Increasing the productivity of irradiation facility using multi objective optimization techniques

The importance of multi-objective optimization in different fields like economics, finance, oil industry vehicles industry or engineering designs, and in any problem where decision need to be taken in the presence of some constraints make it a rich area for research. In this thesis, a gamma sterilization process has been studied as a multi-objective optimization problem; it introduces the idea of combining some optimization techniques in order to improve the performance of gamma sterilization process. Two proposed optimization algorithms are introduced based on the regular genetic algorithm, ant colony, and simulated annealing algorithm, first proposed algorithm is a combination between genetic algorithm and ant colony, the second proposed algorithm is a combination between genetic algorithm and simulated annealing. The results showed that the first proposed algorithm (GA combined with AC) optimizes the time needed to finish the sterilization process by 36 % of the total time taken in the current manual system of the gamma radiation facility. The results of the second proposed algorithm (GA combined with SA) showed that the algorithm optimizes the time by 40 % from the current time in the manual system. Finally at the end of the thesis, a performance comparison between the two proposed algorithms is carried out in order to explain the performance of the proposed algorithms (GA with AC and GA with SA) and ordinary genetic algorithm results compared with the manual system results for ten different cases.