Abstract
Process planning is an essential part of the manufacturing system linking the designing and practical manufacturing. However, the reported process planning models are too simple to describe all characteristics because of the complexity of process planning. Therefore, a new mixed-integer linear programming (MILP) mathematical model is established based on OR-node of the network graph. In the model, the linear expression of the OR-node controlling function as well as three types of changing costs are first established. Beside, considering the OR-node selection state in the encoding and decoding method, a hybrid evolutionary algorithm (HEA) is designed to combine a genetic algorithm with a simulated annealing algorithm. The tournament selection method is adopted in the proposed HEA, and the discussion on the tournament size is conducted on the open problems to make the algorithm designing more reasonable and scientific. The HEA and the new MILP model are both tested on series of numerical experiments which are carried on the existing benchmarks as well as some randomly generated cases. The behavior of both two methods can verify their effectiveness and superiority successfully.
Similar content being viewed by others
References
Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418–429.
Falih, A., & Shammari, A. Z. M. (2020). Hybrid constrained permutation algorithm and genetic algorithm for process planning problem. Journal of Intelligent Manufacturing, 31(5), 1079–1099.
Floudas, C. A., & Lin, X. (2005). Mixed integer linear programming in process scheduling: modeling, algorithms, and applications. Annals of Operations Research, 139(1), 131–162.
Gan, P. Y., Lee, K. S., & Zhang, Y. F. (2001). A branch and bound algorithm based process-planning system for plastic injection mould bases. The International Journal of Advanced Manufacturing Technology, 18(9), 624–632.
Gong, G. L., Deng, Q. W., Chiong, R., Gong, X. R., Huang, H. Z. Y., & Han, W. W. (2020). Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation. International Journal of Production Research, 58(12), 3781–3799.
Guo, Y. W., Mileham, A. R., Owen, G. W., & Li, W. D. (2006). Operation sequencing optimization using a particle swarm optimization approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(12), 1945–1958.
Hu, Q., Qiao, L., & Peng, G. (2017). An ant colony approach to operation sequencing optimization in process planning. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 231(3), 470–489.
Hua, G., Zhou, X., & Ruan, X. (2007). GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts. The International Journal of Advanced Manufacturing Technology, 33(5), 594–603.
Jiang, J., & Hsiao, W.-C. (1994). Mathematical programming for the scheduling problem with alternate process plans in FMS. Computers & Industrial Engineering, 27(1–4), 15–18.
Jiang, Z. G., Jiang, Y., Wang, Y., Zhang, H., Cao, H. J., & Tian, G. D. (2019). A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. Journal of Intelligent Manufacturing, 30(1), 19–32.
Jin, L., & Zhang, C. (2019). Process planning optimization with energy consumption reduction from a novel perspective: mathematical modeling and a dynamic programming-like heuristic algorithm. IEEE Access, 7, 7381–7396.
Kim, Y. K., Park, K., & Ko, J. (2003). A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers & Operations Research, 30(8), 1151–1171.
Kumar, S. L. (2017). State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Engineering Applications of Artificial Intelligence, 65, 294–329.
Kusiak, A. (1985). Integer programming approach to process planning. The International Journal of Advanced Manufacturing Technology, 1(1), 73–83.
Lee, H. C., & Ha, C. (2019). Sustainable integrated process planning and scheduling optimization using a genetic algorithm with an integrated chromosome representation. Sustainability, 11(2), 502–525.
Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93–110.
Li, W. D., Ong, S. K., & Nee, A. Y. C. (2002). Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. International Journal of Production Research, 40(8), 1899–1922.
Li, W. D., Ong, S. K., & Nee, A. Y. C. (2004). Optimization of process plans using a constraint-based tabu search approach. International Journal of Production Research, 42(10), 1955–1985.
Li, X., Shao, X., & Gao, L. (2008). Optimization of flexible process planning by genetic programming. The International Journal of Advanced Manufacturing Technology, 38(1), 143–153.
Li, X., Gao, L., Shao, X., Zhang, C., & Wang, C. (2010). Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling. Computers & Operations Research, 37(4), 656–667.
