Modular supply chain optimization considering demand uncertainty to manage risk

Bhosekar, Atharv; Badejo, Oluwadare; Ierapetritou, Marianthi

doi:10.1002/aic.17367

Title: Modular supply chain optimization considering demand uncertainty to manage risk

Journal Article · 15 July 2021 · AIChE Journal

DOI: https://doi.org/10.1002/aic.17367 · OSTI ID:1976255

^[1]; Badejo, Oluwadare ^[2]; Ierapetritou, Marianthi ^[2]

GAMS Development Corp, Fairfax VA (United States); OSTI
University of Delaware, Newark, DE (United States)

Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. We report the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

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Research Organization:: University of Delaware, Newark, DE (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007888

OSTI ID:: 1976255

Journal Information:: AIChE Journal, Journal Name: AIChE Journal Journal Issue: 11 Vol. 67; ISSN 0001-1541

Publisher:: American Institute of Chemical EngineersCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Modular supply chain optimization considering demand uncertainty to manage risk

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