Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply
Selected Papers from AISB Workshop on Evolutionary Computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Multi-objective pump scheduling optimisation using evolutionary strategies
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
Resource allocation by genetic algorithm with fuzzy inference
Expert Systems with Applications: An International Journal
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Study on the inventory control of deteriorating items under VMI model based on bi-level programming
Expert Systems with Applications: An International Journal
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Water distribution systems play a vital role in a current urban infrastructure and they always consume a great deal of electricity due to pumping and conveying water for our daily use. Scheduling the pumps in these systems is a good choice for saving more electricity cost. However, conventional schemes have two shortcomings. First, conventional schemes treat a day as 24 unit time intervals and thus a pump only can be turned either on or off at each o'clock sharp. Their time encoding is over-simplified, i.e., a 24-bit binary string. It is too inflexible to save more electricity cost. Indeed, this real-world scheduling problem can be formulated in a more practical and precise way. Hopefully, the optimal solution to the problem can be found out. Second, conventional schemes do not take land subsidence into account when water distribution systems pump groundwater all day long. They achieved their goals at the expense of land subsidence. In fact, such natural resource depletion can be completely avoided or at least slowed down if groundwater is pumped intermittently. For this reason, another objective that helps alleviate land subsidence is considered. In this paper, a genetic algorithm-based pump scheduling method is proposed for not only cost reduction but also environment protection. Contrary to past methods, the proposed method can achieve lower pumping cost and provide a wider range of eco-aware schedules. The experimental results also suggest that the proposed method may be extended to other similar optimization problems and hopefully achieves near-optimal results.