Considering multiple recovery options and uncertainties in a closed-loop supply chain network design problem

Rim Jerbia, Mohamed Amine Sehli, Zied Jemai, Mouna Kchaou Boujelben

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a closed-loop supply chain network design problem with multiple recovery options is studied. The problem is modeled as a Mixed Integer Linear Program (MILP). Then, a sensitivity analysis is carried out in order to investigate the impact of variations of the customer return rates, revenues, costs as well as the proportions of returns assigned to each recovery option, on the network structure and profit. A stochastic version of the model is also developed to account for the high uncertainties faced by companies. The computational results show that the solution of the stochastic problem is stable and that the benefit from using stochastic modeling increases when the penalty over non collected returns increases.

Original languageEnglish
Title of host publication2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages533-538
Number of pages6
ISBN (Electronic)9781509064656
DOIs
Publication statusPublished - Nov 8 2017
Event4th International Conference on Control, Decision and Information Technologies, CoDIT 2017 - Barcelona, Spain
Duration: Apr 5 2017Apr 7 2017

Publication series

Name2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
Volume2017-January

Other

Other4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
Country/TerritorySpain
CityBarcelona
Period4/5/174/7/17

Keywords

  • Closed loop supply chain network design
  • Facility location
  • MILP
  • Reverse logistics
  • Two-stage stochastic program

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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