The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes, and optimization models. In particular, the students will be prepared to deal mostly with mathematical programming integer models and some of their applications.
teacher profile teaching materials
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes.
Programme
Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. Typical ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.Core Documentation
[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CHAP. 2, 5, part of 6 and 7).[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes.
Type of delivery of the course
lectures, exercises, computer lab experienceType of evaluation
The exam is in two parts: the written exam usually lasts 2 hours and the oral exam follows. The written exam consists of a number of exercises (typically, from 3 to 5), aimed at veryfing the level of understanding of the theoretical concepts and the students' ability to apply the techniques explained in class.