By Miguel Mujica Mota, Idalia Flores De La Mota
Presenting suggestions, case-studies and methodologies that mix using simulation ways with optimization ideas for dealing with difficulties in production, logistics, or aeronautical difficulties, this ebook offers options to universal commercial difficulties in different fields, which diversity from production to aviation difficulties, the place the typical denominator is the combo of simulation’s flexibility with optimization options’ robustness.
Providing readers with a accomplished advisor to take on related concerns in business environments, this article explores novel how one can face commercial difficulties via hybrid methods (simulation-optimization) that enjoy the merits of either paradigms, in an effort to provide recommendations to big difficulties in provider undefined, construction methods, or offer chains, corresponding to scheduling, routing difficulties and source allocations, between others.
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Featuring options, case-studies and methodologies that mix using simulation techniques with optimization strategies for dealing with difficulties in production, logistics, or aeronautical difficulties, this publication presents suggestions to universal business difficulties in different fields, which variety from production to aviation difficulties, the place the typical denominator is the mix of simulation’s flexibility with optimization recommendations’ robustness.
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Additional resources for Applied Simulation and Optimization: In Logistics, Industrial and Aeronautical Practice
Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University, Linz, Austria, 2009. 53. S. Wagner, G. Kronberger, A. Beham, M. Kommenda, A. Scheibenpflug, E. Pitzer, S. Vonolfen, M. Kofler, S. Winkler, V. Dorfer, and M. Affenzeller. Advanced Methods and Applications in Computational Intelligence, volume 6 of Topics in Intelligent Engineering and Informatics, chapter Architecture and Design of the HeuristicLab Optimization Environment, pages 197– 261.
Most of the times the sum of the squared errors at predefined time steps is calculated and used as quality value. Every algorithm which is able to handle real-vector encoded problems could be used to solve this parameter optimization problem for continuous simulation models. HeuristicLab provides several algorithms which are suitable for this task: Genetic algorithms, evolution strategies, simulated annealing, etc. However, it was observed that the best results regarding solution quality, convergence speed, and robustness were obtained using the covariance matrix adaptation evolution strategy (CMAES) .
Hutterer, S. Vonolfen, and M. Affenzeller. Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow. In Companion Publication of the 2013 Genetic and Evolutionary Computation Conference, pages 1529–1536, 2013. 25. O. R. Inderwildi and D. A. King. Quo vadis biofuels? Energy Environ. , 2:343–346, 2009. 26. J. Kennedy and R. C. Eberhardt. Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, volume 4, pages 1942–1948.
Applied Simulation and Optimization: In Logistics, Industrial and Aeronautical Practice by Miguel Mujica Mota, Idalia Flores De La Mota