The aim of the course is to provide the main theoretical and methodological tools for decision modeling and for identifying the best decision support strategies in consideration of the established objectives. The course also aims to provide skills and competencies on how to use available data to implement analytical prescriptive models to support decisions, how to read the results provided by the models in use, and how to interpret them to propose appropriate solutions to complex management problems.
teacher profile teaching materials
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).
2. Slides e notes given by the lecturer
Fruizione: 20810533-1 DECISION SUPPORT SYSTEMS AND ANALYTICS in Ingegneria gestionale e dell'automazione LM-32 (docente da definire)
Programme
Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).
Core Documentation
1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.2. Slides e notes given by the lecturer
Type of delivery of the course
Lessons both on the blackboard and with projected slides. Some lessons will be devoted to the analysis of case studies.Attendance
Preferably in person. In any case, the provided material offers the opportunity for non-attending students to prepare for the exam.Type of evaluation
The exam will be a 2-hour written test, organized through a number of questions, aimed at verifying the students' actual level of understanding of the concepts and their ability to apply them in real contexts. The written test will be integrated either with an oral test or with the development of a project to be carried out in the laboratory under the guidance of the teacher.