Pump Scheduling Optimization Using Asynchronous Parallel Evolutionary Algorithms

Optimizing the pump-scheduling is an interesting proposal to achieve cost reductions in water distribution pumping stations. As systems grow, pump-scheduling becomes a very difficult task. In order to attack harder pump-scheduling problems, this work proposes the use of parallel asynchronous evolutionary algorithms as a tool to aid in solving an optimal pump-scheduling problem. In particular, this work considers a pump-scheduling problem having four objectives to be minimized: electric energy cost, maintenance cost, maximum power peak, and level variation in a reservoir. Parallel and sequential versions of different evolutionary algorithms for multiobjective optimization were implemented and their results compared using a set of experimental metrics. Analysis of metric results shows that our parallel asynchronous implementation of evolutionary algorithms is effective in searching for solutions among a wide range of alternative optimal pump schedules to choose from.