Capítulo 1: Descripción del proyecto 1.1 Introducción 1.2 Objetivos Capítulo 2: Contexto y descripción del problema de estudio 2.1 Contexto y descripción del problema. 2.2 Justificación. 2.3 Metodología propuesta. Capítulo 3: Marco referencial 3.1 Marco teórico.3.2 Estado del arte. Marco conceptual.Capítulo 4: Caracterización del sistema de relocalización de los vehículos de servicio público taxi en una empresa en la ciudad de Barranquilla 4.1 Estudio de satisfacción de los usuarios frente al servicio público de taxi en la Ciudad. 4.2 Descripción de la empresa y funcionamiento del sistema de la prestación de servicio.Capítulo 5: Desarrollo del modelo matemático de relocalización de los vehículos de servicio público taxi en una empresa en la ciudad de Barranquilla 5.1 Modelo de referencia. 5.2 Modelo propuesto. Escenarios alternativos de prueba.EVALUACIÓN DEL SISTEMA DE RELOCALIZACIÓN Capítulo 6: Análisis y resultados 6.1 Análisis de resultados obtenidos de la modelación.6.2 Estrategias de mejoramiento para el sistema de relocalización del servicio público taxi.

Evolutionary algorithms (EAs) have been applied to many optimization problems successfully in recent years. The genetic algorithm (GAs) and evolutionary programming (EP) are two di!erent types of EAs. GAs use crossover as the primary search operator and mutation as a background operator, while EP uses mutation as the primary search operator and does not employ any crossover. This paper proposes a novel EP algorithm for cutting stock problems with and without contiguity. Two new mutation operators are proposed. Experimental studies have been carried out to examine the e!ectiveness of the EP algorithm. They show that EP can provide a simple yet more e!ective alternative to GAs in solving cutting stock problems with and without contiguity. The solutions found by EP are signi"cantly better (in most cases) than or comparable to those found by GAs. Scope and purposeThe one-dimensional cutting stock problem (CSP) is one of the classical combinatorial optimization problems. While most previous work only considered minimizing trim loss, this paper considers CSPs with two objectives. One is the minimization of trim loss (i.e., wastage). The other is the minimization of the number of stocks with wastage, or the number of partially "nished items (pattern sequencing or contiguity problem). Although some traditional OR techniques (e.g., programming based approaches) can "nd the global optimum for small CSPs, they are impractical to "nd the exact global optimum for large problems due to combinatorial explosion. Heuristic techniques (such as various hill-climbing algorithms) need to be used for large CSPs. One of the heuristic algorithms which have been applied to CSPs recently with success is the genetic algorithm (GA). This paper proposes a much simpler evolutionary algorithm than the GA, based on evolutionary programming (EP). The EP algorithm has been shown to perform signi"cantly better than the GA for most benchmark problems we used and to be comparable to the GA for other problems.

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.