An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming

• Published in: IIE transactions Vol. 49; no. 1; pp. 1 - 12
• Main Authors: Cardin, Michel-Alexandre, Xie, Qihui, Ng, Tsan Sheng, Wang, Shuming, Hu, Junfei
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2017; Taylor & Francis Ltd
• Subjects: Complex systems; Decision analysis; decision rule; Dynamic programming; Flexibility; Flexibility in engineering design; Hybrid systems; real options analysis; Stochastic programming; Systems design; Uncertainty; Urban environments; Waste to energy; waste-to-energy system
• ISSN: 2472-5854; 2472-5862
• DOI: 10.1080/0740817X.2016.1189627

This article introduces an approach to assess the value and manage flexibility in engineering systems design based on decision rules and stochastic programming. The approach differs from standard Real Options Analysis (ROA) that relies on dynamic programming in that it parameterizes the decision variables used to design and manage the flexible system in operations. Decision rules are based on heuristic-triggering mechanisms that are used by Decision Makers (DMs) to determine when it is appropriate to exercise the flexibility. They can be treated similarly as, and combined with, physical design variables, and optimal values can be determined using multistage stochastic programming techniques. The proposed approach is applied as demonstration to the analysis of a flexible hybrid waste-to-energy system with two independent flexibility strategies under two independent uncertainty drivers in an urban environment subject to growing waste generation. Results show that the proposed approach recognizes the value of flexibility to a similar extent as the standard ROA. The form of the solution provides intuitive guidelines to DMs for exercising the flexibility in operations. The demonstration shows that the method is suitable to analyze complex systems and problems when multiple uncertainty sources and different flexibility strategies are considered simultaneously.