A flexible system design approach for multi-facility capacity expansion problems with risk aversion

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