Crew scheduling

Companies running a 24/7 business (such as railroad operators) also need employees around the clock. Duties at very irregular times (overnight or weekends), a low number of breaks (especially on weekends), and mainly a lack of long-term stability in scheduling plans leads to dissatisfaction and, in the long run, health problems and resignations. When it came to negotiations between the labor union and the management, we were hired as consultants to accompany the process with our scientific background in mathematical optimization.

During this project we developed an optimization model to create feasible working plans for crews. In a second step we evaluated the long-term stability of our plans using Monte-Carlo simulation methods.

The main difficulty was the inherent complexity of the problem. To be feasible, a working plan has to fulfill many complex labor regulations. The resulting optimization model is by far to large to be solved directly even with a state-of-the-art solver. Hence we developed several decomposition rules to break the large problem into a set of smaller ones (using moving horizon and other
techniques), and combined the individual solutions into a global solution for the entire problem.

Partners

  •  TU Darmstadt

Industrial partners / Funding

  • DB Regio AG, Frankfurt.

Related talks

  • Schichtplanung bei der Deutschen Bahn, International Conference on Operations Research OR 2008, Augsburg, Germany, 3.9.2008

This website uses cookies. There are two types of cookies: The first type supports the basic functionality of our website. The second allows us to improve our content for you by saving and analyzing pseudonymised user data. Since this second type is technically not required to run the website, you can withdraw your consent to those cookies at any time. For more information please visit our pages on data protection.

Mandatory

These cookies are needed for a smooth operation of our website.

Statistic

For statistical reasons, we use the platform Matomo to analyse the user flow with the help of website users‘ pseudonymised data. This allows us to optimize website content.

Name Purpose Lifetime Type Provider
_pk_id Used to store a few details about the user such as the unique visitor ID. 13 months HTML Matomo
_pk_ref Used to store the attribution information, the referrer initially used to visit the website. 6 months HTML Matomo
_pk_ses Short lived cookie used to temporarily store data for the visit. 30 minutes HTML Matomo