Skip to main content
SpringerLink
Log in

Evolutionary algorithms for supply chain management

  • Introduction
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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

Access this article

Log in via an institution

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

References

  • Bäck, T. (1996). Evolutionary algorithms in theory and practice. Oxford University Press.

  • Chen, G., Govindan, K., Yang, Z.-Z., Choi, T.-M., & Jiang, L. (2013). Terminal appointment system design by non-stationary M(t)/E/k/c(t) queueing model and genetic algorithm. International Journal of Production Economics, 146(2), 694–703.

  • Cheng, T. C. E., Peng, B., & Lü, Z. (2016). A hybrid evolutionary algorithm to solve the job shop scheduling problem. Annals of Operations Research, 1–15. (this issue).

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). Wiley.

  • Devika, K., Jafarian, A., Hassanzadeh, A., & Khodaverdi, R. (2016). Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics. Annals of Operations Research, 1–31. (this issue).

  • Diabat, A., Abdallah, T., & Le, T. (2016). A hybrid tabu search based heuristic for the periodic distribution inventory problem with perishable goods. Annals of Operations Research, 1–26. (this issue).

  • Do, N. A. D., Nielsen, I. E., Chen, G., & Nielsen, P. (2016). A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal. Annals of Operations Research, 1–17. (this issue).

  • Fahimnia, B., Davarzani, H., & Eshragh, A. (2015). Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms. Computers & Operations Research. doi:10.106/j.cor.2015.10.008.

  • Fattahi, M., Mahootchi, M., & Husseini, S. M. (2016). Integrated strategic and tactical supply chain planning with price-sensitive demands. Annals of Operations Research, 1–34. (this issue).

  • Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & OperationsResearch, 62, 112–130.

  • Grahl, J., Minner, S., & Dittmar, D. (2016). Meta-heuristics for placing strategic safety stock in multi-echelon inventory with differentiated service times. Annals of Operations Research, 1–16. (this issue).

  • Haq, A. N., & Kannan, G. (2006). Two-echelon distribution-inventory supply chain model for the bread industry using genetic algorithm. International Journal of Logistics Systems and Management, 2(2), 177–193.

  • Jourdan, L., Basseur, M., & Talbi, E. G. (2009). Hybridizing exact methods and metaheuristics: A taxonomy. European Journal of Operational Research, 199(3), 620–629.

  • Kalayci, C. B., Polat, O., & Gupta, S. M. (2016). A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Annals of Operations Research, 1–34. (this issue).

  • Kannan, G. (2009). A metaheuristics-based decision support system for the performance measurement of reverse supply chain management. International Journal of Business Performance Management, 11(1–2), 152–169.

  • Kannan, G., Noorul Haq, A., & Devika, M. (2009). Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. International Journal of Production Research, 47(5), 1175–1200.

  • Kannan, G., Sasikumar, P., & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34(3), 655–670.

  • Khan, B. S. H., Govindan, K., & Jeyapaul, R. (2010). Optimisation of genetic algorithm parameters in flow shop scheduling using grey relational analysis. International Journal of Advanced Operations Management, 2(1–2), 25–45.

  • Li, X., Sun, G., & Li, Y. (2016). A multi-period ordering and clearance pricing model considering the competition between new and out-of-season products. Annals of Operations Research, 1–15. (this issue).

  • Li, Y., Cai, X., Xu, L., & Yang, W. (2016). Heuristic approach on dynamic lot-sizing model for durable products with end-of-use constraints. Annals of Operations Research, 1–19. (this issue).

  • Lieckens, K., & Vandaele, N. (2016). Differential evolution to solve the lot size problem in stochastic supply chain management systems. Annals of Operations Research, 1–25. (this issue).

  • Noorul Haq, A., & Kannan, G. (2006). Effect of forecasting on the multi-echelon distribution inventory supply chain cost using neural network, genetic algorithm and particle swarm optimisation. International Journal of Services Operations and Informatics, 1(1–2), 1–22.

  • Pan, A., & Choi, T. M. (2016). An agent-based negotiation model on price and delivery date in a fashion supply chain. Annals of Operations Research, 1-29. (this issue).

  • Soleimani, H., & Govindan, K. (2014). Reverse logistics network design and planning utilizing conditional value at risk. European Journal of Operational Research, 237(2), 487–497.

  • Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990–4012.

  • Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2016). A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design. Annals of Operations Research, 1–23. (this issue).

  • Thiruvady, D., Ernst, A. T., & Singh, G. (2016). Parallel ant colony optimization for resource constrained job scheduling. Annals of Operations Research, 1–18. (this issue).

  • Wei, J., & Zhao, J. (2016). Pricing decisions for substitutable products with horizontal and vertical competition in fuzzy environments. Annals of Operations Research, 1–24. (this issue).

  • Yao, B., Yu, B., Hu, P., Gao, J., & Zhang, M. (2016). An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot. Annals of Operations Research, 1–18. (this issue).

Download references

Acknowledgments

We would like to thank all the authors who have submitted papers for the special issue and the reviewers who have reviewed manuscripts in a timely manner. Special thanks to the Editor in Chief of ANOR, Professor Endre Boros and to journal manager Katie D’Agosta for their constant support right from the beginning until the editorial process was approved.

Author information

Authors and Affiliations

  1. Centre for Engineering Operations Management, Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark

    Kannan Govindan

Authors
  1. Kannan Govindan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kannan Govindan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Govindan, K. Evolutionary algorithms for supply chain management. Ann Oper Res 242, 195–206 (2016). https://doi.org/10.1007/s10479-016-2227-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2227-z

Keywords

Search

Navigation