Markov-chain Monte-Carlo sampling for optimal fidelity determination in dynamic decision-making

Sara Masoud, Bijoy Chowdhury, Young Jun Son, Russell Tronstad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Decision-making for dynamic systems is challenging due to the scale and dynamicity of such systems, and it is comprised of decisions at strategic, tactical, and operational levels. One of the most important aspects of decision-making is incorporating real-time information that reflects immediate status of the system. This type of decision-making, which may apply to any dynamic system, needs to comply with the system’s current capabilities and calls for a dynamic data driven planning framework. Performance of dynamic data driven planning frameworks relies on the decision-making process which in return is relevant to the quality of the available data. This means that the planning framework should be able to set the level of decision-making based on the current status of the system, which is learned through the continuous readings of sensory data. In this work, a Markov-chain Monte-Carlo (MCMC) sampling method is proposed to determine the optimal fidelity of decision-making in a dynamic data driven framework. To evaluate the performance of the proposed method, an experiment is conducted, where the impact of workers performance on the production capacity and the fidelity level of decision-making are studied.

Original languageEnglish (US)
Title of host publicationIISE Annual Conference and Expo 2019
PublisherInstitute of Industrial and Systems Engineers, IISE
ISBN (Electronic)9781713814092
StatePublished - 2019
Event2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019 - Orlando, United States
Duration: May 18 2019May 21 2019

Publication series

NameIISE Annual Conference and Expo 2019

Conference

Conference2019 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2019
Country/TerritoryUnited States
CityOrlando
Period5/18/195/21/19

Keywords

  • Decision Making
  • Dynamic Data Driven Systems
  • Fidelity
  • Markov-chain Monte-Carlo (MCMC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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