Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.

Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering Technical University of Ostrava 70800 Ostrava Czech Republic

Department of Mechanical Engineering Technical Faculty Mihajlo Pupin University of Novi Sad 23000 Zrenjanin Serbia

Department of Production Engineering Faculty of Technical Sciences University of Novi Sad 21000 Novi Sad Serbia

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Xu X., Wang L., Newman S.T. Computer-aided process planning—A critical review of recent developments and future trends. Int. J. Comput. Integr. Manuf. 2011;24:518632. doi: 10.1080/0951192X.2010.518632. DOI

Blum C., Roli A. Metaheuristics in combinatorial optimization. ACM Comput. Surv. 2003;35:268–308. doi: 10.1145/937503.937505. DOI

Zhang F., Zhang Y., Nee A. Using genetic algorithms in process planning for job shop machining. IEEE Trans. Evol. Comput. 1997;1:278–289. doi: 10.1109/4235.687888. DOI

Salehi M., Bahreininejad A. Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. J. Intell. Manuf. 2011;22:643–652. doi: 10.1007/s10845-010-0382-7. PubMed DOI PMC

Li W., Ong S.K., Nee A.Y.C. Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int. J. Prod. Res. 2002;40:1899–1922. doi: 10.1080/00207540110119991. DOI

Kafashi S. Integrated setup planning and operation sequencing (ISOS) using genetic algorithm. Int. J. Adv. Manuf. Technol. 2011;56:589–600. doi: 10.1007/s00170-011-3202-0. DOI

Cai N., Wang L., Feng H.-Y. GA-based adaptive setup planning toward process planning and scheduling integration. Int. J. Prod. Res. 2009;47:2745–2766. doi: 10.1080/00207540701663516. DOI

Huang W., Hu Y., Cai L. An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts. Int. J. Adv. Manuf. Technol. 2012;62:1219–1232. doi: 10.1007/s00170-011-3870-9. DOI

Li S., Liu Y., Li Y., Landers R.G., Tang L. Process planning optimization for parallel drilling of blind holes using a two phase genetic algorithm. J. Intell. Manuf. 2013;24:791–804. doi: 10.1007/s10845-012-0628-7. DOI

Su Y., Chu X., Zhang Z., Chen D. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach. Adv. Mech. Eng. 2015;7:1687814015581241. doi: 10.1177/1687814015581241. DOI

Su Y., Chu X., Chen D., Sun X. A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy. J. Intell. Manuf. 2018;29:313–332. doi: 10.1007/s10845-015-1109-6. DOI

Candan G., Yazgan H.R. Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem. Int. J. Prod. Res. 2014;53:897–915. doi: 10.1080/00207543.2014.939244. DOI

Luo Y., Pan Y., Li C., Tang H. A hybrid algorithm combining genetic algorithm and variable neighborhood search for process sequencing optimization of large-size problem. Int. J. Comput. Integr. Manuf. 2020;33:962–981. doi: 10.1080/0951192X.2020.1780318. DOI

Liu X.-J., Yi H., Ni Z.-H. Application of ant colony optimization algorithm in process planning optimization. J. Intell. Manuf. 2013;24:1–13. doi: 10.1007/s10845-010-0407-2. DOI

Wang J., Fan X., Wan S. A Graph-Based Ant Colony Optimization Approach for Process Planning. Sci. World J. 2014;2014:1–11. doi: 10.1155/2014/271895. PubMed DOI PMC

Wang J., Wu X., Fan X. A two-stage ant colony optimization approach based on a directed graph for process planning. Int. J. Adv. Manuf. Technol. 2015;80:839–850. doi: 10.1007/s00170-015-7065-7. DOI

Hu Q., Qiao L., Peng G. An ant colony approach to operation sequencing optimization in process planning. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2017;231:470–489. doi: 10.1177/0954405415616786. DOI

Li W., Wang L., Li X., Gao L. Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing. Springer; Singapore: 2011. Intelligent Optimisation for Integrated Process Planning and Scheduling; pp. 305–324.

Li X., Gao L., Wen X. Application of an efficient modified particle swarm optimization algorithm for process planning. Int. J. Adv. Manuf. Technol. 2013;67:1355–1369. doi: 10.1007/s00170-012-4572-7. DOI

Petrović M., Mitić M., Vuković N., Miljković Z. Chaotic particle swarm optimization algorithm for flexible process planning. Int. J. Adv. Manuf. Technol. 2016;85:2535–2555. doi: 10.1007/s00170-015-7991-4. DOI

Miljković Z., Petrović M. Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem. Int. J. Comput. Integr. Manuf. 2017;30:271–291. doi: 10.1080/0951192X.2016.1145804. DOI

Dou J., Li J., Su C. A discrete particle swarm optimisation for operation sequencing in CAPP. Int. J. Prod. Res. 2018;56:3795–3814. doi: 10.1080/00207543.2018.1425015. DOI

Lian K., Zhang C., Shao X., Gao L. Optimization of process planning with various flexibilities using an imperialist competitive algorithm. Int. J. Adv. Manuf. Technol. 2012;59:815–828. doi: 10.1007/s00170-011-3527-8. DOI

Wen X.-Y., Li X.-Y., Gao L., Sang H.-Y. Honey bees mating optimization algorithm for process planning problem. J. Intell. Manuf. 2014;25:459–472. doi: 10.1007/s10845-012-0696-8. DOI

