arXiv subjects

Andrea Spinelli

Publications and source records attributed to Andrea Spinelli.

8 recordsLinked to original sources

arXiv2026

A novel L-shaped refinement chain cuts method for two-stage stochastic programs

This paper introduces the L-shaped refinement chain cuts method, a novel approach for solving two-stage stochastic programs. The proposed method integrates the refinement chain of scenarios within the classical L-shaped decomposition framework. In the proposed approach, the full scenario set is partitioned into subgroups at each level of the refinement chain, and one subproblem is solved for each subgroup rather than for each individual scenario as in the classical L-shaped method. The proposed framework generalizes both the classical multi-cut and single-cut L-shaped formulations. Theoretical convergence properties to the optimal solution of the original two-stage stochastic program are established for every refinement level. In addition, the relationships between consecutive refinement levels are characterized in terms of Benders cuts, leading to the development of an iterative refinement-based solution algorithm across consecutive levels of the refinement chain. The effectiveness of the proposed method is evaluated on a two-stage stochastic fixed-charge multicommodity network design problem under a mean-risk formulation. Computational experiments on benchmark instances demonstrate the promising performance of the proposed framework and highlight its applicability to large-scale risk-averse stochastic optimization problems.

math.OC↗

arXiv2025

A Stochastic Electric Vehicle Routing Problem under Uncertain Energy Consumption

The increasing adoption of Electric Vehicles (EVs) for service and goods distribution operations has led to the emergence of Electric Vehicle Routing Problems (EVRPs), a class of vehicle routing problems addressing the unique challenges posed by the limited driving range and recharging needs of EVs. While the majority of EVRP variants have considered deterministic energy consumption, this paper focuses on the Stochastic Electric Vehicle Routing Problem with a Threshold recourse policy (SEVRP-T), where the uncertainty in energy consumption is considered, and a recourse policy is employed to ensure that EVs recharge at Charging Stations (CSs) whenever their State of Charge (SoC) falls below a specified threshold. We formulate the SEVRP-T as a two-stage stochastic mixed-integer second-order cone model, where the first stage determines the sequences of customers to be visited, and the second stage incorporates charging activities. The objective is to minimize the expected total duration of the routes, composed by travel times and recharging operations. To cope with the computational complexity of the model, we propose a heuristic based on an Iterated Local Search (ILS) procedure coupled with a Set Partitioning problem. To further speed up the heuristic, we develop two lower bounds on the corresponding first-stage customer sequences. Furthermore, to handle a large number of energy consumption scenarios, we employ a scenario reduction technique. Extensive computational experiments are conducted to validate the effectiveness of the proposed solution strategy and to assess the importance of considering the stochastic nature of the energy consumption. The research presented in this paper contributes to the growing body of literature on EVRP and provides insights into managing the operational deployment of EVs in logistics activities under uncertainty.

math.OC↗

arXiv2025

A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra' inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional Statistical and Machine Learning (ML) techniques could not guarantee for accurate and reliable results. ML models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models. In this paper, we investigate the performance of a novel robust formulation for Support Vector Machine (SVM) in classifying COVID-19 samples obtained from Raman Spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we derive robust counterpart models of deterministic formulations using bounded-by-norm uncertainty sets around each observation. We explore the cases of both linear and kernel-induced classifiers to address binary and multiclass classification tasks. The effectiveness of our approach is validated on real-world COVID-19 datasets provided by Italian hospitals by comparing the results of our simulations with a state-of-the-art classifier.

q-bio.QM↗

arXiv2024

A rolling horizon heuristic approach for a multi-stage stochastic waste collection problem

In this paper we present a multi-stage stochastic optimization model to solve an inventory routing problem for recyclable waste collection. The objective is the maximization of the total expected profit of the waste collection company. The decisions are related to the selection of the bins to be visited and the corresponding routing plan in a predefined time horizon. Stochasticity in waste accumulation is modeled through scenario trees generated via conditional density estimation and dynamic stochastic approximation techniques. The proposed formulation is solved through a rolling horizon approach, providing a worst-case analysis on its performance. Extensive computational experiments are carried out on small- and large-sized instances based on real data provided by a large Portuguese waste collection company. The impact of stochasticity on waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Some managerial insights on different configurations of the instances are finally discussed.

math.OC↗

arXiv2023

A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification

In this paper, we introduce novel Twin Parametric Margin Support Vector Machine (TPMSVM) models designed to address multiclass classification tasks under feature uncertainty. To handle data perturbations, we construct bounded-by-norm uncertainty set around each training observation and derive the robust counterparts of the deterministic models using robust optimization techniques. To capture complex data structure, we explore both linear and kernel-induced classifiers, providing computationally tractable reformulations of the resulting robust models. Additionally, we propose two alternatives for the final decision function, enhancing models' flexibility. Finally, we validate the effectiveness of the proposed robust multiclass TPMSVM methodology on real-world datasets, showing the good performance of the approach in the presence of uncertainty.

cs.LG↗

arXiv2023

A Novel Robust Optimization Model for Nonlinear Support Vector Machine

In this paper, we present new optimization models for Support Vector Machine (SVM), with the aim of separating data points in two or more classes. The classification task is handled by means of nonlinear classifiers induced by kernel functions and consists in two consecutive phases: first, a classical SVM model is solved, followed by a linear search procedure, aimed at minimizing the total number of misclassified data points. To address the problem of data perturbations and protect the model against uncertainty, we construct bounded-by-norm uncertainty sets around each training data and apply robust optimization techniques. We rigorously derive the robust counterpart extension of the deterministic SVM approach, providing computationally tractable reformulations. Closed-form expressions for the bounds of the uncertainty sets in the feature space have been formulated for typically used kernel functions. Finally, extensive numerical results on real-world datasets show the benefits of the proposed robust approach in comparison with various SVM alternatives in the machine learning literature.

math.OC↗

arXiv2022

Pneumatic System for Pressure Probe Measurements in Transient Flows of Non-Ideal Vapors Subject to Line Condensation

This paper presents the design, construction and commissioning of a pneumatic system for pressure probe measurements in flows of organic vapors in non-ideal conditions, close to the liquid-vapor saturation curve and the critical point where the ideal gas law is not applicable. Experiments were performed with fluid siloxane MM, employed in medium/high temperature Organic Rankine Cycles, in the TROVA blow-down wind tunnel at Politecnico Milano. Challenges linked to condensation in pneumatic lines (vapor-liquid menisci, hydrostatic head, mass-sink), were evaluated with theoretical calculation and experiments, and were avoided with a nitrogen flushing procedure. Commissioning tests were performed with a Pitot tube in non-ideal subsonic flows at Mach numbers of 0.2 and 0.5. Measurement delay issues were identified and solved through a dynamic testing procedure. The correct performance of the complete system was verified for probe measurements of total, static and dynamic pressure in non-ideal flows of organic vapors.

physics.ins-det↗

arXiv2004

Nonadiabatic effects in the dynamics of atoms confined in a cylindric time-orbiting-potential magnetic trap

In a time-orbiting-potential magnetic trap the neutral atoms are confined by means of an inhomogeneous magnetic field superimposed to an uniform rotating one. We perform an analytic study of the atomic motion by taking into account the nonadiabatic effects arising from the spin dynamics about the local magnetic field. Geometric-like magnetic-fields determined by the Berry's phase appear within the quantum description. The application of a variational procedure on the original quantum equation leads to a set of dynamical evolution equations for the quantum average value of the position operator and of the spin variables. Within this approximation we derive the quantum-mechanical ground state configuration matching the classical adiabatic solution and perform some numerical simulations.

cond-mat.soft↗