No abstract

For offshore wind to be competitive with mature energy industries, cost efficiencies must be improved throughout the lifetime of an offshore wind farm (OWF). With expensive equipment hire spanning several years, installation is an area where large savings can potentially be made. Installation operations are subject to uncertain weather conditions, with more extreme conditions as OWF developments tend towards larger sites, further offshore in deeper waters. One approach to reduce the cost of the installation process is to evaluate advanced technologies or operational practices. However, in order to demonstrate cost savings, the impact of these advances on the installation process must be quantified in the presence of uncertain environmental conditions. To addresses this challenge a simulation tool is developed to model the logistics of the installation process and to identify the vessels and operations most sensitive to weather delays. These operations are explored to identify the impact of technological or operational advances with respect to weather delays and the resulting installation duration under different levels of weather severity. The tool identifies that loading operations contribute significantly to the overall delay of the installation process, and that a non-linear relationship exists between vessel operational limits and the duration of installation

Finding low-cost designs of water distribution systems (WDSs) which satisfy appropriate levels of network performance within a manageable time is a complex problem of increasing importance. A novel multi-objective memetic algorithm (MA) is introduced as a solution method to this type of problem. The MA hybridises a robust genetic algorithm (GA) with a local improvement operator consisting of the classic Hooke and Jeeves direct search method and a cultural learning component. The performance of the MA and the GA on which it is based are compared in the solution of two benchmark WDS problems of increasing size and difficulty. Solutions that are superior to those reported previously in the literature were achieved. The MA is shown to outperform the GA in each case, indicating that this may be a useful tool in the solution of real-world WDS problems. The potential benefits from search space reduction are also demonstrated.

With a typical investment of in excess of £100 million for each project, the installation phase of offshore wind farms is an area where substantial cost-reductions can be achieved; however, to-date there have been relatively few studies exploring this. In this paper, we develop a mixed-method framework which exploits the complementary strengths of two decision-support methods: discrete-event simulation and robust optimisation. The simulation component allows developers to estimate the impact of user-defined asset selections on the likely cost and duration of the full or partial completion of the installation process. The optimisation component provides developers with an installation schedule that is robust to changes in operation durations due to weather uncertainties. The combined framework provides a decision-support tool which enhances the individual capability of both models by feedback channels between the two, and provides a mechanism to address current OWF installation projects. The combined tool, verified and validated by external experts, was applied to an installation case study to illustrate the application of the combined approach. An installation schedule was identified which accounted for seasonal uncertainties and optimised the ordering of activities

Many countries seek to secure efficiency in health spending through establishing explicit priority setting institutions (PSIs). Since such institutions divert resources from frontline services which benefit patients directly, it is legitimate and reasonable to ask whether they are worth the money. We address this question by comparing, through simulation, the health benefits and costs from implementing two alternative funding approaches – one scenario in which an active PSI enables cost-effectiveness-threshold based funding decisions, and a counterfactual scenario where there is no PSI. We present indicative results for one dataset from the United Kingdom (published in 2015) and one from Malawi (published in 2018), which show that the threshold rule reliably resulted in decreased health system costs, improved health benefits, or both. Our model is implemented in Microsoft Excel and designed to be user-friendly, and both the model and a user guide are made publicly available, in order to enable others to parameterise the model based on the local setting. Although inevitably stylised, we believe that our modelling and results offer a valid perspective on the added value of explicit PSIs.