Teacher(s)

Catanzaro Daniele;

Language

English

Prerequisites

Students are expected to have a strong background in the following areas:
• Foundations of Linear and Integer Programming
• Basic knowledge of graph theory 
• Probability theory and stochastic processes
• Programming skills in Python and experience in implementing or SCIP.

Main themes

This course introduces foundational principles and advanced mathematical and algorithmic methods for designing, analyzing, and optimizing complex networked systems, including supply chains, infrastructure networks, and digital platforms. It integrates large-scale optimization, uncertainty modeling, decomposition techniques, and AI-assisted decision methods, with applications in transportation, telecommunications, logistics, and strategic resource systems.

Learning outcomes

At the end of this learning unit, the student is able to :
1	1. Model complex real-world logistic and supply chain systems as mathematical optimization programs (MILP, MINLP, and bilevel formulations) integrating discrete decisions, flows, and complex constraints. 2. Critically analyze optimization models, assessing correctness, scalability, sensitivity to parameters, and structural strength. 3. Design advanced solution strategies, including relaxations, decomposition methods, cutting-plane approaches, and controlled heuristics. 4. Evaluate robustness and structural resilience of decision systems under uncertainty, disruptions, adversarial events, or demand shocks. 5. Formulate and analyze network-based decision systems across physical, digital, and hybrid infrastructures. 6. Interpret AI methods within optimization pipelines, understanding machine learning and reinforcement learning as tools for approximating optimal decisions or accelerating optimization procedures. 7. Implement computational optimization workflows using modern modeling environments, solvers, and simulation tools. 8. Make informed strategic decisions in complex networked environments balancing efficiency, risk, resilience, and economic performance.

Content

 

• Mathematical optimization foundations for networked systems: large-scale MILP/MINLP modeling, structured formulations (network design, facility location, scheduling), complexity and integrality gaps, LP and Lagrangian relaxations, solver diagnostics, and structure-exploiting cutting planes
• Algorithmic architectures for large-scale decision problems: Benders decomposition, Dantzig–Wolfe reformulation, column generation, branch-and-price, path-based formulations, and scalability vs responsiveness trade-offs
• Optimization under uncertainty: stochastic programming with recourse, chance constraints, robust and distributionally robust optimization, min–max and regret models, stability and risk-aware decision making
• Networks, flows, and infrastructure systems: survivable network design, interdiction and stress-testing models, multi-commodity flows, congestion and equilibrium phenomena, transportation systems, and telecommunications networks
• Strategic and adversarial logistics: dynamic resupply, inventory-routing, crisis response optimization, strategic sourcing and stockpiling, and resilience of global supply chains under disruption and geopolitical risk
• Integrated applications and case studies drawn from transportation, telecommunications, logistics, industrial systems, and large-scale digital service platforms

Teaching methods

The course combines lectures, computational labs, and case-based sessions. Emphasis is placed on mathematical modeling, algorithmic design, and implementation using optimization solvers. Interactive decision scenarios allow students to apply methods to realistic large-scale network systems under uncertainty and operational constraints.

Evaluation methods

Assessment Components
Written Exam (60%): Assesses theoretical understanding, modeling proficiency, and algorithmic reasoning.
Case Study (40%): Assesses problem-solving and applied modeling skills. Students are required to formulate and solve a case study related to topics covered in the course. The case study may involve real or realistic data and may be completed individually or in small groups. Students must develop an appropriate mathematical model, implement a solution method, analyze computational performance, interpret results, and justify the resulting decisions.
Final Grade: The final grade is computed as the weighted sum of the scores obtained in the written exam and the case study. A failing performance in either component results in an overall failing grade.

Other information

Prerequisites: Regular prerequisites for a Master level course in Management.

Online resources

The platform for exchanging information and communicating with all stakeholders will be the Moodle sites of this course: LLSMS 2030

Bibliography

Useful references:

• Chopra, S. and P. Meindl, Supply Chain Management: Strategy, Planning and Operation, Prentice Hall
• M. Christopher, Logistics and Supply Chain Management, FT Prentice Hall.

Faculty or entity

> CLSM