Skip to main content

Advertisement

Log in

Optimisation of production scheduling for multi-product orders in VCIM systems using GA

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Virtual computer-integrated manufacturing (VCIM) is a global integrated manufacturing system which can exploit locally as well as globally distributed manufacturing resources. Production scheduling plays an important role in the success of a VCIM system. In this paper, an innovative genetic algorithm (GA) is developed to search for optimal/sub-optimal solutions to the production scheduling problem in VCIM systems. The developed GA has a unique chromosome representation, two modified crossovers, three modified mutations, dynamic ranking selection, adaptive stop-and-restart-with-memory mechanism, and a parameter set tuned by the response surface method. The effectiveness of the developed GA is validated through a comprehensive case study. The computational data from the case study show that the developed GA outperforms three commercial optimisation solvers. The outcomes of this research serve as a foundation towards a global decision support system that can help decision makers to operate VCIM systems more effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Asawasakulsorn A (2009) Transportation collaboration: partner selection criteria and IOS design issues for supporting trust. Int J Bus Inf 4(2):199–220

    Google Scholar 

  • Boudissa E, Bounekhla M (2012) Genetic algorithm with dynamic selection based on quadratic ranking applied to induction machine parameters estimation. Electr Power Compon Syst 40(10):1089–1104

    Article  Google Scholar 

  • Chan FTS, Zhang T (2011) The impact of collaborative transportation management on supply chain performance: a simulation approach. Expert Syst Appl 38(3):2319–2329

    Article  Google Scholar 

  • Cheng C-H, Huang Y-H, Chen H-C (2016) Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm. Soft Comput 20(10):4139–4148

    Article  Google Scholar 

  • Dai B, Chen H (2009) Mathematical model and solution approach for collaborative logistics in less than truckload (LTL) transportation. In: International conference on computers and industrial engineering, pp 767–772

  • Dao SD, Marian R (2011) Modeling and optimisation of precedence-constrained production sequencing and scheduling using multi-objective genetic algorithms. In: Proceedings of the world congress on engineering, 6–8 July, London, U.K., pp 1027–1032

  • Dao SD, Abhary K, Marian R (2012) Optimisation of resource scheduling in VCIM systems using genetic algorithm. Int J Adv Res Artif Intell 1(8):49–56

    Google Scholar 

  • Dao SD, Abhary K, Marian R (2014) Optimisation of partner selection and collaborative transportation scheduling in virtual enterprises using GA. Expert Syst Appl 41(15):6701–6717

    Article  Google Scholar 

  • Dao SD, Abhary K, Marian R (2016a) An innovative model for resource scheduling in VCIM systems. Oper Res Int J. https://doi.org/10.1007/s12351-016-0252-y

    Google Scholar 

  • Dao SD, Abhary K, Marian R (2016b) A stochastic production scheduling model for VCIM systems. Intell Ind Syst 2(1):85–101

    Article  Google Scholar 

  • Dao SD, Abhary K, Marian R (2017) An integrated production scheduling model for multi-product orders in VCIM systems. Int J Syst Assur Eng Manag 8(1):12–27

    Article  Google Scholar 

  • Hoos HH, Stützle T (2014) On the empirical scaling of run-time for finding optimal solutions to the travelling salesman problem. Eur J Oper Res 238(1):87–94

    Article  MathSciNet  MATH  Google Scholar 

  • Kumar P, Gospodaric D, Bauer P (2007) Improved genetic algorithm inspired by biological evolution. Soft Comput 11(10):923–941

    Article  Google Scholar 

  • Li S, Wu X, Tan M (2008) Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft Comput 12(11):1039–1048

    Article  Google Scholar 

  • Ling SH, Leung FHF (2007) An improved genetic algorithm with average-bound crossover and wavelet mutation operations. Soft Comput 11(1):7–31

    Article  MATH  Google Scholar 

  • Mollaiy-Berneti S (2016) Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry. Soft Comput 20(12):4897–4906

    Article  Google Scholar 

  • Nagalingam SV, Lin GCI, Wang D (2007) Resource scheduling for a virtual CIM system. In: Wang L, Shen W (eds) Process planning and scheduling for distributed manufacturing. Springer, London, pp 269–294

    Chapter  Google Scholar 

  • Stefansson H, Sigmarsdottir S, Jensson P, Shah N (2011) Discrete and continuous time representations and mathematical models for large production scheduling problems: a case study from the pharmaceutical industry. Eur J Oper Res 215(2):383–392

    Article  MathSciNet  MATH  Google Scholar 

  • Tawhid MA, Ali AF (2017) A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function. Soft Comput 21(21):6499–6514

    Article  Google Scholar 

  • Wang D (2007) The development of an agent-based architecture for virtual CIM. Ph.D. thesis, University of South Australia, Adelaide

  • Wang D, Nagalingam SV, Lin GCI (2004) Development of a parallel processing multi-agent architecture for a virtual CIM system. Int J Prod Res 42(17):3765–3785

    Article  Google Scholar 

  • Wang D, Nagalingam SV, Lin GCI (2007) Development of an agent-based virtual CIM architecture for small to medium manufacturers. Robot Comput Integr Manuf 23(1):1–16

    Article  Google Scholar 

  • Yang K, El-Haik B (2003) Design for six sigma: a roadmap for product development. McGraw-Hill, New York

    Google Scholar 

  • Zhou N, Xing K, Nagalingam SV (2010a) An agent-based cross-enterprise resource planning for small and medium enterprises. IAENG Int J Comput Sci 37(3):1–7

    Google Scholar 

  • Zhou N, Xing K, Nagalingam SV, Lin GCI (2010b) Development of an agent based VCIM resource scheduling process for small and medium enterprises. In: Proceedings of the international multiconference of engineers and computer scientists, pp 39–44

  • Zhou N, Nagalingam SV, Xing K, Lin GCI (2011) Inside virtual CIM: multi-agent based resource integration for small to medium sized manufacturing enterprises. In: Ao S-I, Castillo O, Huang X (eds) Intelligent control and computer engineering, vol 70. Springer, Dordrecht, pp 163–175

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Son Duy Dao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any study with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs. 9, 10 and Tables 16, 17, 18, 19, 20, 21.

Fig. 9
figure 9

Effect of PS solver parameters on total cost

Fig. 10
figure 10

Effect of SA solver parameters on total cost

Table 16 Parameters of PS solver and their experimental levels
Table 17 Experiment layout L18 (21 33) and data for tuning the parameters of PS solver
Table 18 Selected parameters of PS solver
Table 19 Parameters of SA solver and their experimental levels
Table 20 Experiment layout L18 (21 33) and data for tuning the parameters of SA solver
Table 21 Selected parameters of SA solver

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dao, S.D., Abhary, K., Marian, R. et al. Optimisation of production scheduling for multi-product orders in VCIM systems using GA. Soft Comput 23, 10199–10224 (2019). https://doi.org/10.1007/s00500-018-3578-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3578-y

Keywords

Navigation