The demand responsive transport systems (DRTS) aim to satisfy two main objectives: the service flexibility and the costs minimization. They are a good solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests. DRTS may operate according to a static or dynamic mode. The aim of this work is to test on a real case a heuristic for a flexible transport system with different service parameters: fleet size, vehicle capacity, time windows and incoming requests.

In this paper we deal with a semilinear hyperbolic chemotaxis model in one space dimension evolving on a network, with suitable transmission conditions at nodes. This framework is motivated by tissue-engineering scaffolds used for improving wound healing. We introduce a numerical scheme, which guarantees global mass densities conservation. Moreover our scheme is able to yield a correct approximation of the effects of the source term at equilibrium. Several numerical tests are presented to show the behavior of solutions and to discuss the stability and the accuracy of our approximation.

In this paper we introduce a simulation algorithm based on fluid dynamic models to reproduce the behavior of traffic in a portion of the urban network in Rome. Numerical results, obtained comparing experimental data with numerical solutions, show the effectiveness of our approximation. (c) 2009 Elsevier Inc. All rights reserved.

One of the most important problems in the coordination of the entire supply chain comes from the fact that the whole system, working on the basis of a future prediction, is strongly affected by unexpected changes in external demand and even small changes can lead to huge distortions in the management of supply to higher levels. This phenomenon is called "Bullwhip Effect". The study carried out has the purpose to analyze the occurrence of Bullwhip Effect varying the parameters of demand, but also to quantify it through a discrete event simulation model.

The concept of innovation in transport systems requires the satisfaction of two main objectives: the service flexibility and the costs minimization. The demand responsive transport systems (DRTS) seem to be the solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests, respecting the limited capacity of the fleet and time constraints (hard time windows) for each network's node, and the service time of the system.

Bioventing is an in site remediation technique, which is useful for decontaminating polluted subsoil. Air is injected into the subsoil to enhance the bacteria biodegradation activity. A multiphase mathematical model describing the removal of hydrocarbon in the unsaturated zone will be described and the problem of the optimal design of a decontamination intervention will be formulated. In order to simplify the computational approach to the problem, a conjecture will be introduced, affirming that control of the subsoil airflow field allows the pollutant removal phenomenon to be controlled.

In this paper, a semi-implicit finite difference method for the 2-D shallow water equations is derived and applied. A characteristic analysis of the governing equations indicates those terms to be discretised implicitly so that the stability of the method will not depend on the celerity. Such terms are the gradient of the water surface elevation in the momentum equations, and the velocity divergence in the continuity equation. The convective terms are discretised explicitly by using either an upwind or an Eulerian-Lagrangian formula.

Reaction-diffusion systems with cross-diffusion are analyzed here for modeling the population dynamics of epidemic systems. In this paper specific attention is devoted to the numerical analysis and simulation of such systems to show that, far from possible pathologies, the qualitative behaviour of the systems may well interpret the dynamics of real systems.

The convergence quality of the cross-entropy (CE) optimizer relies critically on the mechanism meant for randomly generating data samples, in agreement with the inference drawn in the earlier works--the fast simulated annealing (FSA) and fast evolutionary programming (FEP). Since tracing a near-global-optimum embedded on a nonconvex search space can be viewed as a rare event problem, a CE algorithm constructed using a longtailed distribution is intuitively attractive for effectively exploring the optimization landscape.