OVERVIEW

The course presents mathematical models and methods, in particular of Operations Research, with which decision-making problems can be tackled in various fields, from production to logistics. The approaches studied are mainly aimed at situations in which the decisions have a discrete nature (combinatorial problems), presenting techniques to the state of the art of scientific literature.

AIMS AND CONTENT

LEARNING OUTCOMES

Modeling and solving complex decision-making problems. Applications for manufacturing planning and scheduling and logistics (network flow, location and vehicle routing). Integer programming, heuristic and metauristic models and methods for combinatorial optimization problems are studied. In addition, key concepts are introduced to solve multi-criterion and decision-making problems

AIMS AND LEARNING OUTCOMES

The main objective is to provide students with the ability to face problems using the tools made available by Operations Research. The problems treated constitute a reference for large classes of real application problems of a complex nature. Specifically, they will deal with applications for manufacturing planning and scheduling and for logistics and transportation (network flow, location and vehicle routing). Most of the models studied will be mixed integer programming; models of network flow and multi-criteria models will be also considered. A large space will be dedicated to heuristic and metaheuristic methods that constitute the main tool with which to face complex decisional situations in reality.

PREREQUISITES

Basic concepts of Operations Research and Computer Science

TEACHING METHODS

Frontal classes and some practices using a mixed integer programming solver

SYLLABUS/CONTENT

Introduction to decision-making problems, methodologies and their limits. Linear Optimization Models: Example of formulations, use of Solver and interpretation of results. Flow Networks, Max Flow and Min Cost Flow Algorithms, Network Simplex. Production Planning Templates: Dynamic Lot Sizing Problem (single item, multi-item) and its variants. Multi-stage Planning Models. Decision makers on graphs and networks with application in the logistics industry. Mixed Integer Programming Templates (planning, location, scheduling). Single Machine MIP Templates: Alternative Formulations. Relaxation Techniques. Lagrangian relaxation. Metaheuristic methods for the solution of combinatorial problems. Neighbourhood Search Methods. Trajectory Methods (Iterated Local Search, Tabu Search, Simulated Annealing, Variable Neighborhood Search, GRASP, Iterated Greedy Algorithm, Adaptive Large Neighborhood Search). Population-based Methods (Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization). Models for Routing Vehicles in Transport Networks (Vehicle Routing Problems). Exact and heuristic routing models on nodes (Traveling Salesman Problem, Capacitated Vehicle Routing Problem). Exact and heuristic routing patterns (Chinese Postman Problem, Capacitated Arc Routing Problem). Project Management (Project Management). Deterministic decision-making models that use many criteria (Multicriteria Decision Making). Multi-attribute and multi-objective decision making methods

RECOMMENDED READING/BIBLIOGRAPHY

Teaching material available on aulaweb.

TEACHERS AND EXAM BOARD

Exam Board

MASSIMO PAOLUCCI (President)

DAVIDE ANGHINOLFI

ALBERTO GROSSO

MARCELLO SANGUINETI

LESSONS

Class schedule

METHODS AND MODELS FOR DECISION SUPPORT

EXAMS

EXAM DESCRIPTION

Oral testing and / or development of a project (for students who have attended classes with assiduity).

ASSESSMENT METHODS

At the end of the course the acquired skills will enable students to structure decision-making problems of medium complexity and to choose the right solution methodologies as well as to use simple software packages as decision support tools

Exam schedule