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Sequential Decision-making in Interdependent Sectors with Multiobjective Inoperability Decision Trees: Application to Biofuel Subsidy Analysis

Author

Listed:
  • Joost Santos
  • Kash Barker
  • Paul Zelinke

Abstract

Decision-making involving large-scale systems often involves considerations for temporal changes, interdependencies in organizational structures, multiple competing objectives, and risk and uncertainty, among others. In this paper we develop a risk-based methodology, the Multiobjective Inoperability Decision Tree (MOIDT). It integrates several dimensions of decision-making processes associated with interconnected systems in terms of: (i) evaluation of sequential policies; (ii) analysis of interdependencies; (iii) treatment of multiple objectives and their tradeoffs; and (iv) characterization of uncertainties. To demonstrate the integration of these four components, we present a case study to analyze the impact of government policies towards mass-scale biofuel production. Using a multi-period decision framework, the analysis utilizes economic input-output data to model the probabilistic demand adjustments for sectors that will likely be affected by biofuel policies.

Suggested Citation

  • Joost Santos & Kash Barker & Paul Zelinke, 2008. "Sequential Decision-making in Interdependent Sectors with Multiobjective Inoperability Decision Trees: Application to Biofuel Subsidy Analysis," Economic Systems Research, Taylor & Francis Journals, vol. 20(1), pages 29-56.
    • Handle: RePEc:taf:ecsysr:v:20:y:2008:i:1:p:29-56
    DOI: 10.1080/09535310801890672
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    Citations

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    Cited by:

    1. Kash Barker & Kaycee J. Wilson, 2012. "Decision Trees with Single and Multiple Interval-Valued Objectives," Decision Analysis, INFORMS, vol. 9(4), pages 348-358, December.
    2. Kasivisvanathan, Harresh & Barilea, Ivan Dale U. & Ng, Denny K.S. & Tan, Raymond R., 2013. "Optimal operational adjustment in multi-functional energy systems in response to process inoperability," Applied Energy, Elsevier, vol. 102(C), pages 492-500.
    3. Elizabeth B. Connelly & Lisa M. Colosi & Andres F. Clarens & James H. Lambert, 2015. "Risk Analysis of Biofuels Industry for Aviation with Scenario‐Based Expert Elicitation," Systems Engineering, John Wiley & Sons, vol. 18(2), pages 178-191, March.
    4. Barker, Kash & Haimes, Yacov Y., 2009. "Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 819-829.
    5. Pratiwi, Astu Sam & Trutnevyte, Evelina, 2022. "Decision paths to reduce costs and increase economic impact of geothermal district heating in Geneva, Switzerland," Applied Energy, Elsevier, vol. 322(C).
    6. Pant, Raghav & Barker, Kash & Zobel, Christopher W., 2014. "Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 92-102.
    7. Barker, Kash & Santos, Joost R., 2010. "Measuring the efficacy of inventory with a dynamic input-output model," International Journal of Production Economics, Elsevier, vol. 126(1), pages 130-143, July.
    8. Zachary Walchuk & Kash Barker, 2013. "Analyzing interdependent impacts of resource sustainability," Environment Systems and Decisions, Springer, vol. 33(3), pages 391-403, September.
    9. Suzuki, Kengo & Uchiyama, Yohji, 2010. "Quantifying the risk of an increase in the prices of non-energy products by combining the portfolio and input-output approaches," Energy Policy, Elsevier, vol. 38(10), pages 5867-5877, October.
    10. Frini, Anissa & Guitouni, Adel & Martel, Jean-Marc, 2012. "A general decomposition approach for multi-criteria decision trees," European Journal of Operational Research, Elsevier, vol. 220(2), pages 452-460.
    11. Aristotle T. Ubando & Isidro Antonio V. Marfori & Kathleen B. Aviso & Raymond R. Tan, 2019. "Optimal Operational Adjustment of a Community-Based Off-Grid Polygeneration Plant using a Fuzzy Mixed Integer Linear Programming Model," Energies, MDPI, vol. 12(4), pages 1-17, February.
    12. Erik Dietzenbacher & Ronald E. Miller, 2015. "Reflections On The Inoperability Input--Output Model," Economic Systems Research, Taylor & Francis Journals, vol. 27(4), pages 478-486, December.
    13. Shital A. Thekdi & Joost R. Santos, 2016. "Supply Chain Vulnerability Analysis Using Scenario‐Based Input‐Output Modeling: Application to Port Operations," Risk Analysis, John Wiley & Sons, vol. 36(5), pages 1025-1039, May.
    14. Tan, Raymond R., 2011. "A general source-sink model with inoperability constraints for robust energy sector planning," Applied Energy, Elsevier, vol. 88(11), pages 3759-3764.

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