Why do engineers often design what people don't want? And why do people often want solutions that are not feasible? This is because the current design and decision support optimisation methodologies are one-sided and ignore or fail to capture the dynamic interaction between people's preferences (desirability) and technical assets' performance (feasibility). Furthermore, most methodologies contain fundamental problems and often cannot achieve a single best design solution. Moreover, the offshore & dredging engineering (ODE) industry can significantly benefit from multi-objective design/decision optimisation as projects become larger, more stakeholders are involved in concurrent design/decision-making, and new technologies emerge.

To solve the shortcomings given the current momentum within the ODE industry, a truly integrative Open Systems Design (Odesys) methodology is proposed in this thesis. For this purpose, a new multi-objective optimisation method IMAP is introduced which is operationalised in a Python-based software tool called Preferendus, including a new inter-generational Genetic Algorithm (GA) solver.

The application and added value of the Preferendus/IMAP are validated for two demonstrators within the maritime contractor Boskalis.

The first validation case concerns planning and design optimisation in the development of offshore floating wind farms, focusing on scheduling and mooring design. For this purpose, an integration was made with the wind turbine simulation tool OpenFAST. The new Preferendus/IMAP significantly improved the overall tender performance by: 1) providing initial design approaches within a few hours, where currently tender teams spend days working on design alternatives that, in hindsight, were suboptimal; 2) removing tender team bias from the design process and finding design solutions that were otherwise unfairly disregarded; 3) open glass-box modelling support for concurrent design between the asset owner and the contractor.

The second validation case is a decision support optimisation application of a dredging production in which multiple vessels jointly execute a dredging project. Due to different types of disturbances, these vessels often have to wait for each other, reducing the overall efficiency of the project. Current expert-based optimisation approaches are limited in their ability to adjust best-for-project. The new Preferendus/IMAP shows a clear improvement and finds solutions that significantly reduce waiting time while simultaneously achieving high production levels. Moreover, the Preferendus/IMAP outperforms single-sided optimisation on production alone by achieving similar high production levels while also improving other objectives such as CO2 emissions and vessel efficiency.

Steps for further development include: 1) improving OpenFAST integration and modelling; 2) addition of fatigue loading in the anchor design; 3) improving discrete event simulation scheduling modelling; 4) improving runtime by exploring other algorithms and/or programming improvements.