الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The cornerstone of any healthy population is access to safe drinking water and healthy sanitation services although the population growth in developing countries almost entirely wiped out the gains. The main purpose of a water and waste water networks is to feed consumers with water in sufficient quantity, at appropriate pressure, of acceptable quality and collect waste water back to effectively treat it; all this process must be as economically as possible. Water distribution and sanitation systems, like any large complex system, must be operated properly so that it performs at an acceptable level of service, most networks can be operated either manually or automatically, many small systems worldwide have been operated automatically for years. Considerable effort has been invested over the last three decades in the development of optimization techniques to tackle the economic water supply and waste water collecting networks operational problems. Typically, operations are based on •tank water levels, but in this thesis an explicit representation is presented which is based on time controlled triggers. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active or inactive. Reducing energy consumption of water and waste water distribution networks has never had more significance than today. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. An application of the ACO framework was developed for the optimal scheduling of pumps. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. Minimization of electrical cost was considered as the objective, while satisfying system constraints. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. The proposed approach was tested on a small test network and on a real-world network.