Decision Support Under Uncertainty

Decision Support Under Uncertainty is a course of the Ph.D Program in Computational Mathematics and Decision Science (Università degli Studi di Pavia). The course consists of 24 hours of frontal activity (8 lectures of 3 hours) and the first edition is planned for November-December 2022. 

Lectures will be held in person at the Department of Mathematics "F. Casorati", via A. Ferrata 5, Pavia. For registration or more information, write to For interested students, the examination will consist of a project report using one or more methodologies seen during the course on a real-world problem.

Description: The course aims to offer students an overview of operations research and management science methodologies for supporting complex decisions affected by uncertainty. Theoretical elements of optimization, simulation, and data science will be flanked by examples of real-life applications, with an eye to healthcare management.

Course Program: 

1. Elements of modeling, combinatorial optimization, and computational complexity. Uncertainty and dynamicity. Stochastic combinatorial optimization problems: multi-stage stochastic programming and chance-constrained programming. Sample average approximation and metaheuristics. Python/Gurobi tutorial.

2. Online optimization. Competitive and performance analysis. Classical online problems and algorithms. Randomized online algorithms, adversaries, and competitive analysis through matrix games. Distributional analyses. Python tutorial. 

3. Discrete Event Simulation, System Dynamics, and Agent-Based Simulation; Statistical models, distributions, and processes. Verification, calibration, and validation. Simulation and optimization. Performance analysis. AnyLogic PLE tutorial. 

4. Introduction to process mining: process discovery, conformance checking, and enhancement. ProM tutorial. 



- Online Algorithms: The State of the Art (Amos Fiat, Gerhard J. Woeginger. Lecture Notes in Computer Science, Springer, 1998)

- Online Computation and Competitive Analysis (Allan Borodin, Ran El-Yaniv. Cambridge University Press, 2005)

- Discrete-Event System Simulation: Fifth Edition (Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol. Pearson, 2010)

- The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 8 (Andrei Borshchev, Ilya Grigoryev. AnyLogic) [download]

- Process Mining: Data Science in Action (Wil van der Aalst. Springer, 2016)

Calendar of lectures (and locations):