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Designing a Closed-loop Supply Chain under Uncertainty Using Sample Average Approximation (SAA) Method

Akbari, Sina | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49374 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akhavan Niaki, Taghi
  7. Abstract:
  8. These days, most companies are creating Closed-loop Supply Chains or they are adding Reverse Supply Chain to their existing Forward Supply Chain. Reducing raw material consumption, profit, customer satisfaction and environmental laws are most important reasons of this phenomenon. In the Closed-loop Supply Chains, companies collect the end of life and the end of use products and then if the quality of the returned product is good enough, that product with be refurbished and will be sold again and if the quality of returned products is not good enough to be refurbished, companies will use its good parts in manufacturing new products and the rest of the parts would be sent to disposal centers. Costs, demand, amount and quality of returned products and disposal rate are most important parameters in the designing network of a closed-loop supply chain and because these parameters have uncertain nature, for a better design, this uncertainty should be considered in the model. In these thesis, a new model for designing a new Closed-loop Supply Chains has been proposed for which a Stochastic Programming framework has been used for considering uncertainty in the model’s parameters. Because of the difficulty of evaluating the expected value term in the objective function and also, for determining obtained solution’s quality, the Sample Average Approximation (SAA) method has been used for solving the proposed model. Since, in the SAA method, a lot of mixed integer problems needs to be solved and because of the inefficiency of commercial software in solving this type of problems, L-Shaped decomposition method has been used. Obtained results confirm the efficiency of the SAA problem for solving stochastic programming problems and L-Shaped decomposition method for solving mixed integer problem
  9. Keywords:
  10. Stochastic Programming ; Reverse Logistics ; Uncertainty ; Closed-Loop Supply Chain ; Sample Average Approxination Method ; L-Shaped Decomposition Method

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