,并进行了调度结果与算法性能的对比分析。结果表明,该算法能有效地解决多目标资源受限的运输任务调度问题,并具有良好的算法性能。

关键词: 资源受限, 运输任务调度, 多目标, 混合遗传算法, 串行调度, 模糊逻辑

Abstract: According to the Resource-Constrained Transport Task Scheduling Problem(RCTTSP) with two optimal objectives,an Adaptive Multi-Objective Hybrid Genetic Algorithm(AMOHGA) is proposed.The proposed algorithm uses the serial scheduling method to initialize the population and evaluate the individual,and uses the weighted sum method and the rank-based fitness assignment method to assign the individual fitness.Furthermore,the performance of the algorithm is improved by using an adaptive GA parameters tuning method based on fuzzy logic controller.Based on the description of the multi-objective RCTTSP,the principle of the AMOHGA is presented and the algorithm is developed to scheduling several experimental cases with different problem sizes.On this basis,the effectiveness and efficiency of the algorithm are compared.The results indicate that the proposed AMOHGA can resolve the proposed multi-objective RCTTSP efficiently and has good effectiveness.

Key words: resource-constrained, transport task scheduling, multi-objective, hybrid genetic algorithm, serial scheduling, fuzzy logic