Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm

• Main Author: Abiodun, Folorunso Taliha
• Format: Thesis
• Language: English
• Published: 2013
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf; http://eprints.utm.my/id/eprint/33264/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70028?site_name=Restricted Repository
• Institution: Universiti Teknologi Malaysia

Water supply system has an inherently high operational cost. This is significantly due to the high amount of electric energy expended by the pumps of the system and the cost of their maintenance in cause of delivering water for the daily use by the consumers. Scheduling the operations of the pumps in the system ensures that the cost of energy consumed is minimized and also prevents the increased wear and tear in the pumps. Thus, creating an optimal schedule for the pumps is of paramount importance in order to save more electric cost which in turn leads to a reduced operational cost for the system. This work adopts the use of an Adaptive Weighted-sum Genetic Algorithm (AWGA), based on popular weighted sum approach Genetic Algorithm (GA) for multi-objective optimization problem. The AWGA weights multipliers of the individual cost functions are adaptively formed using the information of the fitness function on every generation of the GA process. This study adopts a water supply system consisting of 5 fixed speed pumps and a reservoir with the objective of minimizing the electric energy cost as well as the maintenance cost associated with the operating pumps subject to satisfaction of the maximum and minimum levels in the system reservoir. With the application of the AWGA a schedule that satisfies the demand requirement as well as the system requirement was obtained. Thereafter as a means for the validation and comparison of the results obtained, two other well known weighted sum GA approaches namely the Fixed Weighted-sum GA (FWGA) and Random Weighted-sum GA (RWGA) approaches were also simulated.. The results show that AWGA produces a schedule with a 16.2% reduction in terms of the fitness index parameter as compared 7.23% and 7.74% of the FWGA and RWGA respectively.