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Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design

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  • Moncayo-Martínez, Luis A.
  • Zhang, David Z.

Abstract

This paper proposes a new approach to determining the Supply Chain (SC) design for a family of products comprising complex hierarchies of subassemblies and components. For a supply chain, there may be multiple suppliers that could supply the same components as well as optional manufacturing plants that could assemble the subassemblies and the products. Each of these options is differentiated by a lead-time and cost. Given all the possible options, the supply chain design problem is to select the options that minimise the total supply chain cost while keeping the total lead-times within required delivery due dates. This work proposes an algorithm based on Pareto Ant Colony Optimisation as an effective meta-heuristic method for solving multi-objective supply chain design problems. An experimental example and a number of variations of the example are used to test the algorithm and the results reported using a number of comparative metrics. Parameters affecting the performance of the algorithm are investigated.

Suggested Citation

  • Moncayo-Martínez, Luis A. & Zhang, David Z., 2011. "Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design," International Journal of Production Economics, Elsevier, vol. 131(1), pages 407-420, May.
    • Handle: RePEc:eee:proeco:v:131:y:2011:i:1:p:407-420
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    References listed on IDEAS

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    Cited by:

    1. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    2. M. H. Alavidoost & Mosahar Tarimoradi & M. H. Fazel Zarandi, 2018. "Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 809-826, April.
    3. Yu-Hsin Chen, Gary, 2013. "A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems," International Journal of Production Economics, Elsevier, vol. 142(2), pages 362-371.
    4. Vahid Nooraie, S. & Parast, Mahour Mellat, 2016. "Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 8-21.
    5. Cardona-Valdés, Y. & Álvarez, A. & Pacheco, J., 2014. "Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 66-84.
    6. De Vincenzo, Ilario & Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe & Grigolini, Paolo, 2018. "Mimicking the collective intelligence of human groups as an optimization tool for complex problems," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 259-266.
    7. Boxuan Zhao & Jianmin Gao & Kun Chen & Ke Guo, 2018. "Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 93-108, January.
    8. Abdelsalam, Hisham M. & Elassal, Magy M., 2014. "Joint economic lot sizing problem for a three—Layer supply chain with stochastic demand," International Journal of Production Economics, Elsevier, vol. 155(C), pages 272-283.
    9. Olivares-Benitez, Elias & Ríos-Mercado, Roger Z. & González-Velarde, José Luis, 2013. "A metaheuristic algorithm to solve the selection of transportation channels in supply chain design," International Journal of Production Economics, Elsevier, vol. 145(1), pages 161-172.
    10. Moncayo-Martínez, Luis A. & Zhang, David Z., 2013. "Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAX–MIN ant system," International Journal of Production Economics, Elsevier, vol. 145(1), pages 18-28.

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