On the maintenance task list of each water distribution system (WDS) operator, determination of the order of undertaken repairs seems quite a typical task. Characteristics of damages, their localization, and other factors that influence repair sequencing have a sound impact on the execution of such tasks. In the case of the most complex cases where numerous failures of different types occur at the very same time (i.e., due to earthquakes), there is a long list of selection criteria that have to be analyzed to deliver an objectively logical schedule for repair teams. In this article, authors attempt to find out if it is possible to define pipe rankings in having obtained the best factors for defined objective functions (criteria), making it feasible to deliver judicious repair sequencing. For the purposes of this paper, a survey has been carried out. Its conclusions made it possible to propose a method to create rankings of pipes and evaluate them using a selected multicriteria decision method: preference ranking organization method for enrichment evaluation (PROMETHEE). The work was carried out for five different disaster scenarios that had been supplied by ‘The Battle of Post-Disaster Response and Restoration’ organization committee. Obtained results might be further used to finetune this sequencing method of undertaken repairs, while conclusions could be useful to model similar events in WDS when required. This article is an extended paper based on the conference preprint presented at the 1st International Water Distribution Systems Analysis (WDSA)/International Computing & Control for the Water Industry (CCWI) Joint Conference in July 23–25, 2018 in Kingston, Ontario, Canada.

This study demonstrates the use of the differential evolution method for optimizing multi-source water supply systems. The objective was to identify a water-supply scenario that minimizes the total water-supply costs in multiple sources network. The analysis assumed varying water-production costs at each source, with control implemented through the adjustment of valve settings at the network inlets. For the research, open-source tools such as EPANET, the WNTR library, and Python’s scipy.optimize package, were utilized. The analyses were conducted on two water distribution networks. The first was a simplified network with three water sources, designed to validate the simulation environment and perform initial assessments of the optimization algorithms, whereas the second was a complex model representing a real water distribution network. Analyses were conducted for the latter assuming three, four, five, and six sources. For both networks, multiple pricing scenarios were evaluated to explore how different conditions impact the performance of the algorithm. The results show that near-optimal solutions can be achieved with significantly reduced computation times. The differences in objective function values between the brute force and differential evolution (DE) methods were no greater than 0.2%, while the computation time for DE was substantially shorter. These findings highlight DE scalability, efficiency, and potential for real-world applications, promoting cost-effective and accessible optimization in engineering practice.