Abstract
In recent years, integrated process planning and scheduling models have been proposed as solutions that can bridge the gap between practical process planning and production scheduling. However, most structures of these models have been algorithm-based and hence may not be very useful when a problem contains process and operational aspects that are difficult to capture in an algorithm template. In dynamic manufacturing environments, examples of such aspects include process and operational flexibilities that enable manufacturers to cope with unexpected variations in production and product mix. Appropriate process planning models that take cognizance of such aspects can be proven more useful to human process planners. In this paper, an innovative multiple parts process planning (MPPP) model for solving process planning problems with process and operational flexibilities is introduced. This model strikes a balance between process- and operations-related meta-data in a bid to capture process and operational flexibilities in the search for an optimal process planning solution. Merits of this model are discussed with reference to the operations of a typical serial-parallel flexible flow line. An illustrative example of the modeling framework is outlined. In seeking a feasible solution, a relative comparative analysis is carried out between; (a) a simulated annealing (SA) algorithm and (b) a simulated annealing algorithm that implements a mutation operator. Results show that the SA algorithm with a mutation operator outperforms the SA algorithm without a mutation operator.
Similar content being viewed by others
References
Mehrabi MG, Ulsoy AG, Koren Y, Heytler P (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems. J Intell Manuf 13(2):135–146
ElMaraghy HA (2006) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17:261–276
Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing system and their enabling technologies. Int J Manuf Technol Manag 1(1):113–130
Zhang YF, Nee AYC (2001) Applications of genetic algorithms and simulated annealing in process planning optimization. In: J. Wang and A. Kusiak (ed) Computational intelligence in manufacturing handbook. Boca Raton, Florida, pp 9-1–9-26
Lee H, Kim SS (2001) Integration of process planning and scheduling using simulation based genetic algorithms. Int J Adv Manuf Technol 18(8):586–590
Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171
Ro I, Kim J (1990) Multi-criteria operational control rules in flexible manufacturing systems (FMSs). Int J Prod Res 28(1):47–63
Musharavati F (2008) Process planning optimization for reconfigurable manufacturing systems. Boca Raton, Florida USA
ElMaraghy HA (2007) Reconfigurable process plans for responsive manufacturing systems. In: Cunha P.F. and Maropulos P.G. (eds) Digital enterprise technology: perspectives and future challenges. Springer Science, pp 35–44
Azab A, ElMaraghy HA (2007) Mathematical modeling for reconfigurable process planning. Ann CIRP 56(1):467–472
Azab A, Perusi G, ElMaraghy H, Urbanic J (2007) Semi-generative process planning for reconfigurable manufacturing. In: Cunha P.F. and Maropulos P.G. (eds) Digital enterprise technology: perspectives and future challenges. Springer Science, pp 251–258
Xinyu L, Liang G, Xiaoyu W (2012) Application of an efficient modified particle swarm optimization algorithm for process planning. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4572-7
Kunlei L, Chaoyong Z, Xinyu S, Liang G (2012) Optimization of process planning with various flexibilities using an imperialist competitive algorithm. Int J Adv Manuf Technol 59:815–828
Musharavati F, Hamouda ASM (2011) Enhanced simulated‐annealing‐based algorithms and their applications to process planning in reconfigurable manufacturing systems. Adv Eng Softw 45(1):80–90
Musharavati F, Hamouda ASM (2012) Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing. J Robot Comput-Integr Manuf 28(2):113–131
Rauschecker U, Ford JS, and Athanssopoulou N (2013) Developing a vision for multi-site manufacturing system of systems. In: M.F. Zaeh (ed.) 5th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2013), Munich, Germany, 79
Mehdi G, Bouzouia B, Achour N (2014) An evolutionary simulation-optimization approach to product-driven manufacturing control. In: Service orientation in holonic and multi-agent manufacturing and robotics. Springer International Publishing, 283–294
Paulo L, Barbosa J, Trentesaux D (2012) Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Eng Appl Artif Intell 25(5):934–944
Hollnagel E, Woods DD, Levesson N (eds) (2006) Resilience engineering: concepts and precepts. Ashgate, Hampshire
Zhang WJ, Van Luttervelt CA (2011) Toward a resilient manufacturing system. CIRP Annals-Manuf Technol 60(1):469–472
Yao H (2013) The modeling, analysis and control of resilient manufacturing enterprises. Dissertation, University of Kentucky
Engelke W D (1987) How to integrate CAD/CAM systems: management and technology, pp. 237–238. CRC Press
Cay F, Chassapis C An IT view on perspectives of computer aided process planning research. Comput Ind 34 (3): 307–337
Xu X, Wang L, Newman ST (2011) Computer-aided process planning—a critical review of recent developments and future trends. Int J Comput Integr Manuf 24(1–3):1–31
Phanden RK, Jain A, Verma R (2011) Integration of process planning and scheduling: a state-of-the-art review. Int J Comput Integr Manuf 24(6):517–534
Rembold U, Nnaji BO, Storr A (1993) Computer integrated manufacturing and engineering. Addison-Wesley Longman
