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, the PERT method for Project Management. In addition, key concepts are introduced to solve multi-criterion and decision-making problems

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. Models for the allocation of logistic centers or services (center and median of a graph, covering problems). Mixed Integer Programming Templates (planning, location, scheduling). Single Machine MIP Templates: Alternative Formulations. Relaxation Techniques. Lagrangian relaxation. Meta - euristic 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). Population-based Methods (Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization). Heuristic techniques for solving MIP problems (Math - heuristic methods). 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). The PERT - CPM method. Non-deterministic decision-making models (Theory of Decisions). Risk-based decision makers and uncertainties. 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