基于遗传算法的舰载装备多目标作业调度优化研究
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摘要
舰载装备的作业调度是多任务模式下的复杂组合优化问题。已有研究主要针对单一目标优化,而实际往往需同时优化路径、时长、资源等多个目标。以两栖登陆舰舰载装备出库前的作业调度为研究对象,在考虑先序约束的基础上,对作业时长及资源用量同时进行优化。分析建立了该作业调度的多目标优化模型,利用遗传算法求解;针对两目标设计了可自适应调整的适应度函数,并对编码方式及遗传算子进行设计;实例仿真,验证算法可以高效地同时优化调度作业时长和资源用量。
Multi-objective operation scheduling of shipborne equipment is a complex combinational optimization problem under multi-task system. Existing research focuses mainly on single-objective optimization while several other objectives need to be considered during real operation such as path,duration, resource, etc. Considering the operation scheduling before exporting of an amphibious landing ship as the research object, both scheduling duration and resource requirement under the precedence constraint are optimized. The mathematical model of this multi-objective operation scheduling is established and solved using genetic algorithm. A fitness function which can be self-adaptively adjusted is designed; an adapting encoding strategy, a crossover operator, and a mutation operator are also designed during the solution. The result of the instance simulation indicates that the algorithm is effective and reliable to optimize duration and resource requirements during the operation scheduling.
Multi-objective operation scheduling of shipborne equipment is a complex combinational optimization problem under multi-task system. Existing research focuses mainly on single-objective optimization while several other objectives need to be considered during real operation such as path,duration, resource, etc. Considering the operation scheduling before exporting of an amphibious landing ship as the research object, both scheduling duration and resource requirement under the precedence constraint are optimized. The mathematical model of this multi-objective operation scheduling is established and solved using genetic algorithm. A fitness function which can be self-adaptively adjusted is designed; an adapting encoding strategy, a crossover operator, and a mutation operator are also designed during the solution. The result of the instance simulation indicates that the algorithm is effective and reliable to optimize duration and resource requirements during the operation scheduling.
引文
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[4]Ryan J C,Banerjee A G,Cummings M L,et al.Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations.[J].IEEE Transactions on Cybernetics(S2168-2267),2014,44(6):761-773.
[5]Ryan J,Cummings M,Roy N,et al.Designing an Interactive Local and Global Decision Support System for Aircraft Carrier Deck Scheduling:AIAA[Z].St.Louis,Missouri:2015.
[6]王涛.基于混合进化算法的军用车辆维修保障资源调度优化研究[D].北京:北京理工大学,2013.WANG Tao.Research on Maintenance Resource Scheduling Optimization for Military Vehicles Based on Hybrid Evolutionary Algorithm[D].Beijing:Beijing Institute of Technology,2013.
[7]李彬彬.基于遗传算法的多目标生产作业调度研究[D].上海:东华大学,2014.Li Binbin,The Research on multi-objective production scheduling based on genetic algorithm on the background of a clothing enterprise[D].Shanghai:Donghua University,2014.
[8]Ishikawa S,Kubota R,Horio K.Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem[J].Expert Systems with Applications(S0957-4174),2015,42(24):9434-9440.
[9]Meng Q,Zhang L,Fan Y.Approach of hybrid GA for multi-objective job-shop scheduling[J].International Journal of Modeling,Simulation,and Scientific Computing(S1793-9623),2016,7(4):1643006.
[10]Ole?ków Szlapka J,Paw?owski G.Scheduling and lot sizing problems for variable range of products using GA-based method[J].IFAC-Papers On Line(S2405-8963),2016,49(12):662-667.
[11]李梅娟,陈雪波.Pareto遗传算法在货位配置中的应用研究[J].控制工程,2006,13(2):138-140.Li Meijuan,Chen Xuebo.Research and Application of Pareto Genetic Algorithm for Freight Sites Assignment Optimization[J].Control Engineering of China,2006,13(2):138-140.
[12]孙明辉,刘继来.一般均衡与pareto最优关系的探讨[J].河北北方学院学报(自然科学版).2007,23(6):8-10.Sun Minghui,Liu Jilai.Discussion of the relationship between General Equilibrium and Pareto Optimum[J].Journal of Heibei North University(Natural Science Editon),2007,23(6):8-10.
[13]朱华炳,程翔宇,唐自玉,等.多目标遗传算法适应度函数权重设置的改进[J].组合机床与自动化加工技术,2007(4):11-14.Zhu Huabing,Chen Xiangyu,Tang Ziyu,et al.Improvement of Moderate Weight to Fitness Function of Multi-objective Genetic Algorithm[J].Modular Machine Tool&Automatic Manufacturing Technique.2007(4):11-14.