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Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems. [ABSTRACT FROM AUTHOR]

Hyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the optimiser’. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The lowlevel heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic basedon sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space. [ABSTRACT FROM AUTHOR]

High school timetabling is one of those recurring NP-hard real-world combinatorial optimisation problems that has to be dealt with by many educational institutions periodically, and so has been of interest to practitioners and researchers. Solving a high school timetabling problem requires scheduling of resources and events into time slots subject to a set of constraints. Recently, an international competition, referred to as ITC 2011 was organised to determine the state-of-the-art approach for high school timetabling. The problem instances, obtained from eight different countries across the world used in this competition became a benchmark for further research in the field. Selection hyper-heuristics are general-purpose improvement methodologies that control/mix a given set of low level heuristics during the search process. In this study, we evaluate the performance of a range of selection hyper-heuristics combining different reusable components for high school timetabling. The empirical results show the success of the approach which embeds an adaptive great-deluge move acceptance method on the ITC 2011 benchmark instances. This selection hyper-heuristic ranks the second among the previously proposed approaches including the ones competed at ITC 2011. [ABSTRACT FROM AUTHOR]

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results. [ABSTRACT FROM AUTHOR]

The magnitude of the workload associated with the provision of emergency response services in the aftermath of natural disasters, coupled with limited availability of personnel for providing these services, leads to demand–supply imbalances with detrimental effects on the provision of the required services. In this context, personnel routing and scheduling decisions aim to meet the demand as fast as possible while at the same time they ensure fair provision of services among the impacted areas. Due to their excessive working hours, and their travel over unreliable transportation networks, personnel are prone to burnout effects and are exposed to risks derived from the unreliable condition of the disaster impacted transportation networks. To address these issues, we propose a novel Disaster Response Personnel Routing and Scheduling (DRPRS) model with efficiency, fairness and risk objectives, subject to working and resting related constraints. The proposed model can be applied to routing and scheduling decisions for different types of emergency response services, and takes into account the precedence relations among them. We solve the resulting multi-objective model lexicographically over a rolling horizon sequentially on a daily basis until the demand for all types of services considered is satisfied. We report results from the application of the proposed model for routing and scheduling personnel involved in the provision of evacuation and medical services in the context of 2018 Lombok Earthquake, Indonesia. • Experiments using data from 2018 Lombok Earthquake provide managerial insights. • A multi-objective model incorporating efficiency, fairness and risk is proposed. • Precedence relations between different emergency response operations are modelled. • A rolling horizon approach ensuring the fulfilment of the entire demand is proposed. [ABSTRACT FROM AUTHOR]