Designing a cased based reasoning decision support system for ice management operations using expert knowledge

Yazdanpanah, Fatemeh (2021) Designing a cased based reasoning decision support system for ice management operations using expert knowledge. Masters thesis, Memorial University of Newfoundland.

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Prevention of safety hazards plays an important role in the offshore and maritime industries, especially in offshore ice management operations as the safety of these operations depends on the judgment and decision making of experienced captains and their bridge teams. To address safety challenges that may arise in the context of ice management operations, this study focused on a human-centered approach to develop an early-stage decision support system (DSS) for offshore ice management operations by applying a case-based reasoning (CBR) method. The aim of this research is to (i) capture knowledge from expert seafarers to be used in the development of a DSS; and (ii) propose a DSS employing a CBR model to be used onboard ships in a real-time basis for ice management operations. To capture seafarers’ experience, this study employed semi-structured interviews and bridge simulator exercises. The results of the knowledge capture exercises were translated into an ice management DSS using a CBR model. The case-based reasoning (CBR) model develops solutions to new problems by using similar problems in the past. The DSS employs a decision tree algorithm to retrieve a case to match observations from the current situation with an unknown outcome to a case base with known outcomes. This thesis describes the methods used in the development of the onboard DSS to provide tactical guidance for ice management operations. It also outlines the methods used to test the DSS software’s suggested ice management strategies and adjustments during a series of simulator exercises.

Item Type: Thesis (Masters)
Item ID: 15161
Additional Information: Includes bibliographical references (pages 139-145).
Keywords: knowledge capture, decision support system, case-based reasoning
Department(s): Engineering and Applied Science, Faculty of
Date: October 2021
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Ice navigation--Safety measures; Support services (Management)--Methodology.

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