An optimization problem in a closed-loop manufacturing system with stochastic variability

Cong Zheng, Kimitoshi Sato*, Kenichi Nakashima

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


This paper develops a decision-making model in a Closed-Loop Supply Chain (CLSC) consisting of two-recyclers and one remanufacturer. The remanufacturer purchases the repaired parts from the recyclers and produces the products to sell them on the market. The sold products are collected and recovered by the recyclers at the end of product life cycle. Each recycler has the different lead time and costs, as well as supply risk. The market demand and parts supply from recyclers are assumed to be random. We model the CLSC as a Markov decision problem to obtain the optimal production and procurement policy. that minimizes the expected average cost period. We discuss the properties of the optimal policy and show the sensitivity analysis under various scenarios. Moreover, the computation results give us that the minimum expected average cost decreases as the reliability and/or the recovery rates of the CLSC increase. Finally, we provide the decision rules for selecting the recyclers.

Original languageEnglish
Pages (from-to)1607-1615
Number of pages9
JournalProcedia Manufacturing
Publication statusPublished - 2019
Event25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, ICPR 2019 - Chicago, United States
Duration: 2019 Aug 92019 Aug 14


  • Closed-loop supply chain
  • Markov decision process
  • Random yield
  • Remanufacturing
  • Supply Risk

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


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