Determining departure times in dynamic and stochastic maritime routing and scheduling problem

In maritime transportation, decisions are made in a dynamic setting where many aspects of the future are uncertain. However, most academic literature on maritime transportation considers static and deterministic routing and scheduling problems. This work addresses a gap in the literature on dynamic and stochastic maritime routing and scheduling problems, by focusing on the scheduling of departure times. Five simple strategies for setting departure times are considered, as well as a more advanced strategy which involves solving a mixed integer mathematical programming problem. The latter strategy is significantly better than the other methods, while adding only a small computational effort.

Improved solutions to dynamic and stochastic maritime pick-up and delivery problems using local search.

Heuristics for stochastic and dynamic vehicle routing problems are often kept relatively simple, in part due to the high computational burden resulting from having to consider stochastic information in some form. In this work, three existing heuristics are extended by three different local search variations: a first improvement descent using stochastic information, a tabu search using stochastic information when updating the incumbent solution, and a tabu search using stochastic information when selecting moves based on a list of moves determined through a proxy evaluation. In particular, the three local search variations are designed to utilize stochastic information in the form of sampled scenarios. The results indicate that adding local search using stochastic information to the existing heuristics can further reduce operating costs for shipping companies by 0.5–2 %. While the existing heuristics could produce structurally different solutions even when using similar stochastic information in the search, the appended local search methods seem able to make the final solutions more similar in structure.

Estimating and forecasting generalized fractional Long memory stochastic volatility models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

The SEIQS stochastic epidemic model with external source of infection

This paper deals with a stochastic epidemic model for computer viruses with latent and quarantine periods, and two sources of infection: internal and external. All sojourn times are considered random variables which are assumed to be independent and exponentially distributed. For this model extinction and hazard times are analyzed, giving results for their Laplace transforms and moments. The transient behavior is considered by studying the number of times that computers are susceptible, exposed, infectious and quarantined during a period of time (0, t] and results for their joint and marginal distributions, moments and cross moments are presented. In order to give light this analysis, some numerical examples are showed.

Variation in the expression of migratory activity in blackcap (Sylvia atricapilla); effects of the origin, environmental conditions, dominance and personalities

La migración es una respuesta a cambios estacionales del clima generando desplazamientos periódicos entre hábitats de cría y de invernada, permitiendo así el uso temporal de los recursos disponibles. La migración implica unos costes energéticos muy elevados, un aumento de la depredación potencial, variaciones ambientales y una disponibilidad de alimento impredecible a lo largo de la ruta migratoria; por lo que es una de las actividades más desafiantes de su ciclo vital. A pesar de ello, los beneficios de la migración compensan sus costes. La migración está programada genéticamente, siendo relativamente constante en su momento, distancia y dirección. Por otro lado, ambiente juega un papel predominante en algunas poblaciones, pudiendo modificar el comportamiento migratorio de una estrategia parcial o facultativa a un modo de vida sedentario. Con el fin de describir el origen y evolución del comportamiento migratorio en aves, se ha propuesto un “modelo de umbral” genético para determinar si un ave es migrante o sedentaria. Dentro de una variable continua (p.ej. la concentración de proteínas u hormonas), este modelo asume que existe una actividad migratoria subyacente implicada en su expresión génica. Este umbral divide cada variable en categorías dicotómicas que definen el fenotipo de un individuo. Los ejemplares sin actividad migratoria muestran valores por debajo de este umbral, siendo clasificados como sedentarios, mientras que los ejemplares migrantes muestran valores por encima del umbral definido. Los cambios de estrategia vital no dependen únicamente de la posición del umbral determinado genéticamente sino también de las variables ambientales, por lo que dichas variaciones deben ser añadidas al modelo. Este modelo de umbral ambiental predice que el carácter migratorio de los individuos situados en los extremos de distribución no se encuentra afectado por los factores ambientales, mientras que aquellos más próximos al umbral pueden más fácilmente cambiar su estrategia migratoria...