| 摘 要: 为解决柔性流水作业车间生产效率低、能耗高、加工成本高、订单周期长等问题，设计了以最小化最大完工时间、最小化总能耗为目标的车间调度模型。针对遗传算法和模拟退火算法计算效率低和易陷入局部最优的缺点，提出了一种改进遗传模拟退火算法(Improved Genetic Simulated Annealing Algorithm，IGSAA)。在遗传算法的基础上采用了动态的交叉率和变异率，以及在模拟退火算法中加入了基于逆序的局部搜索方法，提高了最优解的搜索效率，减少了算法的迭代次数。最后，通过工厂实例进行验证。结果表明，与遗传算法和模拟退火算法相比，所提算法分别节省了12.1%和21.4%的能耗，缩短了17.5%和36.7%的完工时间以及减少了20%和48.4%的迭代次数。 |
| 关键词: 流水车间调度；多目标优化；遗传模拟退火算法；能耗 |
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中图分类号: TP278
文献标识码: A
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| Energy-saving Optimization of Flexible Flow Shop Scheduling based on Genetic Simulated Annealing Algorithm |
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PENG Laihu1,2, WANG Weihua1, WAN Changjiang1,2, WAN Lulu1
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( 1.Zhejiang Sci -Tech University, Hangzhou 310000, China ;
2.Research Institute of Zhejiang Sci -Tech University in Longgang, Wenzhou 325000, China)
laihup@zstu.edu.cn; wwhjiushiwo@163.com; wanchj@zstu.edu.cn; 202030605271@zstu.edu.cn
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| Abstract: In order to solve the problems of low production efficiency, high energy consumption, high processing cost and long order cycle in flexible flow shop, this paper proposes to design a workshop scheduling model to minimize the maximum completion time and minimize the total energy consumption. Aiming at the shortcomings of genetic algorithm and simulated annealing algorithm, such as low computational efficiency and being easily falling into local optimum, an improved genetic simulated annealing algorithm (IGSAA) is proposed. Based on the genetic algorithm, the dynamic crossover rate and mutation rate are adopted. A local search method based on reverse order is added to the simulated annealing algorithm. As a result, the search efficiency of the optimal solution is improved and the number of iterations is reduced. Finally, it is verified by a factory instance. Results show that compared with genetic algorithm and simulated annealing algorithm, the proposed algorithm saves energy consumption by 12.1% and 21.4%, shortens the completion time by 17.5% and 36.7%, and reduces the number of iterations by 20% and 48.4%, respectively. |
| Keywords: flow shop scheduling; multi-objective optimization; genetic simulated annealing algorithm; energy consumption |
