Genetic algorithms to solve the cover printing problem

Genetic algorithms to solve the cover printing problem

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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: , ,
Keywords: production, heuristics: genetic algorithms
Abstract:

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.

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