Skip to main content
placeholder image

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

Journal Article


Abstract


  • In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

UOW Authors


  •   Patne, Kalpit (external author)
  •   Shukla, Nagesh (external author)
  •   Kiridena, Senevi
  •   Tiwari, Manoj K. (external author)

Publication Date


  • 2018

Citation


  • Patne, K., Shukla, N., Kiridena, S. & Tiwari, M. (2018). Solving closed-loop supply chain problems using game theoretic particle swarm optimisation. International Journal of Production Research, 56 (17), 5836-5853.

Scopus Eid


  • 2-s2.0-85049572886

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1869

Number Of Pages


  • 17

Start Page


  • 5836

End Page


  • 5853

Volume


  • 56

Issue


  • 17

Place Of Publication


  • United Kingdom

Abstract


  • In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

UOW Authors


  •   Patne, Kalpit (external author)
  •   Shukla, Nagesh (external author)
  •   Kiridena, Senevi
  •   Tiwari, Manoj K. (external author)

Publication Date


  • 2018

Citation


  • Patne, K., Shukla, N., Kiridena, S. & Tiwari, M. (2018). Solving closed-loop supply chain problems using game theoretic particle swarm optimisation. International Journal of Production Research, 56 (17), 5836-5853.

Scopus Eid


  • 2-s2.0-85049572886

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1869

Number Of Pages


  • 17

Start Page


  • 5836

End Page


  • 5853

Volume


  • 56

Issue


  • 17

Place Of Publication


  • United Kingdom