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Multi-objective flexible job shop energy-saving scheduling problem based on improved genetic algorithm(PDF)


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Multi-objective flexible job shop energy-saving scheduling problem based on improved genetic algorithm
Wang LeiCai JingcaoShi Xin
School of Mechanical and Automotive Engineering,Anhui Polytechnic University,Wuhu 241000,China
improved genetic algorithm multi-objective flexible job shop scheduling comfort energy-saving scheduling
To reduce the energy consumption in flexible job shop scheduling,by analyzing the current research status and insufficiency,the makespan,power consumption of machine and the comfort level of employee are supposed as multi-objectives function for flexible job shop scheduling problem(FJSP).An improved genetic algorithm is proposed to optimize this problem.The weighting method is used to initialize the population in order to obtain better solution,meanwhile the total fitness value is obtained by a fast decoding method.The modified crossover and mutation operations are used to avoid creating the illegal solution.The elitism strategy is used to keep good genes.The efficiency and quality of solution can be improved by using the proposed improved genetic algorithm.Simulation tests are done to verify the effectiveness of the proposed improved genetic algorithm.


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Last Update: 2017-08-31