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## Publications

Publications (72)

The term Monte Carlo is typically associated with the process of modeling and simulating a system affected by randomness: Several random scenarios are generated, and relevant statistics are gathered in order to assess, e.g., the performance of a decision policy or the value of an asset. Stated as such, it sounds like a fairly easy task from a conce...

The energy coming from the motion of the waves of seas and oceans could be an important component in the solution of the energy problem related to the pursuit of alternatives to fossil fuels. However, wave energy is still technologically immature and it has not reached the economic feasibility required for economy of scale. One of the major technol...

The energy coming from the motion of the waves of seas and oceans could be an important component in the solution of the energy problem related to the pursuit of alternatives to fossil fuels. However, wave energy is still technologically immature and it has not reached the economic feasibility required for economy of scale. One of the major technol...

The interdisciplinary relationship between machine learning and financial markets has long been a theme of great interest among both research communities. Recently, reinforcement learning and deep learning methods gained prominence in the active asset trading task, aiming to achieve outstanding performances compared with classical benchmarks, such...

This paper introduces a model-free control strategy aimed at maximizing the power absorbed by a Pendulum Wave Energy Converter (PeWEC). This control strategy is based on the development of a metamodel and on the optimization of the control action through it. The metamodel is built only from the collected data by linking the applied control action w...

We consider a simple assembly to order problem, where components must be manufactured under demand uncertainty and end items are assembled only after demand is realized. The problem can be naturally cast as a two-stage stochastic linear program with recourse, and possibly generalized to multiple stages. We investigate the two-stage case not only be...

The numerical methods for stochastic dynamic programming that we have discussed in Chap. 6 are certainly useful tools for tackling some dynamic optimization problems under uncertainty. However, they are not a radical antidote against the curses of DP.

We consider an inventory control problem for quickly perishable items, such as fresh produce, at the retail store level, assuming a fixed shelf life. Demand is affected by both uncertainty and seasonality within the week, as sales feature a peak close to weekends. Another complicating factor is customer behavior and inventory issuing: In the case o...

We use a fairly general framework to analyze a rich variety of financial optimization models presented in the literature, with emphasis on contributions included in this volume and a related special issue of OR Spectrum. We do not aim at providing readers with an exhaustive survey, rather we focus on a limited but significant set of modeling and me...

The scope of this volume is primarily to analyze from different methodological perspectives similar valuation and optimization problems arising in financial applications, aimed at facilitating a theoretical and computational integration between methods largely regarded as alternatives. Increasingly in recent years, financial management problems suc...

An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics. Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo meth...

Classical Quadrature Formulas Gaussian Quadrature Extension to Higher Dimensions: Product Rules Alternative Approaches for High-Dimensional Integration Relationship with Moment Matching Numerical Integration in R

The paper deals with an MILP model to schedule satellite launches with alternative launchers and different mission profiles, subject to resource constraints. The model is part of a simulation tool developed within a joint research project with the European space agency. The focus is on geostationary transfer orbit (GTO) launches of payloads, which...

In this article, we touch on a few selected topics illustrating the interplay between computational statistics and numerical analysis. Rather than briefly reviewing a long array of methods, on one hand we place emphasis on problems, such as stochastic optimization, which require the joint application of several techniques; on the other hand, we str...

An accessible introduction to the essential quantitative methods for making valuable business decisions. Quantitative methods-research techniques used to analyze quantitative data-enable professionals to organize and understand numbers and, in turn, to make good decisions. Quantitative Methods: An Introduction for Business Management presents the a...

This chapter starts with a motivating example from inventory control theory, the economic order quantity (EOQ) model. Then, it provides readers with a little background on numbers, intervals, and permutations. Functions, the core business of calculus, are introduced in the chapter, followed by a discussion on continuous functions, building function...

This chapter begins with a classification of decision models; it draws the line between linear and nonlinear programming models, as well as between convex and nonconvex optimization problems. This classification has a quite practical purpose, as it is related to solution methods available to solve each class of problems. Then, the chapter describes...

This chapter describes the simple linear regression concepts. The first quite natural idea is building a linear regression model involving more than one regressor. Finding the parameters by ordinary least squares (OLS) is a rather straightforward exercise. What is much less straightforward is the statistical side of the coin, since the presence of...

This chapter first motivates the need for data reduction; this is often a preliminary step to make the application of other quantitative methods possible. Principal component analysis (PCA) is a nice illustration of the role played by linear algebra in multivariate statistics. The chapter illustrates factor analysis, which shares some of the techni...

This chapter illustrates a few toy examples that hopefully provide a person with enough motivation to proceed. The author has emphasized the role of data to make decisions. If one knew all of the relevant data in advance, then the task would be considerably simplified. Nevertheless, the chapter shows that even in such an ideal situation some quanti...

This chapter introduces density functions, where the author sees that the concept of cumulative distribution function (CDF) needs no adjustment when moving from discrete to continuous random variables. It explains how expected values and variances are applied in this context. Then, the chapter also talks about the distribution of random variables b...

