Minimization of logic networks under a generalized cost function /

Includes bibliographical references.

Minimization of switching networks using negative functions

Bibliography: p. 46.

Variable Neighborhood Search for Solving the Capacitated Single Allocation Hub Location Problem

In this paper a Variable Neighborhood Search (VNS) algorithm for solving the Capacitated Single Allocation Hub Location Problem (CSAHLP) is presented. CSAHLP consists of two subproblems; the first is choosing a set of hubs from all nodes in a network, while the other comprises finding the optimal allocation of non-hubs to hubs when a set of hubs is already known. The VNS algorithm was used for the first subproblem, while the CPLEX solver was used for the second. Computational results demonstrate that the proposed algorithm has reached optimal solutions on all 20 test instances for which optimal solutions are known, and this in short computational time.

Effektiviseringsmöjligheter avseende fyllnadsgrad : En jämförande analys mellan nuläge och optimerat resultat

The study aims to provide information on efficiency opportunities on SCA's northbound cassettes. It has been made by examining the capacity utilization rate on the northbound cassettes on SCA's vessels for a period of two weeks. The cargo loaded in the ports of Rotterdam and Sheerness consists of external cargo of varying shape. The cargo is shipped northbound to Holmsund and Sundsvall. Measurements have been made on the cargo to the final destinations Sundsvall, Holmsund and Finland. The measurements have been used in a mathematical optimization model created to optimize the loading of the cassettes. The model is based on placing boxes in a grid where the boxes that are placed representing the cargo and the grids representing the cassettes. The aim of the model is to reduce the number of cassettes and thereby increase the capacity utilization rate. The study resulted in an increase in capacity utilization rate for both area and volume to all destinations. The overall improvement for all cassettes examined resulted in an increase in the area capacity utilization rate by 9.02 percentage points and 5.72 percentage points for the volume capacity utilization rate. It also resulted in a decrease of 22 cassettes in total on the four voyages that were examined which indicate that there are opportunities to improve the capacity utilization rate. The study also shows that the model can be used as a basis for similar problems.

Protocolo de roteamento para redes oportunistas utilizando otimização por enxame de partículas

In this research work, a new routing protocol for Opportunistic Networks is presented. The proposed protocol is called PSONET (PSO for Opportunistic Networks) since the proposal uses a hybrid system composed of a Particle Swarm Optimization algorithm (PSO). The main motivation for using the PSO is to take advantage of its search based on individuals and their learning adaptation. The PSONET uses the Particle Swarm Optimization technique to drive the network traffic through of a good subset of forwarders messages. The PSONET analyzes network communication conditions, detecting whether each node has sparse or dense connections and thus make better decisions about routing messages. The PSONET protocol is compared with the Epidemic and PROPHET protocols in three different scenarios of mobility: a mobility model based in activities, which simulates the everyday life of people in their work activities, leisure and rest; a mobility model based on a community of people, which simulates a group of people in their communities, which eventually will contact other people who may or may not be part of your community, to exchange information; and a random mobility pattern, which simulates a scenario divided into communities where people choose a destination at random, and based on the restriction map, move to this destination using the shortest path. The simulation results, obtained through The ONE simulator, show that in scenarios where the mobility model based on a community of people and also where the mobility model is random, the PSONET protocol achieves a higher messages delivery rate and a lower replication messages compared with the Epidemic and PROPHET protocols.

Optimización financiera de portafolios de inversión en la Bolsa de Valores de Colombia mediante el uso de RGOP

