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...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/687730 |
| Acceso en línea: | http://hdl.handle.net/10803/687730 |
| Access Level: | acceso abierto |
| Palabra clave: | heurístiques aleatoritzades esbiaixades heurísticas aleatorizadas sesgadas biased-randomized heuristics simulació Montecarlo simulación Montecarlo Monte Carlo simulation simheurístiques simheurísticas simheuristics lògica difusa lógica difusa fuzzy logic problemes de ruteig problemas de ruteo routing problems problemes de localització problemas de localización location problems Logística 004 625 |
| Sumario: | 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 with the most powerful of computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, may be used 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 involves the introduction of complex mathematical formulas and procedures, however, the model realism increases and, therefore, so does its reliability in representing real world situations. Stochastic approaches, which require the use of probability distributions, are among the approaches employed most often 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. Therefore, biased-randomized heuristics and metaheuristics are firstly explained to solve T&L problems that only include deterministic parameters. Later, Monte Carlo simulation is introduced to these approaches to deal with stochastic parameters. Finally, fuzzy simheuristics are employed to address simultaneously fuzzy and stochastic uncertainty. |
|---|