Elements of artificial neural networks
Elements of artificial neural networks
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Panel: simulation optimization: future of simulation optimization
Proceedings of the 33nd conference on Winter simulation
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
New advances and applications for marrying simulation and optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
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Many real-world manufacturing problems are too complex to be modelled analytically. For these problems, simulation can be a powerful tool for system analysis and optimisation. While traditional optimisation methods have been unable to cope with the complexities of many problems approached by simulation, evolutionary algorithms have proven to be highly useful. This paper describes how simulation and evolutionary algorithms have been combined to improve a manufacturing cell at Volvo Aero in Sweden. This cell produces high-technology engine components for civilian and military airplanes, and also for space rockets. Results from the study show that by using simulation and evolutionary algorithms, it is possible to increase the overall utilisation of the cell and at the same time decrease the number of overdue components.