This chapter first introduces the formal concepts of joint and marginal distributions. Then, it summarizes properties of independent random variables. The chapter also characterizes the interdependence between two random variables in terms of covariance and correlation. Further, the chapter illustrates the role of these concepts in risk management,...

This chapter first introduces the axiomatic approach to probability theory, laying down the fundamental concepts of events and probability measures, along with a set of basic rules of the game in order to work with probabilities in a sensible and consistent manner. Then, it introduces conditional probabilities in a mathematically unsophisticated wa...

Multivariate analysis is the more-or-less natural extension of elementary inferential statistics to the case of multidimensional data. The first difficulty the person encounters is the representation of data. How can he visualize data in multiple dimensions, on the basis of our limited ability to plot bidimensional and tridimensional diagrams? This...

In this case study we describe an ongoing effort by Piedmont Region to foster MICE tourism in Turin and surrounding areas. This seems one of the promising ways to overcome the traditional image of an industrial city dominated by car industry. We set this effort within a more general framework, describing first results obtained by a pilot study and...

Commercial software packages for production management are characterized by a gap between MRP logic, based on a backward scheduling approach, and finite capacity scheduling, usually based on forward scheduling. In order to partially bridge that gap, we need scheduling algorithms able to meet due dates while keeping WIP and inventory costs low. This...

Ordering perishable items for sale at a retail store is a difficult problem, when demand is subject to predictable and unpredictable variability due, e.g., to seasonality within the week and to significant uncertainty. Ordering fresh produce with a long shelf life is a way to avoid scrapping stuff, while ensuring suitable service level, but this ha...

One of the most critical issues in wireless sensor networks is represented by the limited availability of energy on network nodes; thus, making good use of energy is necessary to increase network lifetime. In this paper, we define network lifetime as the time spanning from the instant when the network starts functioning properly, i.e., satisfying t...

IntroductionThe Variable to be PredictedMetrics for Forecast ErrorsA Classification of Forecasting Methods
Moving AverageSimple Exponential SmoothingExponential Smoothing with TrendExponential Smoothing with SeasonalitySmoothing with Seasonality and TrendSimple Linear RegressionForecasting Models Based on Multiple RegressionForecasting Demand for N...

Unique introduction to distribution logistics that focuses on both quantitative modeling and practical business issues. Introduction to Distribution Logistics presents a complete and balanced treatment of distribution logistics by covering both applications and the required theoretical background, therefore extending its reach to practitioners and...

We consider a stochastic version of the classical multi-item Capacitated Lot-Sizing Problem (CLSP). Demand uncertainty is explicitly modeled through a scenario tree, resulting in a multi-stage mixed-integer stochastic programming model with recourse. We propose a plant-location-based model formulation and a heuristic solution approach based on a fi...

Introduction and classification of PDEs Numerical solution by finite difference methods Explicit and implicit methods for the heat equation Solving the bidimensional heat equation Convergence, consistency, and stability For further reading Reference

Sample space, events, and probability Random variables, expectation, and variance Jointly distributed random variables Independence, covariance, and conditional expectation Parameter estimation Linear regression For further reading Reference

Mixed-integer programming models Fixed-mix model based on global optimization Branch and bound methods for non-convex optimization Heuristic methods for non-convex optimization For further reading Reference

MATLAB environment MATLAB graphics MATLAB programming

Running optimization models in AMPL Mean variance efficient portfolios in AMPL The knapsack model in AMPL Cash flow matching For further reading Reference

Applying finite difference methods to the Black-Scholes equation Pricing a vanilla European option by an explicit method Pricing a vanilla European option by a fully implicit method Pricing a barrier option by the Crank-Nicolson method Dealing with American options For further reading Reference

Stochastic programming models have been proposed for capacity planning problems in different environments, including energy, telecommunication networks, distribution networks, and manufacturing systems. In this chapter we give an introductory tutorial to stochastic linear programming models, with emphasis on modeling techniques, rather than special...

One of the most critical issues in wireless sensor networks is represented by the limited availability of energy within network nodes; thus, making good use of energy is a must to increase network lifetime. We define as network lifetime the period from the time instant when the network starts functioning till the network runs satisfying its quality...

One of the most critical issues in wireless sensor networks is represented by the limited availability of energy within network nodes; thus, making good use of energy is a must to increase network lifetime. We define as network lifetime the period from the time instant when the network starts functioning till the network runs, satisfying its qualit...

We consider here the application of trivial LP-based rounding heuristics to the capacitated lot-sizing problem (CLSP). The motivation behind the use of LP-based heuristics is that their extension to cope with complicating features (to be expected, for example, when dealing with a master production scheduling problem within a MRP system) is generall...

The importance of finite-capacity schedulers is increasing, with respect to the widespread MRP packages, due to their ability to model the shop floor more accurately. However, this very advantage may turn into a disadvantage, since it is quite difficult to devise a high-quality general purpose scheduler able to cope with the technological peculiari...

