Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems

Akhil Garg, Surinder Singh, Liang Gao*, Xu Meijuan, Chee Pin Tan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In this work a multi-objective optimisation framework of genetic programming (GP) in the modelling of bullwhip effect and NSA for centralised and decentralised supply chain systems has been proposed. The individual and interactive effect of these four input factors has been investigated on bullwhip effect and NSA by adapting the parametric and sensitivity approach on the formulated models. The appropriate settings of dominant input factors (batch ordering and demand signal processing for a decentralised chain, demand signal processing and rationing shortage gaming for a centralised chain) are suggested to optimise the bullwhip effect and NSA of three-stage supply chain simultaneously. The implications and advantages of proposed optimisation framework will be useful for business practitioners to monitor and supervise the sudden demand amplification that generally faced by them in the supply chains.

Original languageEnglish
Pages (from-to)241-251
Number of pages11
JournalInternational Journal of Bio-Inspired Computation
Volume16
Issue number4
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Bullwhip effect
  • Genetic programming
  • Modelling
  • Net stock amplification
  • NSA
  • Optimisation

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