Faculty of Business, Economics and Statistics

Department of Business Administration

Chair of Production and Operations Management
(Lehrstuhl für Produktion und Logistik)
o. Univ.-Prof. Dipl.-Ing. Dr. Richard F. Hartl

PhD-L: Advanced Methods in Optimization
aka How to use your favorite MIP solver: modeling, solving, cannibalizing.


Prof. Andrea Lodi
DEIS, University of Bologna



There will be two blocks of three days and 4-6 hours per day.
  • Monday, Jan 23, 2012, 13-15 and 15:30-18, SE2, BWZ
  • Tuesday, Jan 24, 2012, 10-12 and 14-16, SE1, BWZ
  • Wednesday, Jan 25, 2012, 9-12 HS12, BWZ and 14-15, SE3, BWZ
  • Monday, Feb 13, 2012
    • 11:00 - 12:30, SE1, PART II: Using MIP heuristics in applications
    • 14:15 - 18:00, SE1, PART III: Solving Hard LPs and MIPs
  • Tuesday, Feb 14, 2012
    • 09:15 - 12:00, SE1, Students' presentations and discussion
    • 13:15 - 16:00, SE1, Students' presentations and discussion
  • Wednesday, Feb 15, 2012
    • 09:15 - 11:00, SE1, Class exercises (modeling, processing, solving small MIPs)
    • 11:15 - 13:00, SE1, Wrap up and MIP challenges


Course Material



The first 50 years of Integer and Mixed-Integer Programming (MIP) led to a very stable paradigm for solving problems in a reliable and effective way. This is reflected by very good commercial and noncommercial MIP solvers. The course is intended to give a detailed overview of the MIP technology and the MIP solvers. Namely, it will mainly cover three aspects:

  1. The building blocks of a MIP solver.

    We will run over the first 50 exciting years of MIP by showing some crucial milestones and we will highlight the building blocks that are making nowadays solvers effective from both a performance and an application viewpoint.

  2. How to use a MIP solver as a sophisticated (heuristic) framework.

    Nowadays MIP solvers should not be conceived as black-box exact tools. In fact, they provide countless options for their smart use as hybrid algorithmic frameworks, which thing might turn out especially interesting on the applied context. We will review some of those options and possible hybridizations.

  3. Modeling and algorithmic tips to make a solver effective in practice.

    The capability of a solver to produce good, potentially optimal, solutions is strongly related to the selection of the right model and the use of the right algorithmic tools the solver provides. We will discuss useful tips, from simple to sophisticated, which allow a smart use of a MIP solver.

Finally, we will show that a lot of work must still be done for improving the solvers and extending their modeling capability.



The students' preparation will be evaluated during classes (in the form of the discussion of reading material) and through a homework (in the form of a project possibly requiring modeling and testing).


Last update: Feb 13, 2012