Generating decision rules for flexible capacity expansion to achieve better lifecycle performance

A flexible capacity expansion strategy is important for engineering systems since it enables decision-makers to adjust the capacity of systems as uncertainty unfolds. In a flexible capacity expansion problem, how to generate the optimal decision rule to determine when and how to expand the capacity is a challenging task. In this paper, a parameter optimization framework is proposed to automatically generate decision rules for flexible capacity expansion. The proposed framework provides a systematic way to calibrate decision rules through parameter tuning, which is based on the differential evolution (DE) algorithm. This study is illustrated using a waste-to-energy (WTE) system operated in Singapore. Results show that the flexible design with a DE-based decision rule can achieve the better economic performance, compared with the fixed design and the flexible design with an experience-based decision rule. The proposed framework helps decision-makers analyze and manage flexibility, so as to improve system performance.