Otimização estocástica na programação de bombas em redes de abastecimento urbano

• Ano de defesa: 2014
• Autor(a) principal: De La Vega Martinez, Jonathan Justen
• Orientador(a): Alem Junior, Douglas José
• Banca de defesa: Toledo, Franklina Maria Bragion de , Toso, Eli Angela Vitor , Oliveira, Fabricio Carlos Pinheiro
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP-So
• País: BR
• Palavras-chave em Português: processo estocástico; Monte Carlo, Método de; abastecimento de água nas cidades; programação de bombas; sistemas de abastecimento de água em redes urbanas; demanda por água estocástica; programação estocástica; análise de robustez; desvio médio absoluto; Simulação Monte-Carlo
• Palavras-chave em Inglês: pump scheduling; water supply systems in urban networks; stochastic water-demand; stochastic programming; robustness anlysis; mean absolute deviation; Monte-Carlo Simulation
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/3828

This study presents a pump scheduling problem for the capture, transfer and storage of water supply systems in urban networks, whose objective is to minimize the electricity cost associated to the pumping operations. To deal with the dynamic and random nature of the water-demand, we propose two-stage stochastic programming with recourse models, where the random variables are represented by a finite and discrete set of realizations or scenarios. The developed mathematical models are extensions of previous deterministic models of the literature and they reflect the basic assumption that a fixed cost could be incurred by the turn on/ turn off activities of the hydraulic pumps. In order to control violations of the water-demand constraints in the presence of multiple different scenarios, we also consider a robustness technique in an attempt to obtain almost feasible solutions. Last, but not least, we adopt a risk-aversion criteria so-called mean absolute deviation to obtain second-stage costs less dependent on the realizations of the scenarios. The scenarios were generated according to a Monte-Carlo simulation procedure that may use any probability distributions to produce the empirical probabilities of the random variables. As the proposed pump scheduling problem with fixed cost is a two-stage stochastic mixed 0 − 1 program, we develop a efficient hybrid heuristic to obtain good-quality solutions of practical instances in a plausible running time. Overall results evidence the stability of the scenario generation method, the sensitivity of the solution according to the key parameters of the mathematical model, and the efficiency of the heuristic in solving large instances. Finally, we show that is possible to save resources by solving the stochastic programming model instead of adopting simpler approaches based on the expected value.