By Miguel Mujica Mota, Idalia Flores De La Mota
Presenting innovations, case-studies and methodologies that mix using simulation ways with optimization strategies for dealing with difficulties in production, logistics, or aeronautical difficulties, this booklet offers recommendations to universal business difficulties in numerous fields, which diversity from production to aviation difficulties, the place the typical denominator is the combo of simulation’s flexibility with optimization strategies’ robustness.
Providing readers with a finished consultant to take on related concerns in commercial environments, this article explores novel how you can face commercial difficulties via hybrid techniques (simulation-optimization) that enjoy the merits of either paradigms, with a view to supply strategies to special difficulties in provider undefined, construction tactics, or offer chains, equivalent to scheduling, routing difficulties and source allocations, between others.
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Extra resources for Applied Simulation and Optimization: In Logistics, Industrial and Aeronautical Practice
9] who considered the distribution of industrial gases; since then, many variants of the IRP have been studied. Moin and Salhi  provide an overview where they state that most problem formulations do not consider stochastic demand patterns and are deterministic. In contrast, the stochastic IRP (SIRP) considers product usages as probability distributions, but this also increases the problem complexity, which motivates a simulation-based approach. The considered IRP here is a mixed formulation in which some of the customers choose VMI, while the other customers keep an order-based strategy.
The second is applicable to optimization analysis, where we can maximize or minimize important criteria or objectives to streamline the effectiveness of the system . H. Z. Pece Department of Electrical Engineering - DAELT - Postgraduate Program in Biomedical Engineering, Federal Technological University of Paraná - UTFPR, Curitiba, PR, Brazil © Springer International Publishing Switzerland 2015 M. Mujica Mota et al. H. Z. , machine configuration, maintainers, costs, and individual reliabilities).
Gosavi. Simulation-based optimization: parametric optimization techniques and reinforcement learning, volume 25. Springer, 2003. 21. N. Hansen. The CMA evolution strategy: a comparing review. In J. Lozano, P. Larranaga, I. Inza, and E. Bengoetxea, editors, Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pages 75–102. Springer, 2006. 22. S. Hutterer and M. Affenzeller. Probabilistic electric vehicle charging optimized with genetic algorithms and a two-stage sampling scheme.