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

doi:10.3778/j.issn.1002-8331.2009.14.009

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (14): 30-33.DOI: 10.3778/j.issn.1002-8331.2009.14.009

• 博士论坛 • Previous Articles Next Articles

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

WANG Jian^1,2,3,WANG Hong-wei^1,2,3

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

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.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

通讯作者: 王剑

Abstract

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

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

WANG Jian^1,2,3,WANG Hong-wei^1,2,3. Research on adaptive multi-objective resource-constrained transport task scheduling[J]. Computer Engineering and Applications, 2009, 45(14): 30-33.

王剑^1,2,3,王红卫^1,2,3. 自适应的多目标资源受限的运输任务调度研究[J]. 计算机工程与应用, 2009, 45(14): 30-33.

[1]	ZHANG Bo, XU Liming, HUANG Zhiwei, YAO Xiaopeng. Multi-objective GANs Pareto Optimality Algorithm Using Gradient Strategy [J]. Computer Engineering and Applications, 2021, 57(9): 89-95.
[2]	ZHANG Xu, YUAN Xumei, YUAN Jige. Research on Methods for Supply-Demand Matching Decision-Making Based on Broker [J]. Computer Engineering and Applications, 2021, 57(9): 240-246.
[3]	YANG Wei, WU Yingying, WANG Ting. Research on Configuration Optimization Problems of Shuttle-Carrier Storage and Retrieval System [J]. Computer Engineering and Applications, 2021, 57(4): 258-265.
[4]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[5]	HU Xiaobing, CHEN Shunian, ZHANG Yingfei, GU Shenghao. New Ripple-Spreading Algorithm for Multi-objective Path Optimization [J]. Computer Engineering and Applications, 2021, 57(23): 81-90.
[6]	LI Qian, JIANG Li, LIANG Changyong. Multi-objective Cold Chain Distribution Optimization Based on Fuzzy Time Window [J]. Computer Engineering and Applications, 2021, 57(23): 255-262.
[7]	LI Dahai, AI Zhigang, WANG Zhendong. Improved Yin-Yang-Pair Multi-objective Optimization Algorithm Based on Full-Combination Strategy [J]. Computer Engineering and Applications, 2021, 57(22): 110-124.
[8]	DU Shouxin, WU Tao. Study on Application of Double Population Hybrid Genetic Algorithm in Cut Order Planning [J]. Computer Engineering and Applications, 2021, 57(22): 182-189.
[9]	DING Yi, ZHANG Chengcheng. External Trucks’ Appointment Optimization of Delivering Containers from Outside at Container Terminal [J]. Computer Engineering and Applications, 2021, 57(19): 267-273.
[10]	CUI Caixia, BI Chaochao, FAN Qinqin. Self-Adaptive MODE Based on Hidden Markov Chain and Application [J]. Computer Engineering and Applications, 2021, 57(14): 83-94.
[11]	LI Yuxin, LI Yueyue. Multi-objective Vehicle Scheduling Problem for JIT Procurement [J]. Computer Engineering and Applications, 2021, 57(12): 263-272.
[12]	CHEN Yuanwen. Application of MapReduce Technology in Problem of Material Transportation and Stowage [J]. Computer Engineering and Applications, 2021, 57(12): 273-278.
[13]	YUAN Shunjie, CHENG Hui, YE Zhencheng, CHENG Peixin. Application of SOM-T2 FLS in Stock Market Forecasting [J]. Computer Engineering and Applications, 2020, 56(7): 130-136.
[14]	WANG Lili, ZHANG Linjuan, SHANG Xuening, GAO Deyun. Survey on Application of Intelligence Computing in Deployment of Electric Vehicle Charging Stations [J]. Computer Engineering and Applications, 2020, 56(2): 1-10.
[15]	JI Xunsheng, CAI Yiqing. Solving Multi-Objective FJSP Using Historical Information and Restriction Operator [J]. Computer Engineering and Applications, 2020, 56(2): 272-278.

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

自适应的多目标资源受限的运输任务调度研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics