Ship design evaluation subject to carbon emission policymaking using a Markov decision process framework

Niese, Nathan D.; Kana, Austin A.

doi:10.1016/j.oceaneng.2015.06.042

Cited by 16 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• The sum of all probabilities on the same raw P j P A ij is between 0 and 1, or 0 < P j P A ij < 1, in such case, P A ij is assigned a new value ðP A ij Þ n calculated which is its normalized value using the sum of all probabilities on the same raw, as shown in equation (15).…”

Section: Actionmentioning

confidence: 99%

“…There have been several attempts to implement RL represented in the Markov decision process (MDP) to support design efforts in several engineering disciplines, such as in ship design. 15–17 Markov decision process was also utilized to support design efforts in the AEC industry, such as the design of trusses, 18 building structural frames, 19 and windows. 20 However, RL remains underutilized in the AEC industry 21 and with a scope limited to one discipline.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process

BuHamdan

Alwisy

Danel

et al. 2022

International Journal of Architectural Computing

View full text Add to dashboard Cite

While the design practice in the architecture, engineering, and construction (AEC) industry continues to be a creative activity, approaching the design problem from a perspective of the decision-making science has remarkable potentials that manifest in the delivery of high-performing sustainable structures. These possible gains can be attributed to the myriad of decision-making tools and technologies that can be implemented to assist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom. Such combination comes to extreme importance amid the mounting pressure on the AEC industry players to deliver economic, environmentally friendly, and socially considerate structures. Despite the promising potentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary design endeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing and applying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expedite identifying viable design alternatives within the solutions space in multidisciplinary design while maximizing the likelihood of finding the optimal design.

show abstract

Section: Actionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process

BuHamdan

Alwisy

Danel

et al. 2022

International Journal of Architectural Computing

View full text Add to dashboard Cite

show abstract

“…The simulation begins with an ECA coverage of 1100 nm and a single fuel engine installed that burns MDO and HFO. Selecting the correct starting state is important because the resulting decision paths may be sensitive to these initial conditions (Kana et al, 2016a;Niese et al, 2015). Since this case study examines decisions regarding retrofitting an existing vessel, the starting state is designed to reflect the current design of the vessel as well as current regulatory environment.…”

Section: Starting Statementioning

confidence: 99%

“…The SC-MDP model has been beneficial in analyzing decision making in the maritime domain due to its ability to handle time-varying uncertainty in both the endogenous and exogenous factors. The model has previously been used to examine decision making scenarios in the maritime domain involving ballast water treatment methods (Kana et al, 2016a;Niese & Singer, 2013), various design options to meet the Energy Efficiency Design Index (Niese et al, 2015), and studying personnel movement during emergency situations on board the vessel (Kana & Singer, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power

Kana

Harrison

2017

Ocean Engineering

Self Cite

View full text Add to dashboard Cite

“…The benefits of the model include a state-based representation of the system attributes and uncertain external environment, the capability to model various decision scenarios, and the ability to model uncertain temporal changes. 13 Kana and Singer 5 introduced how to use this model to simulate the uncertain decisions individuals may make while searching for an exit during times of emergency.…”

Section: Introductionmentioning

confidence: 99%

An early-stage design model for estimating ship evacuation patterns using the ship-centric Markov decision process

Kana

Droste

2017

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

An early-stage design model is presented that estimates personnel locations on board a vessel during times of evacuation. This model takes into account various levels of uncertainty and pain that individuals may feel while heading toward safety, while simultaneously not requiring highly detailed information regarding the vessel layout. This makes this model suitable for analysis during early stages of design. To do this, principal eigenvector analysis is applied to the ship-centric Markov decision process model. Principal eigenvector analysis provides a leading indicator metric for forecasting and quantifying locations of individuals when coupled with the ship-centric Markov decision process model. For evacuation models suited for later stages of design, full temporal simulations may be required to understand long-term implications of personnel movement. This article proposes an alternative method that is able to identify some of these implications while not requiring full details of the vessel layout nor temporal simulations. To do this, a common theorem in Markov theory is applied that defines how the principal eigenvector represents the long-term steady-state behavior of the system. Metrics are defined that quantify the probability that an individual will congregate at specific locations on the vessels and highlight sensitivities to long-term behavior. A case study of a simplified vessel layout is presented that examines decision-making regarding ship egress analysis and general arrangements design. The results highlight specific areas of interest that cause significant changes to where individuals congregate and the probability they arrive safely at the exit. Sensitivity studies are performed varying the uncertainty in the movement of the individuals, how much pain they are experiencing, and one example where a passageway is blocked.

show abstract

Ship design evaluation subject to carbon emission policymaking using a Markov decision process framework

Cited by 16 publications

References 19 publications

The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process

The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process

A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power

An early-stage design model for estimating ship evacuation patterns using the ship-centric Markov decision process

Contact Info

Product

Resources

About