Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-28T01:19:51.312Z Has data issue: false hasContentIssue false
  • English
  • Français

France Telecom workforce scheduling problem: a challenge

Published online by Cambridge University Press:  08 October 2009

Sebastian Pokutta  and
Gautier Stauffer
Sebastian Pokutta
Affiliation:
Operations Research Center, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; pokutta@mit.edu
Gautier Stauffer
Affiliation:
IBM Zurich Research Lab, Säumerstrasse 4, 8803 Rüschlikon, Switzerland; gst@zurich.ibm.com
Get access

Abstract

In this paper, we describe the methodology used to tackle FranceTelecom workforce scheduling problem (the subject of the RoadefChallenge 2007) and we report the results obtained on the different data sets provided for the competition. Since the problem at hand appears to be NP-hard and due to the highdimensions of the instance sets, we use a two-step heuristical approach. Wefirst devise a problem-tailored heuristic that provides good feasiblesolutions and then we use a meta-heuristic scheme to improve the currentresults. The tailored heuristic makes use of sophisticated integer programming modelsand the corresponding sub-problems are solved using CPLEXwhile the meta-heuristic framework is a randomized local search algorithm. The approach herein described allowed us to rank 5th in this challenge.

Keywords

Workforce schedulingschedule generation schemeroadef challenge.
Type
Research Article
Information
RAIRO - Operations Research , Volume 43 , Issue 4: ROADEF 07 , October 2009 , pp. 375 - 386
Copyright
© EDP Sciences, ROADEF, SMAI, 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baker, K.R., Workforce allocation in cyclical scheduling problems: a survey. Oper. Res. Q. 27 (1976) 155–167. CrossRef
Boost-Library Team, The Boost C++ libraries. http://www.boost.org/ (2006).
J. Bedaux, C++ Mersenne Twister pseudo-random number generator, http://www.bedaux.net/mtrand/ (2006).
P.-F. Dutot, A. Laugier and A.-M. Bustos, Technicians and interventions scheduling for telecommunications. http://www.g-scop.inpg.fr/ChallengeROADEF2007/en/sujet/sujet2.pdf (2006).
C. Hurkens, Incorporating the strength of MIP modeling in Schedule construction. http://www.g-scop.inpg.fr/ChallengeROADEF2007/TEAMS/roadef28/abstract_roadef28.pdf (2007).
ILOG Inc., CPLEX Solver. http://www.ilog.com (2006).
Kolisch, R. and Hartmann, S., Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 127 (2000) 394–407.
Kolisch, R. and Hartmann, S., Experimental investigations of heuristics for resource-constrained project scheduling: an update. Eur. J. Oper. Res. 174 (2006) 23–37. CrossRef
H. Mahmoud, Pólya Urn Models. Taylor & Francis Group LLC – CRC Press (2008).
D. Merkle, M. Middendorf and H. Schmeck, Ant colony optimization for resource-constrained project-scheduling, IEEE Trans. Evol. Comput. 6 (2002) 333–346.
http://www.g-scop.inpg.fr/ChallengeROADEF2007/
E. Tsang and C. Voudouris, Fast local search and guided local search and their application to British Telecom's workforce scheduling problem. Technical Report CSM-246, Department of Computer Science, University of Essex, UK (1995).