Optimization in Artificial Intelligence and Data Sciences: ODS, First Hybrid Conference, Rome, Italy, September 14-17, 2021(8 AIRO Springer Series)

This book is addressed to researchers in operations research, data science and artificial intelligence. It collects selected contributions from the first hybrid “Optimization and Decision Science - ODS2021” international conference on the theme Optimization and Artificial Intelligence and Data Sciences, which was held in Rome 14-17 September 2021 and organized by AIRO, the Italian Operations Research Society and the Department of Statistical Sciences of Sapienza University of Rome. The book offers new and original contributions on different methodological optimization topics, from Support Vector Machines to Game Theory Network Models, from Mathematical Programming to Heuristic Algorithms, and Optimization Methods for a number of emerging problems from Truck and Drone delivery to Risk Assessment, from Power Networks Design to Portfolio Optimization. The articles in the book can give a significant edge to the general themes of sustainability and pollution reduction, distributive logistics, healthcare management in pandemic scenarios and clinical trials, distributed computing, scheduling, and many others. For these reasons, the book is aimed not only at researchers in the Operations Research community but also for practitioners facing decision-making problems in these areas and to students and researchers from other disciplines, including Artificial Intelligence, Computer Sciences, Finance, Mathematics, and Engineering.

Table of Contents:
1 Mauro Passacantando and Fabio Raciti, A variational inequality approach to a class of network games with local complementarities and global congestion.- 2 Patrizia Daniele and Daniele Sciacca, A two-stage Variational Inequality formulation for a Game Theory Network Model for hospitalization in critic scenarios.- 3 Lorenzo Lampariello et al., On nested affine variational inequalities: the case of multi-portfolio selection.- 4 Rosita Guido et al., Hyper-Parameter Optimization in Support Vector Machine on unbalanced datasets using Genetic Algorithms.- 5 Giovanni Fasano and Massimo Roma, An improvement of the pivoting strategy in the Bunch and Kaufman decomposition, within Truncated Newton methods.- 6 Giulia Caselli et al., Mixed Integer Linear Programming for a Real-World Parallel Machine Scheduling Problem with Workforce and Precedence Constraints.- 7 Lavinia Amorosi et al., Scheduling K-mers Counting in a Distributed Environment.- 8 Nicolas Zufferey et al., Workload balancing at cross-docking platforms.- 9 Giovanni Righini and Pier Giorgio Villani, Knowledge before solutions: some reflections on a successful O.R. case study.- 10 Luigi Rarità, A genetic algorithm to optimize dynamics of supply chains.- 11 Georgia Fargetta and Laura Scrimali, Closed-Loop Supply Chain Network Equilibrium with online second-hand trading.- 12 Andrea Bettinelli et al., Design and optimization of a regional buffalo milk supply chain: a case study.- 13 Olivier Gallay et al., Truck-and-drone parcel delivery in the Alps.- 14 Giacomo Lanza et al., A fast heuristic approach for the assignment and sequencing storage location problem under a two level storage policy.- 15 Nicolas Zufferey et al., Local Search for Aircraft-Landing Planning.- 16 Annamaria Barbagallo, An Emission Pollution Permit System for Time-Dependent Transportation Networks Based on Origin-Destination Pairs.- 17 Daniel Bienstock et al., Power network design with line activity.- 18 Vassil Vassilev et al., Risk Assessment in Transactions under Threat as Partially Observable Markov Decision Process.- 19 Paola Cappanera et al., A decomposition approach to the Clinical Pathway Deployment for chronic outpatients with comorbidities.- 20 Fulvio De Santis and Stefania Gubbiotti, Optimal sample size for evidence and consensus in phase III clinical trials.- 21 Roberto Aringhieri et al., Novel applications of the team orienteering problem in health care logistics.- 22 Beatrice Bolsi et al., Optimizing a Dynamic Outpatient Facility System with Multiple Servers.- 23 Pranath Kumar Gourishetty et al., Global optimization of a turbine design via neural networks and an evolutionary algorithm. 

About the Author :
Lavinia Amorosi is Assistant Professor in Operations Research at the Department of Statistical Sciences of Sapienza, University of Rome. She received the Ph. in Operations Research from Sapienza University of Rome in 2018. She has been visiting Ph. at Lancaster University Management School, Lancaster UK, in 2016 and at the Institute of Mathematics of the University of Seville, Spain, in 2017. Her research area is mainly combinatorial optimization, with particular interest in network optimization and multiobjective programming and data science, with applications to real network problems in telecommunication and transportation areas where she published articles in international journals. She is a founder of the young chapter of the Italian Operations Research Society (AIROYoung), of which she is an outgoing coordinator. She is one of the awarded researchers of the YoungWomen4OR EURO program in 2020. Paolo Dell'Olmo is a Full Professor in Operations Research at the Department of Statistical Sciences of Sapienza University of Rome. Formerly Department Head, Coordinator of the PhD program in Operations Research, Director of the Master Program in Statistics for the Management of Information Systems; Coordinator of Italian Inter-University Center for Operations Research; Member of the Scientific Committee of Fondazione Roma Sapienza); Member of Board of Directors of Fondazione Sapienza. He is currently Director of the Master Program in Data Intelligence and Strategic Decisions. His research interests are mainly in combinatorial optimization, which is also applied to real-life problems. He has been a scientific coordinator of a number of national research projects, and he's author of several books and approximately 80 papers published in international journals. Isabella Lari obtained a PhD degree in Operations Research from the University of Rome La Sapienza in 1994. She is a researcher at the Department of Statistics of the University of Rome La Sapienza. Dr. Lari is teaching Optimization Mathematical Methods and, in the past, has held courses of Elements of Computer Science, Data Structure and Algorithms, Simulation Techniques. Her research activity is mainly focused on combinatorial optimization with particular attention to location and partitioning problems on graphs. She has published in several major journals in the field.