Mitigating Investment Risk Using Modular Technologies

TL;DR

This paper develops a multi-stage stochastic programming model to optimize capacity expansion with modular processing technologies, effectively balancing profit and risk under demand uncertainty.

Contribution

It introduces a cumulative risk measure approach for investment planning that improves risk mitigation and aligns with standard investment metrics.

Findings

Flexible modular systems outperform inflexible large-unit systems on the Pareto frontier.

The approach effectively balances profit and risk in capacity expansion decisions.

Case studies demonstrate the superiority of flexible deployment despite economies of scale.

Abstract

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