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作 者:李伟铭 杨敬辉[1,2] LI Wei-Ming;YANG Jing-Hui(School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China;School of Intelligent Manufacturing and Control Engineering,Shanghai Polytechnic University,Shanghai 201209,China)
机构地区:[1]上海第二工业大学计算机与信息工程学院,上海201209 [2]上海第二工业大学智能制造与控制工程学院,上海201209
出 处:《计算机系统应用》2024年第8期240-249,共10页Computer Systems & Applications
基 金:上海市教委产学研项目(A30DB1621008B)。
摘 要:针对考虑缓冲区容量限制和机器加工档位的流水车间调度问题(FSSP_LBMPG),建立了有限缓冲区绿色流水车间数学规划模型,模型以最小化最大完工时间和最小化加工能量消耗为目标函数,将缓冲区容量纳入约束,通过合理选择机器的加工档位达到协调加工速度和加工能耗的效果.针对问题模型特点,提出了一种改进蒲公英优化算法(IDOA),算法首先根据调度问题的特点设计了双层实数编码机制表示问题的解,通过引入一种初始化机制,提高初始解质量和求解效率.算法迭代过程中,设计了实数交叉策略和变邻域搜索策略,弥补了原始蒲公英算法局部搜索能力较差的缺点,提高了改进算法的开发能力.最后通过设计案例上的对比实验,表明所提改进措施能有效增强算法性能,也验证了算法的有效性和鲁棒性.To solve the flow shop scheduling problem with limited buffers and machine processing gears(FSSP_LBMPG),this research establishes a mathematical programming model for green flow shops with limited buffers.The model has two objective functions:the minimized values of maximum completion time and processing energy consumption.With buffer capacity as a constraint,the processing speed and energy consumption are coordinated by reasonably selecting machine processing gears.Based on the characteristics of the problem model,an improved dandelion optimization algorithm(IDOA)is proposed.The algorithm first designs a DOA double-layer real-valued encoding mechanism to represent the solution to the problem according to the characteristics of the scheduling problem.By introducing an initialization mechanism,the quality and efficiency of the initial solution are improved.During algorithm iteration,a realvalued crossover strategy and a variable neighborhood search strategy are designed to compensate for the poor local search ability of the original dandelion algorithm and enhance the development capabilities of the improved algorithm.Comparative experiments on designed cases show that the proposed improved algorithm effectively enhances the performance of the original algorithm,thereby verifying the effectiveness and robustness of the improved algorithm.
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