Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Engineering Optimization: Theory and Practice
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An Improved Genetic Algorithm on Logistics Delivery in E-business
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Simulation of an Automated Warehouse for Steel Tubes
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
A comparative study of genetic algorithm components in simulation-based optimisation
Proceedings of the 40th Conference on Winter Simulation
Fuzzy optimality and evolutionary multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Using genetic algorithms in process planning for job shop machining
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Phase transition in a foreign exchange market-analysis based on anartificial market approach
IEEE Transactions on Evolutionary Computation
Rolling System Design Using Evolutionary Sequential Process Optimization
IEEE Transactions on Evolutionary Computation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The paper analyses the issues behind allocation and reordering strategies optimization for an existing automated warehouse for the steelmaking industry. Genetic Algorithms are employed to this purpose by deriving custom chromosome structures as well as ad-hoc crossover and mutation operators. A comparison between three different solutions capable to deal with multi-objective optimization are presented: the first approach is based on a common linear weighting function that combines different objectives; in the second one, a fuzzy system is used to aggregate objective functions, while in the last one the Strength Pareto Evolutionary Algorithm is applied in order to exploit a real multi-objective optimization. These three approaches are described and results are presented in order to highlight benefits and pitfalls of each technique.