Influence of Solution Representation in a Multi-Objective Pump Scheduling Problem

The efficient management of water and energy resources through pump scheduling is crucial due to the increasing limitations and costs associated with both resources. A common approach is to use explicit pump scheduling, controlling their on/off states during predefined time intervals. The optimization problem is multi-objective, simultaneously considering energy costs and water quality. This involves a significant number of decision variables, expanding the search space and slowing down the process. Therefore, measures to enhance algorithm performance are required. This study compares different solution representations to address this optimization problem, evaluating their performance using two metrics. Additionally, hyperparameter optimization is performed to improve the results. The findings illustrate variations among the different solution representations. Statistical analysis of the results identifies the most efficient representation in terms of optimal solutions and feasibility. This enables us to select the best representation for application to a larger network, resulting in improved efficiency in terms of solutions.