A two-objective optimization of ship itineraries for a cruise company

This paper deals with the problem of cruise itinerary planning which plays a central role in worldwide cruise ship tourism. In particular, the Day-by-day Cruise Itinerary Optimization (DCIO) problem is considered. Assuming that a cruise has been planned in terms of homeports and journey duration, the DCIO problem consists in determining the daily schedule of each itinerary so that some Key Performance Indicators are optimized. The schedule of an itinerary, i.e. the sequence of visited ports, the arrival and departure time at each port, greatly affect cruise operative costs and attractiveness. We propose a Mixed Integer Linear Programming (MILP) formulation of the problem with the objective of minimizing the itinerary cost due to fuel and port costs, while maximizing an itinerary attractiveness index. This latter is strongly related to the ports visited as well as to the overall schedule of the itinerary. Therefore the problem turns out to be a bi-objective optimization problem. We provide its solution in terms of Pareto optimal solution points. Each single objective MILP problem which arises is solved by using an exact algorithm,i mplemented in a commercial solver. We consider the day-by-day itineraries of a major luxury cruise company in many geographical areas all over the world. Here we report, as illustrative examples, the results obtained on some of these real instances

Socio Onorario

Socio onorario dell'AIRO per l'apporto di cospicuo rilievo dato all'Associazion

La Matematica, strumento formativo e strumento operativo

Contributo al Convegno su "Valore Educativo e Culturale della Matematica", Accademia Navale di Livorno, 23-24 Marzo 2006

A new augmented Lagrangian function for inequality constraints in nonlinear programming problems

In this paper, a new augmented Lagrangian function is introduced for solving nonlinear programming problems with inequality constraints. The relevant feature of the proposed approach is that, under suitable assumptions, it enables one to obtain the solution of the constrained problem by a single unconstrained minimization of a continuously differentiable function, so that standard unconstrained minimization techniques can be employed. Numerical examples are reported. © 1982 Plenum Publishing Corporation

Nonlinear Optimization, Variational Inequalities and Equilibrium Problems

The Workshop aims to review and discuss recent advances in the development of analytical and computational tools for Nonlinear Optimization, Variational inequalities, Equilibrium Problems and to provide a forum for fruitful interactions in strictly related fields of research. It is the 52th Workshop of the International School of Mathematics, the sixth on Nonlinear Optimization. The preceding Workshops on Nonlinear Optimization have been held in 1995, 1998, 2001, 2004 and 200

Problemi di Modellistica e Controllo nella Gestione delle Risorse Idriche

Atti delle Giornate di Studio su "Problemi di Modellistica e Controllo nella Gestione delle Risorse Idriche", Universita' della Calabria, Arcavacata, 24-27 Marzo 197

Guest Editorial - Erice 2007 Nonlinear Optimization - Special Issue of COMPUTATIONAL OPTIMIZATION AND APPLICATIONS

An edited selection of papers presented at the Workshop on "New Problems and Innovative Applications of Nonlinear Optimization", July 31-August 09 2007, Erice, Ital

Application of the Epsilon Technique to the Identification of Distributed Parameter Systems

The aim of this paper is to present and discuss the application of the epsilon technique to the identification of distributed-parameter systems. After the problem formulation, attention is focused on computational aspects. The numerical results obtained for two typical systems are presented

A new class of augmented Lagrangians in nonlinear programming

In this paper a new class of augmented Lagrangians is introduced, for solving equality constrained problems via unconstrained minimization techniques. It is proved that a solution of the constrained problem and the corresponding values of the Lagrange multipliers can be found by performing a single unconstrained minimization of the augmented Lagrangian. In particular, in the linear quadratic case, the solution is obtained by minimizing a quadratic function. Numerical examples are reported