Demand-dependent recovery decision-making of a batch of products for sustainability

Kai Meng, Ying Cao, Xianghui Peng, Victor Prybutok, Varun Gupta

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This research examines the demand-dependent product recovery decision-making problem for a batch of products that undergo remanufacturing/dismantling via a hybrid recovery system. Product recovery provides an attractive approach to achieving sustainable manufacturing. Faced with changing demand, flexible recovery decisions on product recovery strategies, component recovery options, and recovery yields are critical to enable a successful and sustainable operations. However, a need exists for an integrated model to provide a comprehensive optimal solution from the integrated perspective of economic, environmental, and societal sustainability. This research gap is addressed by proposing and validating a multi-objective decision-making model. Demand-dependent optimal solutions are identified that balance different sustainable performance constraints. Further, a two-stage meta-heuristic method is developed to solve the model, where a cooperative evolution mechanism is designed to optimize dismantling decisions and recovery yields simultaneously. Numerical experiments are conducted that illustrate and validate the developed solution. In addition, the impacts of demand on optimal decisions and different sustainable performance are investigated. The results verify the effectiveness of our new model and approach in assisting decision makers in finding a flexible path to sustainable recovery while providing insights into analyzing the counteraction effect of different demands in hybrid recovery implementation.

Original languageEnglish (US)
Article number107552
JournalInternational Journal of Production Economics
Volume224
DOIs
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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