Li, X., Gao, L., & Wen, X. (2013). Application of an efficient modified particle swarm optimization algorithm for process planning. The International Journal of Advanced Manufacturing Technology, 67(5–8), 1355–1369.
Li, X. Y., Gao, L., Pan, Q. K., Wan, L., & Chao, K.-M. (2018). An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(10), 1933–1945.
Lian, K., Zhang, C., Shao, X., & Gao, L. (2012). Optimization of process planning with various flexibilities using an imperialist competitive algorithm. The International Journal of Advanced Manufacturing Technology, 59(5), 815–828.
Liu, X., Yi, H., & Ni, Z. (2013). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing, 24(1), 1–13.
Ma, G. H., Zhang, Y. F., & Nee, A. Y. C. (2000). A simulated annealing-based optimization algorithm for process planning. International Journal of Production Research, 38(12), 2671–2687.
Shabaka, A., & ElMaraghy, H. A. (2008). A model for generating optimal process plans in RMS. International Journal of Computer Integrated Manufacturing, 21(2), 180–194.
Shin, K. S., Park, J.-O., & Kim, Y. K. (2011). Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Computers & Operations Research, 38(3), 702–712.
Sobeyko, O., & Moench, L. (2017). Integrated process planning and scheduling for large-scale flexible job shops using metaheuristics. International Journal of Production Research, 55(2), 392–409.
Tao, F., Bi, L., Zuo, Y., & Nee, A. Y. C. (2017). A cooperative co-evolutionary algorithm for large-scale process planning with energy consideration. Journal of Manufacturing Science and Engineering-Transactions of the Asme, 139(6), 1016–1027.
Touzout, F. A., & Benyoucef, L. (2019). Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches. International Journal of Production Research, 57(8), 2531–2547.
Wang, Y. F., Zhang, Y. F., & Fuh, J. Y. H. (2012). A hybrid particle swarm based method for process planning optimisation. International Journal of Production Research, 50(1), 277–292.
Wang, W., Li, Y., & Huang, L. (2018). Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts. Journal of Intelligent Manufacturing, 29(6), 1329–1336.
Wen, X. Y., Li, X. Y., Gao, L., & Sang, H. Y. (2014). Honey bees mating optimization algorithm for process planning problem. Journal of Intelligent Manufacturing, 25(3), 459–472.
Xia, H., Li, X., & Gao, L. (2016). A hybrid genetic algorithm with variable neighborhood search for dynamic integrated process planning and scheduling. Computers & Industrial Engineering, 102, 99–112.
Xia, Q., Etienne, A., Dantan, J.-Y., & Siadat, A. (2018). Reconfigurable machining process planning for part variety in new manufacturing paradigms: definitions, models and framework. Computers & Industrial Engineering, 115, 206–219.
Xu, X., Wang, L., & Newman, S. T. (2011). Computer-aided process planning – A critical review of recent developments and future trends. International Journal of Computer Integrated Manufacturing, 24(1), 1–31.
Ye, Y., Hu, T., Yang, Y., Zhu, W., & Zhang, C. (2020). A knowledge based intelligent process planning method for controller of computer numerical control machine tools. Journal of Intelligent Manufacturing, 31(7), 1751–1767.
Zhang, S., & Wong, T. N. (2018). Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning. Journal of Intelligent Manufacturing, 29(3), 585–601.
Zhang, F., Zhang, Y. F., & Nee, A. Y. C. (1997). Using genetic algorithms in process planning for job shop machining. IEEE Transactions on Evolutionary Computation, 1(4), 278–289.
Zhang, J. H., Xiao, M., Gao, L., & Pan, Q. K. (2018). Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464–490.
Acknowledgements
This work was supported in part by the National Key Research and Development Project under Grant 2019YFB1704603, National Natural Science Foundation of China under Grant 51775216 and the Program for HUST Academic Frontier Youth Team under Grant 2017QYTD04.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, Q., Li, X. & Gao, L. Mathematical modeling and a hybrid evolutionary algorithm for process planning. J Intell Manuf 32, 781–797 (2021). https://doi.org/10.1007/s10845-020-01703-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-020-01703-w