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A multi-stage stochastic mixed-integer linear programming to design an integrated production-distribution network under stochastic demands

Derakhshi, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.7232/iems.2018.17.3.417
  3. Publisher: Korean Institute of Industrial Engineers , 2018
  4. Abstract:
  5. Supply chain management has gained much interest from researchers and practitioners in recent years. Proposing practical models that efficiently address different aspects of the supply chain is a difficult challenge. This research investigates an integrated production-distribution supply chain problem. The developed model incorporates parties with a specified number of processes to obtain raw materials from the suppliers in order to convert them to semi and final products. These products are then distributed through warehouses to end-distributors having uncertain demands. This uncertainty is captured as a dynamic stochastic data process during the planning horizon and is modeled into a multi-stage stochastic mixed integer linear program using a scenario tree approach. For large-size instances, a hybrid exact-approximate algorithm is proposed, where its effectiveness is assessed via several numerical cases. Furthermore, the model is generalized to its bi-objective version by considering the accessibility of the products based on the safety stock policy of the companies involved. In the end, an existing algorithm is combined with the ε-constraint method to obtain an approximate Pareto front. © 2018 KIIE
  6. Keywords:
  7. Bi-objective optimization ; Pareto front ; Production-distribution ; Scenario reduction ; Stochastic programming ; Supply chain
  8. Source: Industrial Engineering and Management Systems ; Volume 17, Issue 3 , 2018 , Pages 417-433 ; 15987248 (ISSN)
  9. URL: http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07539619