A flexible system design approach for multi-facility capacity expansion problems with risk aversion. (22nd November 2022)

• Record Type: Journal Article
• Authors: Zhao, Sixiang; Haskell, William B.; Cardin, Michel-Alexandre
• Is Part Of: IISE transactions. Volume 55:Number 2(2023)
• Page Start: 187
• Page End: 200
• Publication Date: 2022-11-22
• Subjects: Capacity expansion problem -- system design -- real options -- risk aversion -- multi-stage stochastic programming -- decision rules; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2021.2022815
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: This article studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the Conditional Value-at-Risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if–then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.