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 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.

• 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