Multi-objective robust early stage ship design optimisation under uncertainty

Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2019
Thesis identifier
  • T15451
Person Identifier (Local)
  • 201569923
Qualification Level
Qualification Name
Department, School or Faculty
  • Shipping industry has become very competitive, while a lot of research is carried out in the shipbuilding world to investigate possible ways to improve ship design and create efficient and economical ships. Technological improvements allow the detailed exploration of design space and assist the theory of optimisation in becoming a vital part of ship design. Advanced software tools are available to designers and researchers to expand their design optimisation methodologies and introduce not only more efficient techniques, but also more robust approaches to ship design.The topic of ship design optimisation has been investigated by numerous researchers, who have established structured methodologies which can be applied to real case studies and produce efficient solutions to the ship design problem. However, the definition of the ship design problem changes often due to the introduction of new ship types, international regulations and technological improvements.This thesis contributes to the aforementioned developments with regard to the ship design optimisation problem. The mission is to develop a methodology for a multi-objective robust early stage ship design optimisation under uncertainty. Several aspects of ship design are incorporated, taking into account the holistic ship design model. Various performance indicators are used as measures of merit to evaluate the response of possible solutions to the problem. New regulations are incorporated to the optimisation problem, investigating their impact on its solution. In addition, uncertainty quantification is applied throughout the proposed methodology, while its effect on the ship design optimisation problem is examined.
Advisor / supervisor
  • Boulougouris, Evangelos
  • Turan, Osman
Resource Type
Date Created
  • 2019
Former identifier
  • 9912783393202996