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Supply Chain Management under Uncertainty

Credit to Download: 1 | Page Numbers 8 | Abstract Views: 152
Year: 2017
COI code: IIEC14_062
Paper Language: English

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Authors Supply Chain Management under Uncertainty

  Abdollah Arasteh - Babol Noshirvani University of Technology, Babol, Iran


The concentration of this paper is to measure and break supply chain planning decisions under market and/or technical uncertainty. The main approach is considering suppliers and distributor selection and determining the affected customers in the system problems simultaneously under uncertain conditions. The objects of the proposed model are maximizing the quality of products and income and minimizing the total cost and the shipping time from distributors to customers. Also, a two-organize, stochastic programming methodology is proposed for joining request vulnerability in multisite midterm store network arranging issues. The assessment of the normal second stage expenses is accomplished by expository reconciliation yielding an equivalent convex mixed-integer nonlinear problem (MINLP). At long last, a real options-based valuation (ROV) system for supporting under vulnerability is produced. Multistage stochastic writing computer programs are utilized to fuse vulnerability and a quantitative correlation of the ROV approach and the customary net-present-value (NPV) approach is given


supply chains; uncertainty; stochastic programming; fuzzy; real options

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COI code: IIEC14_062

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Arasteh, Abdollah, 2017, Supply Chain Management under Uncertainty, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Arasteh, Abdollah, 2017)
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The University/Research Center Information:
Type: state university
Paper No.: 5490
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

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