Print Email Facebook Twitter A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power Title A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power Author Kana, A.A. (TU Delft Ship Design, Production and Operations) Harrison, B.M. (University of Michigan) Date 2017 Abstract A Monte Carlo approach to the ship-centric Markov decision process (SC-MDP) is presented for analyzing whether a container ship should convert to LNG power in the face of evolving Emission Control Area regulations. The SC-MDP model was originally developed as a means to analyze uncertain, sequential decision making problems. However, the original model is limited in its handling of uncertainty by only using discrete probabilistic values to account for the uncertainty. This paper extends the model to include Monte Carlo simulations to gain a deeper understanding of how uncertainty affects decision making behavior. A case study is presented involving the impact of evolving Emission Control Areas on the design and operation of a notional 13,000 TEU container ship. The decision of whether to invest in a dual fuel LNG engine is analyzed given uncertainties in economic parameters, regulatory scenarios, and supply chain risks. The case study is used to show how variations in uncertain parameters can have a drastic effect on optimal decision strategies. Subject Decision makingEmission Control AreaLNGMarkov decision processMonte Carlo simulationUncertainty analysis To reference this document use: http://resolver.tudelft.nl/uuid:07c2bbfb-da4b-4311-b4eb-787e6e0bd937 DOI https://doi.org/10.1016/j.oceaneng.2016.11.042 Embargo date 2019-01-15 ISSN 0029-8018 Source Ocean Engineering, 130, 40-48 Part of collection Institutional Repository Document type journal article Rights © 2017 A.A. Kana, B.M. Harrison Files PDF Kana_Harrison_OE_Accepted ... _FINAL.pdf 487.2 KB Close viewer /islandora/object/uuid:07c2bbfb-da4b-4311-b4eb-787e6e0bd937/datastream/OBJ/view