R. Mansini
University of Brescia, Department of Information engineering, Faculty Member
Research Interests:
IFORS 2002. OR in a globalized, networked world economy
Proceedings EWGFM XX
The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and, simultaneously, minimizing the tracking... more
The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and, simultaneously, minimizing the tracking error. The EITP is capturing a growing attention among academics, both for its practical relevance and for the scientific challenges that its study, as a multi-objective problem, poses. Several optimization models have been proposed in the literature, where the tracking error is measured in terms of standard deviation or in linear form using, for instance, the mean absolute deviation. More recently, reward-risk optimization measures, like the Omega ratio, have been adopted for the EITP. On the other side, shortfall or quantile risk measures have nowadays gained an established popularity in a variety of financial applications. In this paper, we propose a class of bi-criteria optimization models for the EITP, where risk is measured using the Weighted...
Research Interests:
Research Interests:
Research Interests:
The information needed to solve a vehicle routing problem may be not completely known ahead of time. Rather it may be dynamically revealed as time goes on. We consider a dynamic vehicle routing problem faced by a courier company where... more
The information needed to solve a vehicle routing problem may be not completely known ahead of time. Rather it may be dynamically revealed as time goes on. We consider a dynamic vehicle routing problem faced by a courier company where customer requests with service time windows arrive and have to be serviced on real time by a fleet of vehicles in movement. Differently from other dynamic routing problems motivated by the same courier service, we consider both pick-up and delivery requests and assume that customer requests cannot be refused but can be postponed to future shifts. A heuristic algorithm based on local search is proposed for the problem together with an illustrative example. Experimental analysis is in progress.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
... realizations are representative of future outcomes, to more complex methods based on randomly re-sampling from historical data (Bootstrapping methods) or on randomly sampling from a chosen distribution function of the multivariate... more
... realizations are representative of future outcomes, to more complex methods based on randomly re-sampling from historical data (Bootstrapping methods) or on randomly sampling from a chosen distribution function of the multivariate random variable (Monte Carlo simulation ...
Research Interests:
Research Interests:
Research Interests:
We study the problem of scheduling groups of tasks with precedence constraints on three dedicated processors. Each task requires a specified set of processors. Up to three precedence constraints are considered among groups of tasks... more
We study the problem of scheduling groups of tasks with precedence constraints on three dedicated processors. Each task requires a specified set of processors. Up to three precedence constraints are considered among groups of tasks requiring the same set of processors. The objective of the problem is to find a nonpreemptive schedule which minimizes the maximum completion time (makespan). This
Research Interests:
Recently, the interest in the use of mathematical models for solving urgent public problems has increased. The scope of this paper is to investigate a linear programming model to reduce the costs in the organization of municipal refuse... more
Recently, the interest in the use of mathematical models for solving urgent public problems has increased. The scope of this paper is to investigate a linear programming model to reduce the costs in the organization of municipal refuse collection, by minimizing the maximum amount of refuse collected in a day. It is our intention to give a contribution to improve the efficiency of the decisions taken at a tactical level in the complex refuse disposal framework. Particularly, we point out the additional benefits, always with respect to service efficiency, that the introduction of a separate refuse collection can imply.A public system is faced with a large amount of practical issues which have to be solved to make the services offered to the citizens more efficient. This paper deals with the common problem of costs reduction in the organization of the municipal household refuse collection. First a linear model for the standard problem is applied to the data of the city of Brescia. Then a generalization of the model based on the modern concept of separate refuse collection is presented and examples obtained on data for the city of Brescia are given, assuming the separate collection of two and three different types of refuse. In both cases the solutions of the models give an insight for a more efficient management of the refuse service.
Research Interests:
ABSTRACT Given a set of products each with positive discrete demand, and a set of markets selling products at given prices, the traveling purchaser problem (TPP) looks for a tour visiting a subset of markets such that products demand is... more
ABSTRACT Given a set of products each with positive discrete demand, and a set of markets selling products at given prices, the traveling purchaser problem (TPP) looks for a tour visiting a subset of markets such that products demand is satisfied at minimum purchasing and traveling costs. In this paper we analyze a dynamic variant of the problem, where quantities may decrease as time goes on. Complete information is assumed on current state of the world, i.e.decision maker knows quantities available for each product in each market at present time and is informed about any consumption event when it occurs. Nevertheless, planner does not have any information on future events. Two groups of heuristics are described and compared. The first group consists of simplified approaches deciding which market to visit next on the basis of some greedy criteria considering only one of the two objective costs. The second one includes heuristics based on a look-ahead approach taking into account both traveling and purchasing costs and inserting some future prediction. Heuristics behavior has been tested on a large set of randomly generated instances under different levels of dynamism.
Research Interests:
Research Interests:
Research Interests:
Research Interests: Computational Economics, Econometrics, Modeling and Simulation, Risk Management, Portfolio Optimization, and 10 moreFinancial Institutions, Theory and Practice, Historical Data, STOCK EXCHANGE, Investment Strategies, Conditional Value at Risk, Mean-variance analysis, Portfolio analysis, Finance and Investment Banking Area, and Finance and Investment Banking
Research Interests:
all copies, and that the name of ICCE not be used in advertising or publicity pertaining to this document without specific, written prior permission. ICCE makes no representations about the suitability of this document for any purpose. It... more
all copies, and that the name of ICCE not be used in advertising or publicity pertaining to this document without specific, written prior permission. ICCE makes no representations about the suitability of this document for any purpose. It is provided “as is ” without express or implied warranty.
Research Interests:
The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and, simultaneously, minimizing the tracking... more
The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and, simultaneously, minimizing the tracking error. The EITP is capturing a growing attention among academics, both for its practical relevance and for the scientific challenges that its study, as a multi-objective problem, poses. Several optimization models have been proposed in the literature, where the tracking error is measured in terms of standard deviation or in linear form using, for instance, the mean absolute deviation. More recently, reward-risk optimization measures, like the Omega ratio, have been adopted for the EITP. On the other side, shortfall or quantile risk measures have nowadays gained an established popularity in a variety of financial applications. In this paper, we propose a class of bi-criteria optimization models for the EITP, where risk is measured using the Weighted...
Research Interests:
Research Interests:
ABSTRACT The Team Orienteering Problem (TOP) is a known NP-hard problem that typically arises in vehicle routing and production scheduling contexts. In this paper we introduce a new solution method to solve the TOP with hard Time Window... more
ABSTRACT The Team Orienteering Problem (TOP) is a known NP-hard problem that typically arises in vehicle routing and production scheduling contexts. In this paper we introduce a new solution method to solve the TOP with hard Time Window constraints (TOPTW). We propose a Variable Neighborhood Search (VNS) procedure based on the idea of exploring, most of the time, granular instead of complete neighborhoods in order to improve the algorithm’s efficiency without loosing effectiveness. The method provides a general way to deal with granularity for those routing problems based on profits and complicated by time constraints. Extensive computational results are reported on standard benchmark instances. Performance of the proposed algorithm is compared to optimal solution values, when available, or to best known solution values obtained by state-of-the-art algorithms. The method comes out to be, on average, quite effective allowing to improve the best know values for 25 test instances.