Decision Support for Conceptual Ship Design with Focus on a Changing Life Cycle and Future Uncertainty

Abstract

To better prepare for a changing world, both regarding expected events and unforeseen incidents, flexible design solutions should be considered when projects with a long lifetime and significant investment costs are planned. Accordingly, the main focus of this PhD project is the evaluation of reconfiguration flexibility for important ship design problems. As the potential value of flexible design features can only be seen if future uncertainty is explicitly considered in the evaluation process, stochastic decision support methods are suggested as the methodical approach. The increased focus on harmful emissions and emission regulations will constraint many ship design problems significantly, especially related to the design of machinery systems. An important question becomes that of minimum cost compliance, i.e. how to comply with current and future emission regulations at a minimum cost. Furthermore, emission regulation compliance typically involves considering both an initial investment and an operational cost. As the operational cost depends on factors that change with time, it should be acknowledged that this cost to some extent will be uncertain. Again, this suggests that models able to consider future uncertainty are used for such decision problems. In light of these observations, four research questions were formed for this PhD project: How can future uncertainty and reconfiguration flexibility be included in the ship design process using stochastic optimization methods? How can aspects related to emission regulation compliance for ships (which are strongly affected by future uncertainty) be included in the design process? What uncertainties are most important when making design decisions related to energy efficiency and emission regulation compliance for ships? What is the value of accounting for future uncertainty and reconfiguration flexibility in the design process? In this PhD project, stochastic decision support models have been proposed for various machinery design problems, where also emission regulation compliance has been included. For the considered problems where emission regulations compliance was included in the problem, future fuel prices were identified as the most critical in terms of future uncertainty. For offshore vessels with diesel electric machinery, development in engine efficiencies was identified as the most important uncertainty. Furthermore, results show that by using stochastic optimization models, one obtain valuable information about whether to facilitate for future reconfigurations, which in the end can result in a design better prepared for expected events and unforeseen incidents. It was also found that lifecycle costs can be significantly reduced by including aspects related to emission regulations when making decisions, in particular for machinery design problems and fleet renewal problems.