Genetic algorithms to solve the cover printing problem

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.

[1]  Harish Hirani,et al.  Journal bearing design using multiobjective genetic algorithm and axiomatic design approaches , 2005 .

[2]  Jacques Teghem,et al.  A Pareto Fitness Genetic Algorithm , 2004 .

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Carlos A. Coello Coello,et al.  Evolutionary Algorithms and Multiple Objective Optimization , 2003 .

[5]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[6]  M. Pirlot,et al.  Embedding of linear programming in a simulated annealing algorithm for solving a mixed integer production planning problem , 1995 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[9]  Gary Boone,et al.  Optimal capacitor placement in distribution systems by genetic algorithm , 1993 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Hiroshi Sasaki,et al.  A two level hybrid GA/SLP for FACTS allocation problem considering voltage security , 2003 .

[12]  V. S. Summanwar,et al.  Solution of constrained optimization problems by multi-objective genetic algorithm , 2002 .

[13]  Dong Cao,et al.  Capacitated plant selection in a decentralized manufacturing environment: A bilevel optimization approach , 2006, Eur. J. Oper. Res..

[14]  Zdravko Kravanja,et al.  The two-level strategy for MINLP synthesis of process flowsheets under uncertainty , 2000 .

[15]  Luca Podofillini,et al.  A multiobjective genetic algorithm approach to the optimization of the technical specifications of a nuclear safety system , 2004, Reliab. Eng. Syst. Saf..

[16]  Francisco Jurado,et al.  Enhancing the electrical performance of a solid oxide fuel cell using multiobjective genetic algorithms , 2005 .

[17]  Ahmet B. Keha,et al.  Using genetic algorithms for single-machine bicriteria scheduling problems , 2003, Eur. J. Oper. Res..

[18]  M. A. Abido,et al.  A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch , 2003 .