An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop

• Published in: IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2019-10, Vol.49 (10), p.1933-1945
• Main Authors: Li, Xinyu, Gao, Liang, Pan, Quanke, Wan, Liang, Chao, Kuo-Ming
• Format: Article
• Language: English
• Subjects: Conferences; Genetic algorithms; Hybrid algorithm; integrated process planning and scheduling (IPPS); Job shop scheduling; Machine tools; Manufacturing; Manufacturing systems; Packaging; Process planning; Scheduling; Searching; variable neighborhood search (VNS)
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2018.2881686

Process planning and scheduling are modeled sequentially in the traditional manufacturing system. However, because of their complementarity, the increasing need to integrate them has emerged to enhance the manufacturing productivity significantly. Therefore, the integrated process planning and scheduling (IPPS) is becoming a hotspot in providing a blueprint for efficient manufacturing system. This paper proposes a novel algorithm hybridizing the genetic algorithm with strong global searching ability and variable neighborhood search with strong local searching ability for the IPPS problem. To improve the searching ability, a novel procedure, encoding method, and local search method have been designed. Effective operators have been adopted. Three experiments with totally 37 well-known benchmark problems are employed to evaluate the performance of the proposed method. Based on the results, the proposed algorithm outperforms the state-of-the-art methods and finds the new solutions (the best solutions found so far) for some problems. The proposed method has also been applied on a real-world case from a nonstandard equipment production workshop for the packaging machine of a machine tool company in China. The solution demonstrates that it can solve real-world cases very well.