Lv S., Qiao L. A cross-entropy-based approach for the optimization of flexible process planning. Int. J. Adv. Manuf. Technol. 2013;68:2099–2110. doi: 10.1007/s00170-013-4815-2. DOI

Wang J., Fan X., Zhao A., Yang M. A Hybrid Bat Algorithm for Process Planning Problem. IFAC-PapersOnLine. 2015;48:1708–1713. doi: 10.1016/j.ifacol.2015.06.332. DOI

Musharavati F., Hamouda A.S.M. Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems. Adv. Eng. Softw. 2012;45:80–90. doi: 10.1016/j.advengsoft.2011.09.017. DOI

Mohammadi G., Karampourhaghghi A., Samaei F. A multi-objective optimisation model to integrating flexible process planning and scheduling based on hybrid multi-objective simulated annealing. Int. J. Prod. Res. 2012;50:5063–5076. doi: 10.1080/00207543.2011.631602. DOI

Xu C., Zhang S., Huang R., Huang B., Li X. NC process reuse-oriented flexible process planning optimization approach for prismatic parts. Int. J. Adv. Manuf. Technol. 2016;87:329–351. doi: 10.1007/s00170-016-8460-4. DOI

Lian K., Zhang C., Shao X., Zeng Y. A multi-dimensional tabu search algorithm for the optimization of process planning. Sci. China Ser. E Technol. Sci. 2011;54:3211–3219. doi: 10.1007/s11431-011-4594-7. DOI

Falih A., Shammari A.Z.M. Hybrid constrained permutation algorithm and genetic algorithm for process planning problem. J. Intell. Manuf. 2020;31:1079–1099. doi: 10.1007/s10845-019-01496-7. DOI

Gao B., Hu X., Peng Z., Song Y. Application of intelligent water drop algorithm in process planning optimization. Int. J. Adv. Manuf. Technol. 2020;106:5199–5211. doi: 10.1007/s00170-019-04850-4. DOI

Kizys R., Juan A.A., Sawik B., Calvet L. A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization. Appl. Sci. 2019;9:3509. doi: 10.3390/app9173509. DOI

Sawik T., Sawik B. A rough cut cybersecurity investment using portfolio of security controls with maximum cybersecurity value. Int. J. Prod. Res. 2021:1–17. doi: 10.1080/00207543.2021.1994166. DOI

Milosevic M., Đurđev M., Lukić D., Antić A., Ungureanu N. Intelligent Process Planning for Smart Factory and Smart Manufacturing; Proceedings of the 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing; Belgrade, Serbia. 1–4 June 2020; Cham, Switzerland: Springer; 2020. pp. 205–214. DOI

Djurdjev M., Cep R., Lukic D., Antic A., Popovic B., Milosevic M. A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning. Appl. Sci. 2021;11:1981. doi: 10.3390/app11051981. DOI

Mirjalili S., Mirjalili S.M., Lewis A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014;69:46–61. doi: 10.1016/j.advengsoft.2013.12.007. DOI

Zhang Y., Luo X., Zhang B., Zhang S. Semantic approach to the automatic recognition of machining features. Int. J. Adv. Manuf. Technol. 2017;89:417–437. doi: 10.1007/s00170-016-9056-8. DOI

Li W.D., Ong S.K., Nee A.Y.C. (Series on Manufacturing Systems and Technology).Integrated and Collaborative Product Development Environment—Technologies and Implementations. 2006;Volume 2 doi: 10.1142/9789812774156. DOI

Faheem W., Hayes C., Castano J., Gaines D. In What is manufacturing interaction?; Proceedings of the DETC’98, ASME Design Engineering Technical Conferences; Atlanta, GA, USA. 13–16 September 1998.

Dou J., Zhao X., Su C. An Improved Genetic Algorithm for Optimization of Operation Sequencing; Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA); Changchun, China. 5–8 August 2018; pp. 695–700.

Guo Y.W., Mileham A.R., Owen G.W., Li W.D. Operation sequencing optimization using a particle swarm optimization approach. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2006;220:1945–1958. doi: 10.1243/09544054JEM647. DOI

Ahmed R., Nazir A., Mahadzir S., Shorfuzzaman M., Islam J. Niching Grey Wolf Optimizer for Multimodal Optimization Problems. Appl. Sci. 2021;11:4795. doi: 10.3390/app11114795. DOI

Yue Z., Zhang S., Xiao W. A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm. Sensors. 2020;20:2147. doi: 10.3390/s20072147. PubMed DOI PMC

Wang Y., Wang W. Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem. Mathematics. 2021;9:1233. doi: 10.3390/math9111233. DOI

Martin B., Marot J., Bourennane S. Improved Discrete Grey Wolf Optimizer; Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO); Rome, Italy. 3–7 September 2018; pp. 494–498.

Jiang T., Zhang C. Application of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases. IEEE Access. 2018;6:26231–26240. doi: 10.1109/ACCESS.2018.2833552. DOI

Qin H., Fan P., Tang H., Huang P., Fang B., Pan S. An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Comput. Ind. Eng. 2019;128:458–476. doi: 10.1016/j.cie.2018.12.061. DOI

Premkumar M., Jangir P., Kumar B.S., Alqudah M.A., Nisar K.S. Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem. Comput. Mater. Contin. 2022;70:2435–2452. doi: 10.32604/cmc.2022.016488. DOI

Zhang H., Buchmeister B., Li X., Ojstersek R. Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment. Mathematics. 2021;9:909. doi: 10.3390/math9080909. DOI