The importance of finite-capacity schedulers is increasing, with respect to the widespread MRP packages, due to their ability to model the shop floor more accurately. However, this very advantage may turn into a disadvantage, since it is quite difficult to devise a high-quality general purpose scheduler able to cope with the technological peculiari...

When solving a product/process design problem, we must exploit the available degrees of freedom to cope with a variety of issues. Alternative process plans can be generated for a given product, and choosing one of them has implications on manufacturing functions downstream, including planning/scheduling. Flexible process plans can be exploited in r...

Even a quick glimpse at the literature on modeling in manufacturing systems shows the diversity of approaches that have been adopted. Models may be classified along many dimensions [4].

Advanced modeling techniques are a necessary tool in order to design and manage manufacturing systems effectively. This book contains a set of tutorial chapters on topics ranging from aggregate production planning to real time control, including predictive and reactive scheduling, flow management in assembly systems, simulation of robotic cells, de...

Master Production Scheduling is a cornerstone of MRP II systems, since it represents the link between marketing and production. To overcome the limitations of MRP II systems, we aim at finding a good and realistic MPS by optimization techniques, rather than by a trial-and-error manual procedure. Due to the complexity of the resulting mathematical m...

Owing to the increasing role of integrated supply chain design and management, the traditional task of manufacturing systems design is becoming more complex. The variety of tasks involved in such an activity, both at the level of structural network design and management, calls for a modular approach, enabling the designer to experiment with differe...

Manufacturing systems are often characterized by the use of resources which need to be 'renewed' after a period of use, such as tools and dies. The resources are not available during their maintenance and if it is too expensive to purchase a suitable number of copies, this may introduce delays and lower the quality of the schedule. We refer to such...

This paper deals with the joint part/tool scheduling problem in a flexible manufacturing cell consisting of two machines. Each job requires a set of tools for the execution of a sequence of operations. The tools are stored in a shared tool magazine and are moved throughout the cell by means of a tool handling system; a conflict may arise when the t...

Production scheduling approaches in discrete manufacturing environments must cope with discrete material flows subject to different constraints in order to obtain a good solution. Despite the huge amount of literature on machine scheduling, most commercial schedulers take a myopic approach based on priority rules. Among the reasons behind this gap...

When considering the calibration of a multicomponent robotic dynamometer, metrological requirements as well as operating constraints must be considered, in particular the time needed to perform the calibration task. Considered here is calibration by a special-purpose rotating platform and the problems of (1) reducing the number of setups needed to...

In this paper we propose approximate dynamic models, based on continuous material flows, in order to overcome the difficulties usually faced by classical machine scheduling methods, such as computational complexity, limited ability to model all the features of real manufacturing environments and to cope with unpredictable events. The continuous flo...

CIM systems modelling is a very complex task which can be referred to various phases of system development. Modelling is both dispersed in time and in scope, since it is generally carried out on different aspects of the system, and by a set of people whose expertise ranges on different fields. Indeed, Manufacturing Engineers exploit modelling appro...

The job shop scheduling literature deals with problems characterized by a fixed linear process plan for each job: it is assumed that the process planning problem has been solved before scheduling, and no flexibility in the process plan is considered. Our aim here is to propose a solution approach for a joint process plan selection and job shop sche...

Several heterogenous processes can be found in industrial practice, such as plants involving partly continuous, partly discrete manufacturing and companies composed by departments managed by different production planning philosophies. An efficient management of heterogeneity calls for decentralization. This paper addresses a distributed approach to...

A hierarchical algorithm for the flexible job shop scheduling problem is described, based on the tabu search metaheuristic. Hierarchical strategies have been proposed in the literature for complex scheduling problems, and the tabu search metaheuristic, being able to cope with different memory levels, provides a natural background for the developmen...

Neighbourhood search is one of the general strategies used in designing heuristic algorithms for discrete optimization. Apart from its simplicity from the conceptual and implementation point of view, a notable characteristic of neighbourhood search is its generality: no assumption is made about the objective and the constraints, whereas other heuri...

Some experiments on the integration of algorithmic techniques with
knowledge-based ones are discussed. Two case studies are presented: an
FMS cell and a press shop. It was found that the algorithmic procedures
developed for production scheduling resulted in limiting the ability to
cope with the complexity of the real manufacturing world. The schedu...

The purpose of the paper is to present preliminary results concerning the implementation of a rule based supervisory module for adaptive control algorithms in the framework of Expert Control Theory.

This paper presents the outline of an environment for the specification and prototyping of manufacturing systems software, which is under development in the framework of a research project sponsored by Italian National Research Council.

The purpose of the paper is to present preliminary results concerning the implementation of a rule based supervisory module for adaptive control algorithms in the framework of Expert Control Theory

The integration of algorithmic and heuristic methods has been
investigated in different manufacturing environments. The algorithmic
procedures developed for production scheduling results were limited in
their ability to cope with the complexity of real-world manufacturing.
The scheduling problem, seen as a constraints-satisfaction problem, can
be a...