A study of genetic crossover operations on the facilities layout problem
Computers and Industrial Engineering
A solution to the facility layout problem using simulated annealing
Computers in Industry - Special issue: ASI'96: life cycle approaches to production systems: management, control and supervision
Facilities layout design by genetic algorithms
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multi-objective evolutionary approach for solving facility layout problem using local search
Proceedings of the 2010 ACM Symposium on Applied Computing
An adaptive local search based genetic algorithm for solving multi-objective facility layout problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Using pareto-optimality for solving multi-objective unequal area facility layout problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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Over the years, various evolutionary approaches have been proposed in efforts to solve the facility layout problem (FLP). Unfortunately, most of these approaches are limited to a single objective only, and often fail to meet the requirements for real-world applications. To date, there are only a few multi-objective FLP approaches have been proposed. However, they are implemented using weighted sum method and inherit the customary problems of this method. In this paper, we propose an evolutionary approach for solving multi-objective FLP using multi-objective genetic algorithm that presents the layout as a set of Pareto optimal solutions optimizing both quantitative and qualitative objective simultaneously. Experimental results obtained with the proposed algorithm on test problems taken from the literature are promising.