Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics
Transportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all o...
- Autores:
-
Tordecilla Madera, Rafael David
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2022
- Institución:
- Universidad de la Sabana
- Repositorio:
- Repositorio Universidad de la Sabana
- Idioma:
- eng
- OAI Identifier:
- oai:intellectum.unisabana.edu.co:10818/57593
- Acceso en línea:
- https://hdl.handle.net/10818/57593
- Palabra clave:
- Transporte -- Planificación
Logística en los negocios
Mercancías
Toma de decisiones
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
| title |
Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics Transporte -- Planificación Logística en los negocios Mercancías Toma de decisiones |
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
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Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
| title_sort |
Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics |
| dc.creator.fl_str_mv |
Tordecilla Madera, Rafael David |
| dc.contributor.advisor.none.fl_str_mv |
Montoya Torres, Jairo Rafael |
| dc.contributor.author.none.fl_str_mv |
Tordecilla Madera, Rafael David |
| dc.subject.armarc.none.fl_str_mv |
Transporte -- Planificación Logística en los negocios Mercancías Toma de decisiones |
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Transporte -- Planificación Logística en los negocios Mercancías Toma de decisiones |
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Transportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution ¿e.g., a solution with the minimum cost or the maximum profit¿ is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems¿ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one. |
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2022 |
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2022-09-07T19:13:35Z |
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2022-09-07T19:13:35Z 2023-10-04T20:09:43Z |
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2022-07-14 |
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Tesis/Trabajo de grado - Doctorado |
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287522 TE11907 |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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Montoya Torres, Jairo RafaelTordecilla Madera, Rafael David2022-09-07T19:13:35Z2022-09-07T19:13:35Z2023-10-04T20:09:43Z2022-07-14https://hdl.handle.net/10818/57593287522TE11907Transportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution ¿e.g., a solution with the minimum cost or the maximum profit¿ is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems¿ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima ¿por ejemplo, una solución con el mínimo costo o la máxima utilidad¿ es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctor en Logística y Gestión de Cadenas de SuministrosDoctorado242 páginasapplication/pdfengUniversidad de La SabanaDoctorado en Logística y Gestión de Cadenas de SuministrosFacultad de IngenieríaAttribution-NonCommercial-NoDerivatives 4.0 InternacionalAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristicsTesis/Trabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/doctoralThesishttp://purl.org/redcol/resource_type/TDTransporte -- PlanificaciónLogística en los negociosMercancíasToma de decisionesAazam, Mohammad and Eui-Nam Huh (2014). “Fog computing and smart gateway based communication for cloud of things”. 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