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Supply Chain Network Design in Uncertain Environment: A Review and Classification of Related Models

Supply Chain Network Design in Uncertain Environment: A Review and Classification of Related Models

Gholam Reza Nasiri, Fariborz Jolai
Copyright: © 2018 |Pages: 21
ISBN13: 9781522550914|ISBN10: 1522550917|EISBN13: 9781522550921
DOI: 10.4018/978-1-5225-5091-4.ch010
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MLA

Nasiri, Gholam Reza, and Fariborz Jolai. "Supply Chain Network Design in Uncertain Environment: A Review and Classification of Related Models." Optimization Techniques for Problem Solving in Uncertainty, edited by Surafel Luleseged Tilahun and Jean Medard T. Ngnotchouye, IGI Global, 2018, pp. 262-282. https://doi.org/10.4018/978-1-5225-5091-4.ch010

APA

Nasiri, G. R. & Jolai, F. (2018). Supply Chain Network Design in Uncertain Environment: A Review and Classification of Related Models. In S. Tilahun & J. Ngnotchouye (Eds.), Optimization Techniques for Problem Solving in Uncertainty (pp. 262-282). IGI Global. https://doi.org/10.4018/978-1-5225-5091-4.ch010

Chicago

Nasiri, Gholam Reza, and Fariborz Jolai. "Supply Chain Network Design in Uncertain Environment: A Review and Classification of Related Models." In Optimization Techniques for Problem Solving in Uncertainty, edited by Surafel Luleseged Tilahun and Jean Medard T. Ngnotchouye, 262-282. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5091-4.ch010

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Abstract

Dynamic environment imposes such conditions that make it necessary for companies to consider sources of uncertainty in designing core business processes and optimizing supply chain operations. Efficient management of a supply system requires an integrated approach towards various operational functions and related source of uncertainties. Uncertain conditions in supply network design problem such as market demands, delivery time, and facility capacity are considered and incorporated by many studies at the mathematical programming formulations as well. In this chapter, extensive review of existing SCND literature, brief overview and classification on uncertainty sources, useful strategies to deal uncertainties, model formulation with uncertain/stochastic parameters, efficient developed solution methodologies, and improvement adjustment mechanisms are discussed. Lastly, some directions for further research in this area are suggested.

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