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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]

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper‐heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real‐world size revealed the success of the hyper‐heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper‐heuristic framework. [ABSTRACT FROM AUTHOR]