Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem |
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| Affiliation: | 1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China;2. Siemens Technology, Siemens Ltd., China;1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, 100124, China;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China;3. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Jilin, 130012, China;4. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China;5. Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China;6. RIAMB (Beijing) Technology Development Co., Ltd (R.T.D.), Beijing, 100120, China;7. Digital Twin International Research Center, International Research Institute for Multidisciplinary Science, Beihang University, Beijing, 100191, China;1. School of Mechanical Engineering, Shandong University, Jinan 250061, PR China;2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, PR China;1. Advanced Remanufacturing and Technology Centre (ARTC), A*STAR, 3 Cleantech Loop, 637143, Singapore;2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore;3. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, 510070, China;4. Singapore Institute of Manufacturing Technology (SIMTech), A*STAR, 5 Cleantech Loop, 636732, Singapore;1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China;2. Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland, 1010, New Zealand;3. Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada;4. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, 200072, China |
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| Abstract: | Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal. |
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