Decision Tree Modeling Using Integrated Multilevel Stochastic Networks

Moussa, Mohamed; Ruwanpura, Janaka Y.; Jergeas, George

doi:10.1061/(asce)0733-9364(2006)132:12(1254)

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…DTS assess a sequence of decisions and their consequences in an aim to determine the best course of action (Moussa et al 2006). A decision to build a new tide gauge system, a buoy system, a combined system or keep the existing provisions depends on the outcome of a comprehensive DTS analysis.…”

Section: Decision Trees and Model Scenariosmentioning

confidence: 99%

“…The DSSS-DTS module accepts loops/feedback from previous decisions, allows estimating probability of events based on a range estimate, computes the optimality index for each decision and provides the utility/risk profile for any decision and any combination of decisions (Moussa et al 2006). In particular, DSSS analyses the different options with expected costs/benefits under their probabilities of occurrences.…”

Section: Decision Trees and Model Scenariosmentioning

confidence: 99%

“…The two DTS are modelled on the decision tree module in the DSSS (Moussa et al 2006). The four alternatives, their respective chances (detection/non-detection) and the state of the evacuation can all be interpreted with their monetary and/or probability values.…”

Section: Model Construction In Dsssmentioning

confidence: 99%

“…In consequence, the simulation process ideally requires 1000 or more iterations. The DSSS Decision Tree module computes expected value (EV) from the 'backward path' calculation (Moussa et al 2006). All EVs of the considered alternatives can be obtained from the respective decision nodes.…”

Section: Model Construction In Dsssmentioning

confidence: 99%

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Decision analysis for a tsunami detection system – case study: Sri Lanka

Wickramaratne

Ruwanpura

Wirasinghe

2011

Civil Engineering and Environmental Systems

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The massive devastation of the Indian Ocean mega tsunami in 2004, clearly calls for an efficient early warning system for the Indian Ocean countries. The 'Regional Watch Provider' system currently planned makes some countries entirely dependent on the watch providers. We discuss a feasible and reliable tsunami detection mechanism for Sri Lanka that would give it some independent information for decision making purposes. It contemplates Sri Lanka deploying its own additional detection tools. A stand-alone tide gauge system, a buoy system and a combined gauge and buoy system are tested with the status quo or 'do nothing' alternative. A decision analysis exercise investigates the probability of tsunamis, probability of failures of each detection system, the probability of successful completion of an evacuation and the probability of false warnings which have their own costs. This paper concludes with the derived recommendations for a preferred tsunami detection system for Sri Lanka.

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Section: Decision Trees and Model Scenariosmentioning

confidence: 99%

Section: Decision Trees and Model Scenariosmentioning

confidence: 99%

Section: Model Construction In Dsssmentioning

confidence: 99%

Section: Model Construction In Dsssmentioning

confidence: 99%

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Decision analysis for a tsunami detection system – case study: Sri Lanka

Wickramaratne

Ruwanpura

Wirasinghe

2011

Civil Engineering and Environmental Systems

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“…These results then be integrated with the project schedules for continuous refinement and adjustment and finally ensure optimum solution for scheduling and resource planning. It is expected to consider several techniques to optimize the system -Decision theory (Taha, 1997), Dynamic Programming (Rao, 1984), Stochastic Decision Trees (Moussa et al, 2006), and Markov Process (Jacob, 2005) where applicable.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Optimization of Multi-Project Environment (OPMPE)

Hossain¹,

Ruwanpura²

2008

2008 Winter Simulation Conference

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Construction business is project oriented and that is why every construction organization is dependent on projects. Typically they undertake multiple projects with limited multiple resources and information. Most importantly they need to take continuous and quick decisions to keep it going. The reason behind this is lack of tools and structured approach that can efficiently deal with multi-project environment (PME). Resulting is problem of wrong project selection, project slippage and under/over utilization of scares resources. This paper presents a simulation model (OPMPE) for optimizing MPE. The model is capable of analyzing and predicting future problems, assessing the cumulative impact and generates valuable statistics and information for quick decision-making. It will work together with the available scheduling tools and will help strengthening the overall planning and execution system for MPE. The application and of the model is demonstrated using a collection of real project data for building construction.

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