Abstract
Optimization of integrated process planning and scheduling has important practical significance for balancing the load of the process resources, shortening production cycle, and reducing production costs. An optimization algorithm base on ant colony optimization (ACO) for integrated process planning and scheduling is proposed, which can handle the dynamic emergency situation. Firstly, the representation mechanisms of candidate operation and the scheduling scheme construction mechanism is proposed. Then, the process constraints and time cost functions are given; based on this, the mathematical model is constructed. The ACO algorithm has been developed to solve the proposed mathematical model of integrated process planning and scheduling. The optimization algorithm is divided into two stages: the scheduling scheme optimization algorithm and dynamic emergency situation handling mechanism. The scheduling scheme optimization algorithm is used to get feasible and optimize scheduling scheme, and the dynamic emergency situation handling mechanism is used to handle dynamic emergency situation, such as inserting new parts. An example is also provided to demonstrate the effectiveness of the algorithm, and the computing results show that the proposed algorithm performs well in searching the good scheduling scheme.
Similar content being viewed by others
References
Tao F, Zhang L, Venkatesh VC et al (2011) Cloud manufacturing: a computing and service-oriented manufacturing model[J]. Proc Inst Mech Eng B J Eng Manuf 225:1969–1976
Tao F, Zhang L, Liu Y, et al. Manufacturing service management in cloud manufacturing: overview and future research directions[J]. Journal of Manufacturing Science & Engineering, 2015
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 & amp. Oper Res 37(4):656–667
Chryssolouris G, Chan S (1985) An integrated approach to process planning and scheduling. CIRP Ann 34(1):413
Gao Liang,Li Xinyu. Current researches on integrated process planning and scheduling. China mechanical engineering, 22(8): 1001–1007
Usher JM, Fernandes KJ (1996) Dynamic process planning—the static phase. J Mater Process Technol 61(1–2):53
Zhang HC, Merchant ME (1993) IPPM—a prototype to integrate process planning and job shop scheduling functions. CIRP Ann 42(1):513
Ju Quanyong, Zhu Jianyin. Study on the system of dynamic job shop scheduling based on combined genetic algorithm. China mechanical engineering, 18(1): 41–43
Li X, Gao L, Zhang C, Shao X (2010) A review on integrated process planning and scheduling. Int J Manuf Res 5(2):161–180
Jain A, Jain PK, Singh IP (2006) An integrated scheme for process planning and scheduling in FMS. Int J Adv Manuf Technol 30(11–12):1111–1118
Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Computers & amp. Ind Eng 59(1):166–180
Zhang L, Wong TN, Fung RYK: A multi-agent system for dynamic integrated process planning and scheduling using heuristics. Agent and multi-agent systems technologies and applications 2012:309–318
Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimisation in integrated process planning and scheduling. Robot Comput Integr Manuf 25(2):280–288
Li WD, McMahon CA (2007) A simulated annealing-based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20(1):80–95
Baykasoğlu A, Özbakır L (2009) A grammatical optimization approach for integrated process planning and scheduling. J Intell Manuf 20(2):211–221
Zhao F, Yi H, Yu D, Yang Y, Zhang Q (2010) A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. Int J Comput Integr Manuf 23(1):20–39
Li X, Gao L, Li W (2012) Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Syst Applic 39(1):288–297
Li X, Gao L, Shao X: An active learning genetic algorithm for integrated process planning and scheduling. Expert Systems with Applications (0).
Li X, Shao X, Gao L, Qian W (2010) An effective hybrid algorithm for integrated process planning and scheduling. Int J Prod Econ 126(2):289–298
Shao X, Li X, Gao L, Zhang C (2009) Integration of process planning and scheduling—a modified genetic algorithm-based approach. Computers and amp. Oper Res 36(6):2082–2096
Cai N, Wang L, Feng H-Y (2009) GA-based adaptive setup planning toward process planning and scheduling integration. Int J Prod Res 47(10):2745–2766
Shukla SK, Tiwari M, Son YJ (2008) Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-SA algorithm-oriented approach. Int J Adv Manuf Technol 38(1–2):163–175
Choy KL, Leung YK, Chow HKH, Poon TC, Ho GTS, Kwok SK (2011) A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment. Expert Syst Applic 38(3):1931
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
Wong TN, Zhang S, Wang G, Zhang L (2012) Integrated process planning and scheduling—multiagent system with two-stage ant colony optimisation algorithm. Int J Prod Res 50(21):2188–6201
Phanden RK, Jain A, Verma R (2012) An approach for integration of process planning and scheduling. Int J Computer Integr Manuf 1:1–19
Srinivas VRR PS, Rao CSP (2012) Optimization of process planning and scheduling using ACO and PSO algorithms. Int J Emerg Technol Adv Eng 2(10):343–354
Musharavati F (2012) Process planning with embedded scheduling for multi-parts production using simulated annealing. Int J Res Eng Technol 1(3):2277–4378
Seethaler RJ, Yellowley I (2000) Process control and dynamic process planning. Int J Mach Tools Manuf 40(2):239
Anosike AI, D.Z Z (2009) An agent-based approach for integrating manufacturing operations. Int J Prod Econ 121(2):333
Zhang YF, Saravanan AN, Fuh JYH (2003) Integration of process planning and scheduling by exploring the flexibility of process planning. Int J Prod Res 41(3):611–628
Chan FTS, Zhang J, Li P (2001) Modelling of integrated, distributed and cooperative process planning system using an agent-based approach. Proc Inst Mech Eng B J Eng Manuf 215(10):1437–1451
Wang L, Shen W (2003) DPP: an agent-based approach for distributed process planning. J Intell Manuf 14(5):429–439
Wu SH, Fuh JYH, Nee AYC (2002) Concurrent process planning and scheduling in distributed virtual manufacturing. IIE Trans 34(1):77–89
Zhang J, Gao L, Chan FTS, Li P (2003) A holonic architecture of the concurrent integrated process planning system. J Mater Process Technol 139(1–3):267–272
Dorigo M, Member IEEE, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst, Man Cybern-part B 26:1–13
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):52–56
Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling[J]. Comput Oper Res 30(2):1151–1171 (21)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, X., Ni, Z. & Qiu, X. Application of ant colony optimization algorithm in integrated process planning and scheduling. Int J Adv Manuf Technol 84, 393–404 (2016). https://doi.org/10.1007/s00170-015-8145-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-8145-4