Luigi Rarità

An Upwind-Euler Scheme for an ODE-PDE Model of Supply Chains

Numerical techniques for the simulation of an ODE-PDE model for supply chains are presented. First, we describe a scheme based on upwind and explicit Euler methods, then provide corrections to maintain positivity of solutions, and finally, prove convergence and provide a convergence rate. The latter is achieved via comparison with wave front tracking solutions and the use of generalized tangent vectors. Different choices of time and space meshes give similar results, both for CPU times and numerical errors. Fast algorithms, based on an accurate choice of time and space meshes and data structures, are then proposed, achieving high computational gains.

Optimal distribution of traffic flows in emergency cases

The aim of this work is to present a technique for the optimisation of emergency vehicles travel times on assigned paths when critical situations, such as car accidents, occur. Using a fluid-dynamic model for the description of car density evolution, the attention is focused on a decentralised approach reducing to simple junctions with two incoming roads and two outgoing ones (junctions of 2 × 2 type). We assume the redirection of cars at junctions is possible and choose a cost functional that describes the asymptotic average velocity of emergency vehicles. Fixing an incoming road and an outgoing road for the emergency vehicle, we determine the local distribution coefficients that maximise such functional at a single junction. Then we use the local optimal coefficients at each node of the network. The overall traffic evolution is studied via simulations, both for simple junctions or cascade networks, evaluating global performances when optimal parameters on the network are used.

A Knowledge Management Strategy to Identify an Expert in Enterprise

The aim of this paper is to define a strategy to identify, manage and take advantage of competences in the enterprise via figures of opportune experts, with consequent advantages for workers and users in terms of problem solving. In such a context, industrial aspects, such as resources localization, research time and accessibility to the organizational hierarchy and the work load, are also considered. This allows to distinguish three different phases in finding the experts: Initialization, in which a score is assigned to workers on the base of competence levels; Propagation, where the search accuracy is improved using trust and closeness measures; Localization, where updates of scores are made in terms of social and geographical positions of users/enterprises and experts. The three phases allows to identify inside an enterprise the expert, who has the best competence and is close to the resource, that is in the shortest delay possible.

Splitting of Traffic Flows to Control Congestion in Special Events

We deal with the optimization of traffic flows distribution at road junctions with an incoming road and two outgoing ones, in order to manage special events which determine congestion phenomena. Using a fluid-dynamic model for the description of the car densities evolution, the attention is focused on a decentralized approach. Two cost functionals, measuring the kinetic energy and the average travelling times, weighted with the number of cars moving on roads, are considered. The first one is maximized with respect to the distribution coefficient, and the second is minimized with respect to the same control parameter. The obtained results have been tested by simulations of urban networks. Decongestion effects are also confirmed estimating the time a car needs to cross a fixed route on the network.

Fitted Q-iteration and functional networks for ubiquitous recommender systems

Ubiquitous recommender systems facilitate users on-location by personalized recommendations of items in the proximity via mobile devices. Due to a high variability of situations and preferences, an efficient resource processing is needed in order to assist the user in a proper way. In this paper, we consider a recommender system, able to learn preferences/habits of users through contextual information, such as location and time, using a new offline model-free approximate Q-iteration. Following the basic idea of Fitted Q-Iteration, the paper focuses on a computational scheme, based on functional networks, and that, unlike the well-known neural ones, does not require a large number of training samples. A preliminary case study, which deals with a shopping mall, is useful to show the approximation capabilities of the proposed approach.

Optimal paths on urban networks using travelling times prevision

We deal with an algorithm that, once origin and destination are fixed, individuates the route that permits to reach the destination in the shortest time, respecting an assigned maximal travel time, and with risks measure below a given threshold. A fluid dynamic model for road networks, according to initial car densities on roads and traffic coefficients at junctions, forecasts the future traffic evolution, giving dynamical weights to a constrained K shortest path algorithm. Simulations are performed on a case study to test the efficiency of the proposed procedure.

A Queueing Networks-Based Model for Supply Systems

In this paper, a stochastic approach, based on queueing networks, is analyzed in order to model a supply system, whose nodes are working stations. Unfinished goods and control electrical signals arrive, following Poisson processes, at the nodes. When the working processes at nodes end, according to fixed probabilities, goods can leave the network or move to other nodes as either parts to process or control signals. On the other hand, control signals are activated during a random exponentially distributed time and they act on unfinished parts: precisely, with assigned probabilities, control impulses can move goods between nodes, or destroy them. For the just described queueing network, the stationary state probabilities are found in product form. A numerical algorithm allows to study the steady state probabilities, the mean number of unfinished goods and the stability of the whole network.

Kernel of a DSS for the Evaluation of the Founding Team of a University—Based Spin Off

This work focuses on a University-based Spin Off (USO) with consequent review of the traditional university mission on the need/opportunity to give more to Knowledge. Europe, Italian government and other institutions fund spin-offs through announcements, in which the evaluation method for merits still shows limitations, due to a light presence of Italian excellence. The work, starting from the Service Science Management Engineering and Design (SSMD+D), proposes a review of the literature about demographic factors in the founding team, but more generally of human capital in spin-offs in order to create value. According to Visintin and Pittino (see Visintin and Pittino in Technovation 34: 31–43, 2014), the paper aims to design a kernel of a fuzzy logic based DSS to evaluate ex-ante the likely success of the founding team of a USO. Academic spin-offs of the University of Salerno are useful to test the DSS.

Regulation of Traffic Lights at Road Junctions

In this work, we aim to investigate the effects of traffic lights regulation at road junctions, modelled by a fluid dynamic approach. Numerical simulations prove that it is possible to plan some optimization strategies for green and red phases for networks consisting of more nodes.