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A new adaptive real-coded memetic algorithm
, Article 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009 ; Volume 1 , 2009 , Pages 368-372 ; 9780769538167 (ISBN) ; Darabi, D ; Sharif University of Technology
Abstract
A new adaptive real-coded memetic algorithm has been developed for continuous optimization problems. The proposed algorithm utilizes an adaptive variant of Continuous Ant Colony System for local search. Here new adaptive strategies are utilized for online tuning of the number of local search steps and the width of the search interval over each dimension of the search space. A new crossover scheme is also developed and utilized. The new algorithm has been examined with CEC 2005 benchmarks and compared with three other state of the art works in the field. The comparisons have showed relatively better results. © 2009 IEEE
Green Flexible Supply Chain Network Design; A Genetic Algorithm Approach
, M.Sc. Thesis Sharif University of Technology ; Akbari Jokar, Mohammad Reza (Supervisor)
Abstract
This study aimed to develop a green network design for a multi-echelon multi-product supply chain with a focus on bottleneck points as the means of enhancing unutilized capacities to maintain the optimal levels of flexibility. Two different models were developed to compare different approaches of flexible capacity planning by minimizing the total cost of facility establishment as well as their utilization and transportation cost. The proposed mixed-integer linear programming model was characterized by its novelty in taking bottleneck points into account to build a platform for flexible capacity planning required for responding to uncertainties such as fluctuations in demand and new product...
Scheduling TV commercials using genetic algorithms
, Article International Journal of Production Research ; Volume 51, Issue 16 , 2013 , Pages 4921-4929 ; 00207543 (ISSN) ; Alaei, R ; Sharif University of Technology
2013
Abstract
In this paper, the problem of scheduling commercial messages during the peak of viewing time of a TV channel is formulated as a combinatorial auction-based mathematical programming model. Through this model, a profitable and efficient mechanism for allocating the advertising time to advertisers is developed by which the revenue of TV channels is maximised while the effectiveness of advertising is increased. We developed a steady-state genetic algorithm to find an optimal or a near optimal solution for the proposed problem. A computational experiment was conducted for evaluating the efficiency of the proposed algorithm. A set of test problems with different sizes were generated, using the...