2021, Volume 7, Issue 6
Engineering >> 2021, Volume 7, Issue 6 doi: 10.1016/j.eng.2021.04.011
A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System
State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract
Intelligent process planning (PP) is one of the most important components in an intelligent manufacturing system and acts as a bridge between product designing and practical manufacturing. PP is a nondeterministic polynomial-time (NP)-hard problem and, as existing mathematical models are not formulated in linear forms, they cannot be solved well to achieve exact solutions for PP problems. This paper proposes a novel mixed-integer linear programming (MILP) mathematical model by considering the network topology structure and the OR nodes that represent a type of OR logic inside the network. Precedence relationships between operations are discussed by raising three types of precedence relationship matrices. Furthermore, the proposed model can be programmed in commonly-used mathematical programming solvers, such as CPLEX, Gurobi, and so forth, to search for optimal solutions for most open problems. To verify the effectiveness and generality of the proposed model, five groups of numerical experiments are conducted on well-known benchmarks. The results show that the proposed model can solve PP problems effectively and can obtain better solutions than those obtained by the state-ofthe- art algorithms.
Keywords
Process planning ; Network ; Mixed-integer linear programming ; CPLEX
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References
[ 1 ] Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20. link1
[ 2 ] Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of industry 4.0: a review. Engineering 2017;3(5):616–30. link1
[ 3 ] Zhou J, Zhou Y, Wang B, Zang J. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering 2019;5 (4):624–36. link1
[ 4 ] Leo Kumar SP. State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Eng Appl Artif Intell 2017;65:294–329. link1
[ 5 ] Miljkovic´ Z, Petrovic´ M. Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem. Int J Comput Integrated Manuf 2017;30(2–3):271–91. link1
[ 6 ] Xu X, Wang L, Newman ST. Computer-aided process planning—a critical review of recent developments and future trends. Int J Comput Integrated Manuf 2011;24(1):1–31. link1
[ 7 ] Li X, Gao L, Pan Q, Wan L, Chao KM. An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop. IEEE Trans Syst Man Cybern Syst 2019;49 (10):1933–45. link1
[ 8 ] 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 (5–8):1355–69. link1
[ 9 ] Gan PY, Lee KS, Zhang YF. A branch and bound algorithm based processplanning system for plastic injection mould bases. Int J Adv Manuf Technol 2001;18(9):624–32. link1
[10] Wen X, Li X, Gao L, Sang H. Honey bees mating optimization algorithm for process planning problem. J Intell Manuf 2014;25(3):459–72. link1
[11] Zhang J, Xiao Mi, Gao L, Pan Q. Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Appl Math Model 2018;63:464–90. link1
[12] Liu Q, Li X, Gao L, Li Y. A modified genetic algorithm with new encoding and decoding methods for integrated process planning and scheduling problem. IEEE Trans Cybern. Epub 2020 Oct 15. link1
[13] Li WD, Ong SK, Nee AYC. Optimization of process plans using a constraint-based tabu search approach. Int J Prod Res 2004;42(10):1955–85. link1
[14] Liu X, Yi H, Ni Z. Application of ant colony optimization algorithm in process planning optimization. J Intell Manuf 2013;24(1):1–13. link1
[15] Li XY, Shao XY, Gao L. Optimization of flexible process planning by genetic programming. Int J Adv Manuf Technol 2008;38(1–2):143–53. link1
[16] Jin L, Zhang C. Process planning optimization with energy consumption reduction from a novel perspective: mathematical modeling and a dynamic programming-like heuristic algorithm. IEEE Access 2019;7:7381–96. link1
[17] Jiang Z, Jiang Ya, Wang Y, Zhang H, Cao H, Tian G. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf 2019;30(1):19–32. link1
[18] Jin L, Tang Q, Zhang C, Shao X, Tian G. More MILP models for integrated process planning and scheduling. Int J Prod Res 2016;54(14):4387–402. link1
[19] Li WD, Ong SK, Nee AYC. Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 2002;40(8):1899–922. link1
[20] Hua G, Zhou X, Ruan X. GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts. Int J Adv Manuf Technol 2007;33(5–6):594–603. link1
[21] Shin KS, Park JO, Kim YK. Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Comput Oper Res 2011;38(3):702–12. link1
[22] Wang YF, Zhang YF, Fuh JYH. A hybrid particle swarm based method for process planning optimisation. Int J Prod Res 2012;50(1):277–92. link1
[23] Shabaka AI, ElMaraghy HA. A model for generating optimal process plans in RMS. Int J Comput Integrated Manuf 2008;21(2):180–94. link1
[24] Floudas CA, Lin X. Mixed integer linear programming in process scheduling: modeling, algorithms, and applications. Ann Oper Res 2005;139(1): 131–62. link1
[25] Li X, Gao L, Shao X, Zhang C, Wang C. Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling. Comput Oper Res 2010;37(4):656–67. link1
[26] Xia Q, Etienne A, Dantan J, Siadat A. Reconfigurable machining process planning for part variety in new manufacturing paradigms: definitions, models and framework. Comput Ind Eng 2018;115:206–19. link1
[27] Lee K, Jung M. Petri net application in flexible process planning. Comput Ind Eng 1994;27(1–4):505–8. link1
[28] Sormaz D, Sarkar A. Process sequencing problem in distributed manufacturing process planning. In: Goldengorin B, editor. Optimization problems in graph theory. Berlin: Springer; 2018. p. 293–324. link1
[29] Kim YK, Park K, Ko J. A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 2003;30 (8):1151–71. link1
[30] Zhang S, Wong TN. Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning. J Intell Manuf 2018;29(3):585–601. link1
[31] Pan CH. A study of integer programming formulations for scheduling problems. Int J Syst Sci 1997;28(1):33–41. link1
[32] Fattahi P, Saidi Mehrabad M, Jolai F. Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf 2007;18 (3):331–42. link1
[33] Zhang L, Tang Q, Wu Z, Wang F. Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy 2017;138:210–27. link1
[34] Özgüven C, Özbakır L, Yavuz Y. Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Appl Math Model 2010;34 (6):1539–48. link1
[35] Liu Q, Li X, Gao L. Mathematical modeling and a hybrid evolutionary algorithm for process planning. J Intell Manuf 2021;32(3):781–97. link1
[36] Zhang Y, Nee A. Application of genetic algorithms and simulated annealing in process planning optimization. In: Pham DT, editor. Computational intelligence in manufacturing handbook. Boca Raton: CRC Press; 2000. p. 243–68. link1
[37] Li WD, McMahon CA. A simulated annealing-based optimization approach for integrated process planning and scheduling. Int J Comput Integrated Manuf 2007;20(1):80–95. link1
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