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Instituto Tecnológico de Informática has a non-economic Activities Plan (PROMECE) whose general objective is to strengthen the research lines in which the Institute works, within the scope of Information and Communication Technologies (ICT). Within the Optimization Systems R&D Line we work to improve and facilitate the decision making process in supply chain management, from production and purchasing planning to delivery to the final customer, with a global vision, a realistic optimization perspective, and in a simple way. This document presents the results of applying optimization algorithms in 4 scenarios present in real operation routing problems: first, adjustment of departure time according to client time windows; second, use of outsource fleet; third, inclusion of unloading points prior to return to depot and, finally, vehicles that stay overnight in a place different from the depot. ; PROMECE 2016. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of IVACE network. File number: IMAMCN/2016/1

Recently, many new types of wireless networks have emerged for both civil and military applications, such as wireless sensor networks, ad hoc networks, among others. To improve the performance of these wireless networks, many advanced communication techniques have been developed at the physical layer. For both theoretical and practical purposes, it is important for a network researcher to understand the performance limits of these new wireless networks. Such performance limits are important not only for theoretical understanding, but also in that they can be used as benchmarks for the design of distributed algorithms and protocols. However, due to some unique characteristics associated with these networks, existing analytical technologies may not be applied directly. As a result, new theoretical results, along with new mathematical techniques, need to be developed. In this dissertation, we focus on the design of new algorithms and optimization techniques to study theoretical performance limits associated with these new wireless networks. In this dissertation, we mainly focus on sensor networks and ad hoc networks. Wireless sensor networks consist of battery-powered nodes that are endowed with a multitude of sensing modalities. A wireless sensor network can provide in-situ, unattended, high-precision, and real-time observation over a vast area. Wireless ad hoc networks are characterized by the absence of infrastructure support. Nodes in an ad hoc network are able to organize themselves into a multi-hop network. An ad hoc network can operate in a stand-alone fashion or could possibly be connected to a larger network such as the Internet (also known as mesh networks). For these new wireless networks, a number of advanced physical layer techniques, e.g., ultra wideband (UWB), multiple-input and multiple-output (MIMO), and cognitive radio (CR), have been employed. These new physical layer technologies have the potential to improve network performance. However, they also introduce some unique design challenges. For example, CR is capable of reconfiguring RF (on the fly) and switching to newly-selected frequency bands. It is much more advanced than the current multi-channel multi-radio (MC-MR) technology. MC-MR remains hardware-based radio technology: each radio can only operate on a single channel at a time and the number of concurrent channels that can be used at a wireless node is limited by the number of radio interfaces. While a CR can use multiple bands at the same time. In addition, an MC-MR based wireless network typically assumes there is a set of "common channels" available for all nodes in the network. While for CR networks, each node may have a different set of frequency bands based on its particular location. These important differences between MC-MR and CR warrant that the algorithmic design for a CR network is substantially more complex than that under MC-MR. Due to the unique characteristics of these new wireless networks, it is necessary to consider models and constraints at multiple layers (e.g., physical, link, and network) when we explore network performance limits. The formulations of these cross-layer problems are usually in very complex forms and are mathematically challenging. We aim to develop some novel algorithmic design and optimization techniques that provide optimal or near-optimal solutions. The main contributions of this dissertation are summarized as follows. 1. Node lifetime and rate allocation We study the sensor node lifetime problem by considering not only maximizing the time until the first node fails, but also maximizing the lifetimes for all the nodes in the network. For fairness, we maximize node lifetimes under the lexicographic max-min (LMM) criteria. Our contributions are two-fold. First, we develop a polynomial-time algorithm based on a parametric analysis (PA) technique, which has a much lower computational complexity than an existing state-of-the-art approach. We also present a polynomial-time algorithm to calculate the flow routing schedule such that the LMM-optimal node lifetime vector can be achieved. Second, we show that the same approach can be employed to address a different but related problem, called LMM rate allocation problem. More important, we discover an elegant duality relationship between the LMM node lifetime problem and the LMM rate allocation problem. We show that it is sufficient to solve only one of the two problems and that important insights can be obtained by inferring the duality results. 2. Base station placement Base station location has a significant impact on sensor network lifetime. We aim to determine the best location for the base station so as to maximize the network lifetime. For a multi-hop sensor network, this problem is particularly challenging as data routing strategies also affect the network lifetime performance. We present an approximation algorithm that can guarantee (1- )-optimal network lifetime performance with any desired error bound > 0. The key step is to divide the continuous search space into a finite number of subareas and represent each subarea with a "fictitious cost point" (FCP). We prove that the largest network lifetime achieved by one of these FCPs is (1- )-optimal. This approximation algorithm offers a significant reduction in complexity when compared to a state-of-the-art algorithm, and represents the best known result to this problem. 3. Mobile base station The benefits of using a mobile base station to prolong sensor network lifetime have been well recognized. However, due to the complexity of the problem (time-dependent network topology and traffic routing), theoretical performance limits and provably optimal algorithms remain difficult to develop. Our main result hinges upon a novel transformation of the joint base station movement and flow routing problem from the time domain to the space domain. Based on this transformation, we first show that if the base station is allowed to be present only on a set of pre-defined points, then we can find the optimal sojourn time for the base station on each of these points so that the overall network lifetime is maximized. Based on this finding, we show that when the location of the base station is un-constrained (i.e., can move to any point in the two-dimensional plane), we can develop an approximation algorithm for the joint mobile base station and flow routing problem such that the network lifetime is guaranteed to be at least (1- ) of the maximum network lifetime, where can be made arbitrarily small. This is the first theoretical result with performance guarantee on this problem. 4. Spectrum sharing in CR networks Cognitive radio is a revolution in radio technology that promises unprecedented flexibility in radio communications and is viewed as an enabling technology for dynamic spectrum access. We consider a cross-layer design of scheduling and routing with the objective of minimizing the required network-wide radio spectrum usage to support a set of user sessions. Here, scheduling considers how to use a pool of unequal size frequency bands for concurrent transmissions and routing considers how to transmit data for each user session. We develop a near-optimal algorithm based on a sequential fixing (SF) technique, where the determination of scheduling variables is performed iteratively through a sequence of linear programs (LPs). Upon completing the fixing of these scheduling variables, the value of the other variables in the optimization problem can be obtained by solving an LP. 5. Power control in CR networks We further consider the case of variable transmission power in CR networks. Now, our objective is minimizing the total required bandwidth footprint product (BFP) to support a set of user sessions. As a basis, we first develop an interference model for scheduling when power control is performed at each node. This model extends existing so-called protocol models for wireless networks where transmission power is deterministic. As a result, this model can be used for a broad range of problems where power control is part of the optimization space. An efficient solution procedure based on the branch-and-bound framework and convex hull relaxations is proposed to provide (1- )-optimal solutions. This is the first theoretical result on this important problem. ; Ph. D.

