Article ID: | iaor20083953 |
Country: | United Kingdom |
Volume: | 34 |
Issue: | 11 |
Start Page Number: | 3346 |
End Page Number: | 3361 |
Publication Date: | Nov 2007 |
Journal: | Computers and Operations Research |
Authors: | Teghem Jacques, Elaoud Samya, Bouaziz Bassem |
Keywords: | production, heuristics: genetic algorithms |
Inspired by successful application of evolutionary algorithms to solving difficult optimization problems, we explore in this paper, the applicability of genetic algorithms (GAs) to the cover printing problem, which consists in the grouping of book covers on offset plates in order to minimize the total production cost. We combine GAs with a linear programming solver and we propose some innovative features such as the ‘unfixed two-point crossover operator’ and the ‘binary stochastic sampling with replacement’ for selection. Two approaches are proposed: an adapted genetic algorithm and a multiobjective genetic algorithm using the Pareto fitness genetic algorithm. The resulting solutions are compared. Some computational experiments have also been done to analyze the effects of different genetic operators on both algorithms.