Abstract
In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO).
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Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922. doi:10.1080/00207540110119991
Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30(8):1151–1171
Seok Shin K, Park J-O, Keun Kim Y (2011) Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Comput Oper Res 38(3):702–712
Liu X-j, Yi H, Ni Z-h (2010) Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing (in press)
Leo A, Hongchao Z (1989) Computer-aided process planning: the state-of-the-art survey. Int J Prod Res 27(4):553
Marri HB, Gunasekaran A, Grieve RJ (1998) Computer-aided process planning: a state of art. Int J Adv Manuf Technol 14(4):261–268. doi:10.1007/bf01199881
Zhang F, Zhang YF, Nee AYC (1997) Using genetic algorithms in process planning for job shop machining. Evol Comput IEEE Transac 1(4):278–289
Qiao L, Wang X-Y, Wang S-C (2000) A GA-based approach to machining operation sequencing for prismatic parts. Int J Prod Res 38(14):3283–3303
Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing-based optimization algorithm for process planning. Int J Prod Res 38(12):2671–2687
Li WD, Ong SK, Nee AYC (2004) Optimization of process plans using a constraint-based tabu search approach. Int J Prod Res 42(10):1955–1985. doi:10.1080/00207540310001652897
Li L, Fuh JYH, Zhang YF, Nee AYC (2005) Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments. Robot Comput-Integr Manuf 21(6):568–578
Guo Y, Mileham A, Owen G, Li W (2006) Operation sequencing optimization using a particle swarm optimization approach. Proc Inst Mech Eng, Part B: J Eng Manuf 220(12):1945–1958
Salehi M, Tavakkoli-Moghaddam R (2009) Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning. Eng Appl Artif Intell 22(8):1179–1187
Shao X, Li X, Gao L, Zhang C (2009) Integration of process planning and scheduling—a modified genetic algorithm-based approach. Comput Oper Res 36(6):2082–2096
Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Comput Ind Eng 59(1):166–180
Li X, Gao L, Shao X, Zhang C, Wang C (2010) Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling. Comput Oper Res 37(4):656–667
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary Computation. CEC 2007. IEEE Congress on, p 2007. pp 4661–4667
Atashpaz-Gargari E, Caro L (2007) Designing an optimal PID controller using Colonial Competitive Algorithm. In: First Iranian Joint Congress on Intelligent and Fuzzy Systems
Atashpaz-Gargari E, Hashemzadeh F, Lucas C (2008) Designing MIMO PIID controller using colonial competitive algorithm: applied to distillation column process. In: Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on. pp 1929–1934
Gargari EA, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. Int J Intell Comput Cybern 1(3):337–355. doi:10.1108/17563780810893446
Rajabioun R, Atashpaz-Gargari E, Lucas C (2008) Colonial competitive algorithm as a tool for Nash equilibrium point achievement. In: Gervasi O, Murgante B, Laganà A, Taniar D, Mun Y, Gavrilova M (eds) Computational science and its applications—ICCSA 2008, vol 5073. Lecture Notes in Computer Science. Springer Berlin, Heidelberg, pp 680–695. doi:10.1007/978-3-540-69848-7_55
Khabbazi A, Gargari EA, Lucas C (2009) Imperialist competitive algorithm for minimum bit error rate beamforming. Int J Bio-Inspir Comput 1(1/2):125–133. doi:10.1504/IJBIC.2009.022781
Forouharfard S, Zandieh M (2010) An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems. Int J Adv Manuf Technol 51(9):1179–1193. doi:10.1007/s00170-010-2676-5
Kaveh A, Talatahari S (2010) Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct 88(21–22):1220–1229
Lucas C, Nasiri-Gheidari Z, Tootoonchian F (2010) Application of an imperialist competitive algorithm to the design of a linear induction motor. Energy Convers Manag 51(7):1407–1411
Nazari-Shirkouhi S, Eivazy H, Ghodsi R, Rezaie K, Atashpaz-Gargari E (2010) Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm. Expert Syst Appl 37(12):7615–7626
Sarayloo F, Tavakkoli-Moghaddam R (2010) Imperialistic competitive algorithm for solving a dynamic cell formation problem with production planning. In: Huang D-S, Zhao Z, Bevilacqua V, Figueroa J (eds) Advanced Intelligent Computing Theories and Applications, vol 6215. Lecture Notes in Computer Science. Springer Berlin, Heidelberg, pp 266–276. doi:10.1007/978-3-642-14922-1_34
Sayadnavard MH, Haghighat AT, Abdechiri M Wireless sensor network localization using imperialist competitive algorithm. In: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 2010. pp 818–822
Shokrollahpour E, Zandieh M, Dorri B (2010) A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. Int J Prod Res 49(11):3087–3103
Moghimi Hadji M, Vahidi B (2011) A solution to the unit commitment problem using imperialistic competition algorithm. Power Syst, IEEE Trans on PP 99:1–1
Bagher M, Zandieh M, Farsijani H (2010) Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm. The International Journal of Advanced Manufacturing Technology: 1–15. doi:10.1007/s00170-010-2937-3
Niknam T, Taherian Fard E, Pourjafarian N, Rousta A (2011) An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng Appl Artif Intell 24(2):306–317
Abdechiri M, Faez K, Bahrami H (2010a) Adaptive imperialist competitive algorithm (AICA). In: Cognitive Informatics (ICCI). 9th IEEE International Conference on, p 2010. pp 940–945
Abdechiri M, Faez K, Bahrami H (2010b) Neural network learning based on chaotic imperialist competitive algorithm. In: Intelligent Systems and Applications (ISA). 2nd International Workshop on, p 2010. pp 1–5
Bahrami H, Faez K, Abdechiri M (2010) Imperialist competitive algorithm using chaos theory for optimization (CICA). In: Computer Modelling and Simulation (UKSim). 12th International Conference on, p 2010. pp 98–103
Duan H, Xu C, Liu S, Shao S (2010) Template matching using chaotic imperialist competitive algorithm. Pattern Recognit Lett 31(13):1868–1875
Karimi N, Zandieh M, Najafi AA (2010) Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism. International Journal of Production Research (in press)
Ho YC, Moodie CL (1996) Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities. Int J Prod Res 34(10):2901–2923
Tseng HE (2006) Guided genetic algorithms for solving a larger constraint assembly problem. Int J Prod Res 44(3):601–625. doi:10.1080/00207540500270513
Kim YK (2003) A set of data for the integration of process planning and job shop scheduling. http://syslab.chonnam.ac.kr/links/data-pp&s.doc.
Test-bed problems for multi-objective FMS process planning using multi-objective symbiotic evolutionary algorithm. (2010) http://syslab.chonnam.ac.kr/links/MO_FMS_PP_MOSEA.doc.
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Lian, K., Zhang, C., Shao, X. et al. Optimization of process planning with various flexibilities using an imperialist competitive algorithm. Int J Adv Manuf Technol 59, 815–828 (2012). https://doi.org/10.1007/s00170-011-3527-8
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DOI: https://doi.org/10.1007/s00170-011-3527-8