CEI (Ciudad Energéticamente Inteligente – Energetically Smart City) aims at improving the energy and environment conditions of living areas of the city, by managing correctly the existing infrastructures and resources. The improvement will be enabled by the development of technology systems (energy nodes control technologies, energy-environmental sensors technologies, technologies for the improvement of maintenance methodologies) that make possible the control of energy and environment conditions of those areas. This project is funded by the Instituto Valenciano de Competitividad Empresarial (IVACE) and the European Union through the Fondo Europeo de Desarrollo Regional (FEDER). Under this perspective we have developed an optimized planning maintenance services system based on the physical location where the services have to be performed, their requirements and restrictions, and the available resources capable to deliver those services. This document includes a briefing of the work done to construct algorithms to optimize maintenance services routing. These algorithms schedule periodic tasks so that they can be completed with the minimum cost in terms of use of resources and energy, as well as achieving required compliance. Final result of the algorithms is a list of scheduled routes ands tasks to be perfomed each day of the calendar in the planned horizon. ; CEI (Ciudad Energéticamente Inteligente – Energetically Smart City). Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for 2015. File number: IMDECA/2015/14

Many professions occasionally involve the selection of an alternative from among many problem solutions which have impacts in multiple-interest areas; however, due to the very nature of his work, the practicing engineer, regardless of specialty, is unavoidably engaged in this selection process. The emergence of national concern for environmental and social consequences of technical enterprises, as reflected through legislative action, has accentuated the need for multicriteria design methodologies in some areas of engineering (i.e., automotive). Consequently, interest in the development of pragmatic and theoretically sound approaches to multi-impact design situations has been keen. Any approach to multicriteria design/decision problems involves two fundamental aspects: (1) generating information regarding the range of possible designs and their associated impacts; and (2) generating relative value information which is used to compare the relative imp-cats leading to the selection of a "preferred" or "best compromise" alternative. The methodology developed herein is the integration of a formal mathematical programming technique for generating the full range of feasible alternatives with a pragmatic and well-accepted group-interaction technique for extracting value information regarding alternatives. The integration results in an iterative group-interaction process which leads to successive reductions in the preferred range of alternatives until the most preferred alternative is identified. The methodology developed in this research represents an improvement over other methodologies reported in the literature in two areas: 1) The noninferior set is explicitly identified insuring selection of a group decision point which is noninferior, 2) a least squared error mathematical filtering technique is developed for smoothing relative value data obtained from the decision making body. In addition, a convergence proof is developed which not only indicates the theoretically sound and robust nature of the algorithm developed in this work but in addition provides a basis for an improved class of algorithms for solving classical nonlinear constrained problems. The technique was developed for and implemented in an interactive software package. The multiobjective decision problem is solved in a single encounter with a cooperative decision making group.

The Gray Wolf Optimizer (GWO) is a population-based meta-heuristic algorithm that belongs to the family of swarm intelligence algorithms inspired by the social behavior of gray wolves, in particular the social hierarchy and hunting mechanism. Because of its simplicity, flexibility, and few parameters to be tuned, it has been applied to a wide range of optimization problems. And yet it has some disadvantages, such as poor exploration skills, stagnation at local optima, and slow convergence speed. Therefore, different variants of GWO have been proposed and developed to address these disadvantages. In this article, some literature, especially from the last five years, has been reviewed and summarized by well-known publishers. First, the inspiration and the mathematical model of GWO were explained. Subsequently, the improved GWO variants were divided into four categories and discussed. After that, each variant's methodology and experiments were explained and clarified. The study ends with a summary conclusion of the main foundation of GWO and suggests some possible future directions that can be explored further.