Flexible capacity planning for engineering systems based on decision rules and differential evolution

Abstract

Abstract This paper proposes a novel approach to managing capacity planning flexibility in engineering systems based on decision rules and differential evolution (DE) algorithm. A multi-stage capacity expansion problem is formalized based on a decision rule representation. The formalized model can analyze multiple sources of uncertainty simultaneously (e.g., changes in demand and material price) and explicitly consider design principles like economies of scale and learning effect. An evolutionary decision rule optimization framework based on the DE algorithm is proposed to automatically optimize the decision rule for capacity planning. Compared to traditional capacity planning models (e.g., stochastic programming), this work is able to offer a flexible capacity plan under uncertainty assumptions at the system design stage. The output of our approach is a decision rule that provides thresholds/conditions for decision-makers to adaptively adjust decisions at the system deployment stage. In the application of capacity planning for a waste-to-energy system, experimental results show that the decision rules generated by our approach offer a 20% improvement in terms of expected net present value, compared to a fixed capacity plan generated by stochastic programming.