Scallan P (2003) Process planning: the design/manufacture interface. Butterworth and Heinemann, Boston Massachusetts
Tsujimura Y, Gen M (1999) Parts loading scheduling in flexible forging machine using advanced genetic algorithm. J Intell Manuf 10:149–159
Guo Y, Mileham AR, Owen GW, Li WD (2006) Operation sequencing optimization using a particle swarm optimization approach. Proc Inst Mech Eng Part B: J Eng Manuf 220:945–1958
Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing-based optimization algorithm for process planning. Int J Prod Res 38(12):2671–2687
Zijm WHM (1995) The integration of process planning and shop floor scheduling in small batch part manufacturing. Ann CIRP 44(1):429–432
Lim MK, Zhang Z (2003) A multi-agent based manufacturing control strategy for responsive manufacturing. J Mater Process Technol 139:379–384
Lim MK, Zhang Z (2004) An integrated agent-based approach for responsive control of manufacturing resources. Comput Ind Eng 46:221–232
Palmer GJ (1996) A simulated annealing approach to integrated production scheduling. J Intell Manuf 7(3):163–176
Musharavati F (2012) Process planning with embedded scheduling for multi‐parts production using simulated annealing. Int J Res Eng Technol 1(3):129–138
Freiheit T, Shpitalni M, Hu SJ, Koren Y (2004) Productivity of synchronized serial production lines with flexible reserve capacity. Int J Prod Res 42:2009–2027
Lian KL, Zhang CY, Gao L, Xu ST, Sun Y (2011) A cooperative simulated annealing algorithm for the optimization of process planning. Adv Mat Res 181:489–494
Nallakumarasamy G, Srinivasan PSS, Raja KV, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using simulated annealing technique (SAT). Int J Adv Manuf Technol 54(5–8):721–728
Zhang F, Zhang YF, Nee AYC (1997) Using genetic algorithms in process planning for job shop machining. IEEE Trans Evol Comput 1(4):278–289
Li L, Fuh JYH, Zhang YF, Nee AYC (2005) Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments. J Robot Comput-Integr Manuf 21(6):568–578
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
Qiao L, Shengping L (2012) An improved genetic algorithm for integrated process planning and scheduling. Int J Adv Manuf Technol 58(5–8):727–740
Musharavati F, Hamouda AMS (2011) Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines. Expert Syst Appl 38(9):10770–10779
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
Ganesh K, Punniyamoorthy M (2005) Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int J Adv Manuf Technol 26(1–2):148–154
Wang ZG, Wong YS, Rahman M (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24(9–10):727–732
Preux P, Talbi EG (1999) Towards hybrid evolutionary algorithms. Int Trans Oper Res 6(6):557–570
Zhang WJ, Ouyang PR, Sun Z H (2010) A novel hybridization design principle for intelligent mechatronics systems. In: Proceedings of international conference on advanced mechatronics (ICAM2010): 4–6
Ding L, Yue Y, Ahmet K, Jackson M, Parkin R (2005) Global optimization of a feature-based process sequence using GA and ANN techniques. Int J Prod Res 43:3247–3272
Huang W, Hu Y, Cai L (2012) An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts. Int J Adv Manuf Technol 62:1219–1232
Orouji H, Haddad OB, Fallah-Mehdipour E, Mariño MA (2013) Extraction of decision alternatives in project management: application of hybrid PSO-SFLA. J Manag Eng 30(1):50–59
Cakir B, Altiparmak F, Dengiz B (2011) Multi-objective optimization of a stochastic assembly line balancing: a hybrid simulated annealing algorithm. Comput Ind Eng 60(3):376–384
Bettemir ÖH, Sonmez R (2014) Hybrid genetic algorithm with simulated annealing for resource-constrained project scheduling. Journal of Management in Engineering
Salehi M, Bahreininejad A (2011) Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. J Intell Manuf 22(4):643–652
Ong SK, Ding J, Nee AYC (2002) Hybrid GA and SA dynamic set-up planning optimization. Int J Prod Res 40(18):4697–4719
Nallakumarasamy G, Srinivasan PSS, Raja KV, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using superhybrid genetic algorithms-simulated annealing technique. ISRN Mechanical Engineering, 2011
Mahmudy W F, Marian R M, and Luong L H (2013) Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms-part 1: modelling and representation. In: 5th IEEE International Conference on Knowledge and Smart Technology (KST), 2013 January: 75–80
Turban E, Aronson J E (2001) Decision support systems and intelligent systems. New Jersey: Prentice Hall, 6th ed
Onwubolu GC (2002) Emerging optimization techniques in production planning and control. Imperial College Press, London
Wang JW, Wang HF, Ip WH, Furuta K, Kanno T, Zhang WJ (2013) Predatory search strategy based on swarm intelligence for continuous optimization problems. Mathematical Problems in Engineering
Luong LHS, Kazerooni M, Abhary K. (2001) Genetic algorithms in manufacturing systems design. In: J. Wang and A. Kusiak (eds) Computational intelligence in manufacturing handbook. Boca Raton, Florida USA, pp. 6-1–6-25
Hallinan J (2001) Feature selection and classification in the diagnosis of cervical cancer. In: Chambers L (ed) The practical handbook of genetic algorithms. Chapman and Hall/CRC, Boca Raton, pp 167–202
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Musharavati, F., Hamouda, A.M.S. Multiple parts process planning in serial-parallel flexible flow lines: part I—process plan modeling framework. Int J Adv Manuf Technol 78, 115–137 (2015). https://doi.org/10.1007/s00170-014-6616-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-014-6616-7