Water and sewerage companies (WaSC) in the UK are under increasing pressures to improve customer satisfaction. The biggest cause for customer dissatisfaction in the wastewater sector is a service failure caused by a blockage. There is therefore a need to understand the factors which influence blockage processes in order to prevent them. This work demonstrates how preceding rainfall impacts the sewer system of two highly populated regions within South Wales that have differing gradients. The total rainfall, number of dry days and consecutive number of dry days prior to a blockage were investigated using statistical analysis in order to determine the impact that rainfall has on blockages. The results obtained demonstrate the importance that dry weather has on blockage rates in both steep and flat catchments. Future work will incorporate predicted rainfall impact into a proactive maintenance scheduling model.

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Effective functioning of sewer systems is critical for the everyday life of people in the urban environment. This is achieved, among other things, by the means of regular, planned maintenance of these systems. A large water utility would normally have several maintenance teams (or crews) at their disposal who can perform related jobs at different locations in the company area and with different levels of priority. This paper presents a new methodology for the optimisation of related maintenance schedules resulting in clear prioritisation of the ordering of maintenance tasks for crews. The scheduling problem is formulated as a multi-objective optimisation problem with the following three objectives, namely the minimisation of the total maintenance cost, the minimisation of travel times of maintenance teams and the maximisation of the job's priority score, all over a pre-defined scheduling horizon. The optimisation problem is solved using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) optimisation method. The results obtained from a real-life UK case study demonstrate that the new methodology can determine optimal, low-cost maintenance schedules in a computationally efficient manner when compared to the corresponding existing company schedules. Daily productivity of maintenance teams in terms of number of jobs completed improved by 26% when the methodology was applied to scheduling in the case study. Given this, the method has the potential to be applied within water utilities and the water utility Welsh Water (Dŵr Cymru Welsh Water (DCWW)) is currently implementing it into their systems.