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论文类型：会议论文

发表时间：2021-09-11

摘要：In real industrial manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously. Scheduling optimization problem (SOP) is one of the important and complex COP models, where it can have a major impact on the productivity of a production process. Moreover, the COP models make the problem intractable to the traditional optimization techniques because most of scheduling problems fall into the class of NP-hard combinatorial problems. Hybrid evolutionary algorithm (HEA) will be introduced for treating automation problems in factory, manufacturing, planning and scheduling, and logistics and transportation systems. HEA is the most popular metaheuristic method for solving NP-hard optimization problems. In the past few years, EA has been exploited to solve design automation problems. Concurrently, the field of HEA reveals a significant interest in evolvable hardware and problems such as routing, placement or test pattern generation. We propose a random key-based genetic algorithm with crossover and mutation operation to avoid premature convergence and to maintain diversity. Numerical experiments for case study show the effectiveness of proposed approach comparing with hybrid evolutionary algorithm.