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New Hybrid Parallel Algorithm for Variable-sized Batch Splitting Scheduling with alternative machines in Job Shops

Chinese Journal of Mechanical Engineering 2010年第4期23卷 484-495页

作者： ZHAO Yanwei WANG Haiyan WANG Wanliang XU Xinli Key Laboratory of Mechanical Manufacture and Automation of Ministry of Education Zhejiang University of Technology Hangzhou 310014 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310014 China

The batch splitting scheduling problem has recently become a major target in manufacturing systems, and the researchers have obtained great achievements, whereas most of existing related researches focus on equal-sized and consistent-sized batch splitting scheduling problem, and solve the problem by fixing the number of sub-batches, or the sub-batch sizes, or both. Under such circumstance and to provide a practical method for production scheduling in batch production mode, a study was made on the batch splitting scheduling problem on alternative machines, based on the objective to minimize the makespan. A scheduling approach was presented to address the variable-sized batch splitting scheduling problem in job shops trying to optimize both the number of sub-bathes and the sub-batch sizes, based on differential evolution（DE）, making full use of the finding that the sum of values of genes in one chromosome remains the same before and after mutation in DE. Considering before-arrival set-up time and processing time separately, a variable-sized batch splitting scheduling model was established and a new hybrid algorithm was brought forward to solve both the batch splitting problem and the batch scheduling problem. A new parallel chromosome representation was adopted, and the batch scheduling chromosome and the batch splitting chromosome were treated separately during the global search procedure, based on self-adaptive DE and genetic crossover operator, respectively. A new local search method was further designed to gain a better performance. A solution consists of the optimum number of sub-bathes for each operation per job, the optimum batch size for each sub-batch and the optimum sequence of sub-batches. Computational experiments of four test instances and a realistic problem in a speaker workshop were performed to testify the effectiveness of the proposed scheduling method. The study takes advantage of DE＇s distinctive feature, and employs the algorithm as a solution app

关键词： variable-sized batch splitting differential evolution alternative machines local search

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A naive optimization method for multi-line systems with alternative machines

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Frontiers of Mechanical Engineering 2019年第4期14卷 377-392页

作者： Weichang KONG Fei QIAO Qidi WU School of Electronics and Information Engineering Tongji UniversityShanghai 201804China

The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing *** the two problems are considered simultaneously,the resulting problem is much more complex than either of *** sufficient training data for conventional data-based optimization approaches is difficult because of the high diversity of system ***,optimization of multi-line systems with alternative machines requires a simple mechanism and must be minimally dependent on historical *** define a general multi-line system with alternative machines,this study introduces the capability vector and matrix and the distribution vector and matrix.A naive optimization method is proposed in accordance with classic feedback control theory,and its key approaches are *** a reasonable target value is provided,the proposed method can realize closed-loop optimization to the selected objective *** studies are performed on a real 5/6-inch semiconductor wafer manufacturing facility and a simulated multi-line system constructed on the basis of the MiniFAB *** show that the proposed method can effectively and efficiently optimize various objective *** method demonstrates a potential for utilization in multi-objective optimization.

关键词： multi-line systems alternative machines feedback control closed-loop optimization

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