Ecole Centrale Paris - Laboratoire de Génie Industriel
Abstract
In the proposed thesis, we consider an optimization problem arising in the context of call center operations management. We focus on the short-term workforce scheduling problem to be solved by call center managers on a weekly or daily basis. It consists in the determination of the number of agents to be assigned to each shift of the scheduling horizon so as to reach the best possible trade-off between cost minimization and customer quality of service. The main novelty of the proposal lies in the fact that we explicitly take into account in our models the uncertainty in the demand forecasts. We intend to develop stochastic programming approaches to model and solve the resulting difficult stochastic combinatorial optimization problems. This should enable us to provide practitioners with an efficient decision-aid tool as well as with some general useful insights on the call center staffing problem.
Context
Call centers are service systems designed to support the delivery of some interactive service via telephone communications; typically an office space with multiple workstations manned by agents who place and receive calls. Applications include telemarketing, customer service, help desk support and emergency dispatch.
Personnel staffing is a key issue in call center management as the cost of the staff members handling the phone calls usually accounts for 60% to 80% of all operating expenses. Challenges for call center managers include the determination of how many agents to hire and train at what times based on a long term forecast of demand for services (6-12 months ahead) and the scheduling (1-2 weeks ahead) of an available pool of agents for a given time period based on detailed short term forecasts for a given time period.The proposed thesis focuses on short-term staffing decisions, namely workforce scheduling.
Objectives
We aim at developing stochastic programming approaches for the call center workforce scheduling problem under demand forecasts uncertainty. The main scientific challenge ahead is to devise an approach where the optimization problem is modeled with a sufficient degree of accuracy to ensure the practical relevancy of the obtained schedule, while keeping the mathematical formulation computationally tractable. Moreover, the workforce scheduling problem under consideration is a short-term decision problem which has to be solved on a weekly or daily basis. The proposed solution algorithm should thus be capable of providing a good schedule within computation times compatible with an industrial use.
Work program
- extensive review of the literature related to both stochastic programming applications and call center management problems.
- development of a chance-constraint programming approach for a simple version of the call center workforce scheduling problem.
- development of two-stage and multi-stage stochastic programming approaches where forecast updates and schedule adjustment within the scheduling horizon will be considered.
- extensive numerical analysis to evaluate the performance of the proposed models and solution algorithms and to derive some managerial insights for call center practitioners.
Extra information
Prerequisite
Good background in operations research, mathematical programming and statistics
Good programming capacities (C, C++)
Détails
Expected funding
Funding by Digiteo
Status of funding
Confirmed
Candidates
Utilisateur
celine.gicquel
Créé
Vendredi 11 mai 2012 12:18:40 CEST
dernière modif.
Mercredi 13 juin 2012 11:45:57 CEST
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Connexion
Ecole Doctorale Informatique Paris-Sud
Directrice
Nicole Bidoit Assistante
Stéphanie Druetta Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr