Multi-objective optimization method for cross-workshop linkage production of partially flexible free-forging with forward single-machine scheduling

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-08-22 DOI:10.1016/j.cie.2024.110508
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Abstract

Forging is an important sector in China’s machinery manufacturing industry. To complete the processing of forgings, it is often necessary to go through multiple processes, which are commonly performed by different workshops. Due to the complexity of cross-workshop production, there are few studies on cross-workshop scheduling in the forging industry. Therefore, in order to realize resource sharing and collaborative production between multiple workshops, and improve the overall production efficiency and resource utilization rate, it is very important to optimize the scheduling of linked cross-workshop production. In this paper, a new cross-workshop partial flexible hammer forging scheduling model (CSPFH-FSM) is established to solve the scheduling problem of linked cross-workshop production with production time and energy consumption serving as the overall optimization goals in the whole partially flexible free forging production line (P3FPL). A single-machine forward-prediction variable genetic operator NGSA-II algorithm (SPVGO-NGSA II) is proposed to solve the multi-objective optimization problem of partially flexible production, in which the variable genetic operator is added to the effective coding, and the search strategy is dynamically adjusted to avoid reaching locally optimal solutions. Due to the interference of maintenance and the insufficient utilization of energy after forging, a fixed maintenance disturbance and a residual temperature utilization strategy are added to the scheduling process. Finally, the optimization obtained using the proposed variable and traditional fixed genetic operators are compared for different orders, and the algorithm proposed in this paper is compared with the typical multi-objective optimization algorithms. The results validate the effectiveness of the proposed algorithm, and provide a basic scheme for the linked scheduling of the whole production line in practical applications.

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部分柔性自由锻造跨车间联动生产的多目标优化方法与前向单机调度
锻造是中国机械制造业的一个重要部门。要完成锻件的加工,往往需要经过多道工序,而这些工序通常由不同的车间完成。由于跨车间生产的复杂性,关于锻造行业跨车间调度的研究很少。因此,为了实现多个车间之间的资源共享和协同生产,提高整体生产效率和资源利用率,优化跨车间联动生产的调度非常重要。本文建立了一种新的跨车间部分柔性锤锻排产模型(CSPFH-FSM),以解决整个部分柔性自由锻造生产线(P3FPL)中以生产时间和能耗为总体优化目标的联动跨车间生产排产问题。提出了单机前向预测可变遗传算子 NGSA-II 算法(SPVGO-NGSA II)来解决部分柔性生产的多目标优化问题,其中在有效编码中加入了可变遗传算子,并动态调整搜索策略以避免达到局部最优解。由于锻造后存在维修干扰和能量利用不足的问题,在排产过程中加入了固定维修干扰和余温利用策略。最后,比较了本文提出的可变遗传算子和传统固定遗传算子在不同阶次下的优化结果,并将本文提出的算法与典型的多目标优化算法进行了比较。结果验证了所提算法的有效性,并为实际应用中整条生产线的联动调度提供了基本方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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