A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks

Cell Switch-Off (CSO) is recognized as a promising approach to reduce the energy consumption in next-generation cellular networks. However, CSO poses serious challenges not only from the resource allocation perspective but also from the implementation point of view. Indeed, CSO represents a difficult optimization problem due to its NP-complete nature. Moreover, there are a number of important practical limitations in the implementation of CSO schemes, such as the need for minimizing the real-time complexity and the number of on-off/off-on transitions and CSO-induced handovers. This article introduces a novel approach to CSO based on multiobjective optimization that makes use of the statistical description of the service demand (known by operators). In addition, downlink and uplink coverage criteria are included and a comparative analysis between different models to characterize intercell interference is also presented to shed light on their impact on CSO. The framework distinguishes itself from other proposals in two ways: 1) The number of on-off/off-on transitions as well as handovers are minimized, and 2) the computationally-heavy part of the algorithm is executed offline, which makes its implementation feasible. The results show that the proposed scheme achieves substantial energy savings in small cell deployments where service demand is not uniformly distributed, without compromising the Quality-of-Service (QoS) or requiring heavy real-time processing

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Effectively scheduling vehicles and allocating storage locations for containers are two important problems in container terminal operations. Early research efforts, however, are devoted to study them separately. This paper investigates the integration of the two problems focusing on the unloading process in an automated container terminal, where all or part of the equipment are built in automation. We formulate the integrated problem as a mixed-integer programming (MIP) model to minimise ship’s berth time. We determine the detailed schedules for all vehicles to be used during the unloading process and the storage location to be assigned for all containers. A series of experiments are carried out for small-sized problems by using commercial software. A genetic algorithm (GA) is designed for solving large-sized problems. The solutions from the GA for the small-sized problems are compared with the optimal solutions obtained from the commercial software to verify the effectiveness of the GA. The computational results show that the model and solution methods proposed in this paper are efficient in solving the integrated unloading problem for the automated container terminal

Simulation-Based Evolutionary Optimization of Air Traffic Management

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involve-ment of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS)and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione