An Evolutionary Rule-Based Framework for the Design and Management of Engineering Systems With Flexibility

Embedding flexibility in engineering systems is a promising way to improve system performance under uncertainty. However, decision-makers are often unclear when and how to exercise flexibility after it has been embedded in a system design. This paper presents an evolutionary rule-based framework to address this challenge. The proposed framework extends the traditional flexibility management framework by automatically calibrating decision rules using an evolutionary algorithm. The calibrated decision rules can be used by decision-makers to adaptively adjust decisions in response to a changing environment. By incorporating a decision rule and evolutionary algorithm, the proposed framework not only helps generate flexible designs in the system design phase, but also provides intuitive guidance for decision-makers to manage flexibility in the system operation phase. To demonstrate its application, the proposed framework is applied to generate and optimize the decision rule of capacity expansion strategy for a waste-to-energy system. The results show that the flexible design that expands capacity based on the decision rule generated by our proposed framework outperforms the design with a fixed capacity expansion plan. These findings demonstrate that the proposed framework can effectively support flexibility management in terms of improving the system’s overall expected net present value.