Improving Water and Energy Resource Management: A Comparative Study of Solution Representations for the Pump Scheduling Optimization Problem

Sergio A. Silva-Rubio, Yamisleydi Salgueiro, Daniel Mora-Meliá () and Jimmy H. Gutiérrez-Bahamondes
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Sergio A. Silva-Rubio: Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile
Yamisleydi Salgueiro: Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile
Daniel Mora-Meliá: Departamento de Ingeniería y Gestión de la Construcción, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile
Jimmy H. Gutiérrez-Bahamondes: Departamento de Ciencias de la Computación, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile

Mathematics, 2024, vol. 12, issue 13, 1-21

Abstract: Water distribution networks (WDNs) are vital for communities, facing threats like climate change and aging infrastructure. Optimizing WDNs for energy and water savings is challenging due to their complexity. In particular, pump scheduling stands out as a fundamental tool for optimizing both resources. Metaheuristics such as evolutionary algorithms (EAs) offer promising solutions, yet encounter limitations in robustness, parameterization, and applicability to real-sized networks. The encoding of decision variables significantly influences algorithm efficiency, an aspect frequently overlooked in the literature. This study addresses this gap by comparing solution representations for a multiobjective pump scheduling problem. By assessing metrics such as execution time, convergence, and diversity, it identifies effective representations. Embracing a multiobjective approach enhances comprehension and solution robustness. Through empirical validation across case studies, this research contributes insights for the more efficient optimization of WDNs, tackling critical challenges in water and energy management. The results demonstrate significant variations in the performance of different solution representations used in the literature. In conclusion, this study not only provides perspectives on effective pump scheduling strategies but also aims to guide future researchers in selecting the most suitable representation for optimization problems.

Keywords: optimization; solution representation; evolutionary algorithms; multiobjective problem; NSGA-II; pump scheduling; water distribution networks; EPANET (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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