La Bolsa de Valores de Colombia (BVC) ha tenido un impacto reducido en la población colombiana sobre todo debido a la falta de educación financiera y a múltiples casos de corrupción que han opacado su rol en la sociedad -- En general, los colombianos ven con incertidumbre, desconfianza y escepticismo las ventajas de invertir en la bolsa tales como obtener rendimientos superiores que las de las inversiones tradicionales, seguridad en las transacciones y disponibilidad del dinero cuando se requiera, lo que desencadena inversiones en métodos más tradicionales, como los CDT (certificado de depósito a término), con muy bajas tasas de rendimiento, o en negocios que representan altos riesgos -- Dicho comportamiento ha generado que muchas de las inversiones que realiza una persona promedio en Colombia no vayan más allá de productos financieros conocidos, negocios familiares o tradicionales, pirámides o multiniveles que entorpecen el sistema financiero -- Una de las principales barreras encontradas al momento de invertir en la Bolsa de Valores de Colombia son creencias populares tales como: a) es obligatorio tener grandes capitales de dinero, o b) es necesario un conocimiento financiero especializado para invertir -- También resulta incierto para muchos usuarios en cuáles acciones convendría invertir una vez desmitificadas las anteriores creencias -- Para mitigar algunos de dichos inconvenientes, el estudio de portafolios de inversión propone como estrategia principal la diversificación de la inversión y la limitación del riesgo con el fin de crear portafolios altamente eficientes en términos financieros -- Si bien existen múltiples técnicas para la creación y la optimización de portafolios de inversión (por ejemplo: growth optimal portfolio), su uso en Colombia es limitado debido en lo primordial a que es una metodología reciente y a que su implementación no suele ser trivial, puesto que requiere el uso de múltiples herramientas computacionales para ser puesto en práctica -- El presente trabajo de grado presenta la implementación de un algoritmo de optimización robusto, en el sentido de las distribuciones de probabilidad requeridas, llamado portafolio óptimo de crecimiento robusto (robust growth optimal portfolio o RGOP) para acciones de la Bolsa de Valores de Colombia -- Se escogieron varios portafolios al tener en cuenta tres criterios de inclusión para las acciones y se simularon tres escenarios y una suposición con el fin de demostrar la eficacia del algoritmo para minimizar el riesgo de inversión y maximizar la tasa de crecimiento en unos horizontes de tiempo predefinidos -- En último lugar se compararon las rentabilidades de los diferentes portafolios propuestos con las tasas de captación de CDT y CDA (certificados de depósito de ahorro) de bancos populares en Colombia -- La implementación del algoritmo se realizó en la plataforma Matlab y se acudió a varias bibliotecas de modelamiento matemático -- Sin tener en cuentas los costos de transacciones por compra y venta de acciones, los resultados muestran que mientras el sector financiero ofrecía a través de los CDT inferiores de 180 días un promedio de 4.80% de rentabilidad, en un período similar el RGOP arrojaba en promedio 11.83% en los portafolios de inversión de los tres escenarios, es decir, la metodología propuesta ofreció rendimientos superiores a las ofertas de los bancos en 147% para los períodos simulados -- En conclusión, todos los escenarios analizados presentaron mejores rendimientos en la simulación que los rendimientos ofrecidos por los bancos durante el mismo período; se les dio mayor ponderación a las acciones que presentaron tasas de crecimiento mayores de tal forma que se minimizaran los riesgos implícitos de invertir en bolsa -- El RGOP mostró ser una técnica robusta para su uso con acciones de la Bolsa de Valores de Colombia porque ofreció una sólida combinación entre retorno y riesgo para futuros inversionistas

Open-pit block sequencing optimization: A mathematical model and solution technique

This study presents a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open-pit mining. The proposed model aims to obtain the optimum extraction sequences of the original-size (smallest) blocks over short time intervals and in the presence of real-life constraints, including precedence relationship, machine capacity, grade requirements, processing demands and stockpile management. A hybrid branch-and-bound and simulated annealing algorithm is developed to solve the problem. Computational experiments show that the proposed methodology is a promising way to provide quantitative recommendations for mine planning and scheduling engineers.

Mathematical modelling of the refractive index reconstruction through global optimization

We examine a mathematical model of non-destructive testing of planar waveguides, based on numerical solution of a nonlinear integral equation. Such problem is ill-posed, and the method of Tikhonov regularization is applied. To minimize Tikhonov functional, and find the parameters of the waveguide, we use two new optimization methods: the cutting angle method of global optimization, and the discrete gradient method of nonsmooth local optimization. We examine how the noise in the experimental data influences the solution, and how the regularization parameter has to be chosen. We show that even with significant noise in the data, the numerical solution is of high accuracy, and the method can be used to process real experimental da.ta..

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model

Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.