The aim of the course is to present the main theoretical and methodological tools for modeling decisions and for identifying the best decision support strategies. The course also aims at providing the skills on how to use the available data in analytical prescriptive models, how to read the results provided by the adopted models and how to interpret them to propose appropriate solutions to complex management problems.
Curriculum
scheda docente
materiale didattico
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, hints to other metaheuristics.
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
Mutuazione: 20810208 Decision Support Systems and Analytics in Ingegneria gestionale e dell'automazione LM-32 NICOSIA GAIA
Programma
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, hints to other metaheuristics.
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, ....).
Testi Adottati
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
Modalità Erogazione
Lectures both on the blackboard and with projected slides. Some lessons will be devoted to the analysis of case studies.Modalità Valutazione
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.
scheda docente
materiale didattico
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, hints to other metaheuristics.
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
Mutuazione: 20810208 Decision Support Systems and Analytics in Ingegneria gestionale e dell'automazione LM-32 NICOSIA GAIA
Programma
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, hints to other metaheuristics.
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, ....).
Testi Adottati
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
Modalità Erogazione
Lectures both on the blackboard and with projected slides. Some lessons will be devoted to the analysis of case studies.Modalità Valutazione
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.
scheda docente
materiale didattico
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, hints to other metaheuristics.
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
Mutuazione: 20810208 Decision Support Systems and Analytics in Ingegneria gestionale e dell'automazione LM-32 NICOSIA GAIA
Programma
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, hints to other metaheuristics.
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, ....).
Testi Adottati
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
Modalità Erogazione
Lectures both on the blackboard and with projected slides. Some lessons will be devoted to the analysis of case studies.Modalità Valutazione
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.
scheda docente
materiale didattico
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, hints to other metaheuristics.
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
Mutuazione: 20810208 Decision Support Systems and Analytics in Ingegneria gestionale e dell'automazione LM-32 NICOSIA GAIA
Programma
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, hints to other metaheuristics.
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, ....).
Testi Adottati
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
Modalità Erogazione
Lectures both on the blackboard and with projected slides. Some lessons will be devoted to the analysis of case studies.Modalità Valutazione
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.