Mitigating investment risk using modular technologies

We study logistical investment flexibility provided by modular processing technologies for mitigating risk. Specifically, we propose a multi-stage stochastic programming formulation that determines optimal capacity expansion plans mitigate demand uncertainty. The accounts multi-product dependencies between small/large units and trade-offs expected profit uses cumulative risk measure to avoid time-consistency issues of traditional, per-stage risk-minimization formulations, argue this approach is more compatible with typical metrics such as the net present value. Case studies different complexity are presented illustrate developments. Our reveal Pareto frontier flexible setting (allowing deployment small units) dominates an inflexible only large units). Notably, dominance prevalent despite benefits arising from economies scale units.