Simheuristics: an introductory tutorial
Fecha
2022Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
Impacto
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10.1109/WSC57314.2022.10015318
Resumen
Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimi ...
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Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed. [--]
Materias
Uncertainty,
Metaheuristics,
Stochastic processes,
Transportation,
Tutorials,
Machine learning,
Manufacturing
Editor
IEEE
Publicado en
2022 Winter Simulation Conference (WSC): IEEE; 2022. p.1325-1339 978-1-6654-7661-4
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22 /AEI/ 10.13039/501100011033 and RED2018-102642-T), as well as by the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020–2023 (grant 21S09355-001). Moreover, we appreciate the financial support of the Erasmus+ Program (2019-I-ES01-KA103-062602).