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An options-based approach to coordinating distributed decision systems

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  • Ball, Daniel R.
  • Deshmukh, Abhijit
  • Kapadia, Nikunj

Abstract

Engineering and operations management decisions have become increasingly complex as a result of recent advances in information technology. The increased ability to access and communicate information has resulted in expanded system domains consisting of multiple agents, each exhibiting autonomous decision-making capabilities, with potentially complex logistics. Challenges regarding the management of these systems include heterogenous utility drivers and risk preferences among the agents, and various sources of system uncertainty. This paper presents a distributed options-based model that manages the impact of multiple forms of uncertainty from a multi-agent perspective, while adapting as both the stream of information and the capabilities of the agents are better known. Because the actions of decision makers may have an impact on the evolution of underlying sources of uncertainty, this endogenous relationship is modeled and a solution approach developed that converges to an equilibrium system state and improves the performance of agents and the system. The final result is a distributed options-based decision-making policy that both responds to and controls the evolution of uncertainty in large-scale engineering and operations management domains.

Suggested Citation

  • Ball, Daniel R. & Deshmukh, Abhijit & Kapadia, Nikunj, 2015. "An options-based approach to coordinating distributed decision systems," European Journal of Operational Research, Elsevier, vol. 240(3), pages 706-717.
    • Handle: RePEc:eee:ejores:v:240:y:2015:i:3:p:706-717
    DOI: 10.1016/j.ejor.2014.05.037
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    References listed on IDEAS

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    2. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    3. Wu, Desheng & Olson, David L. & Wang, Shouyang, 2019. "Finance-operations interface mechanism and models," Omega, Elsevier, vol. 88(C), pages 1-3.
    4. Chuan-Chuan Ko & Tyrone T. Lin & Fu-Min Zeng & Chien-Yu Liu, 2018. "Optimum Technology Product Life Cycle Technology Innovation Investment-Using Compound Binomial Options," Risks, MDPI, vol. 6(3), pages 1-14, September.

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