Authors:
Mathilde Excoffier
1
;
Céline Gicquel
1
;
Oualid Jouini
2
and
Abdel Lisser
1
Affiliations:
1
Université Paris-Sud, France
;
2
Ecole Centrale Paris - ECP, France
Keyword(s):
Distributionally Robust Optimization, Stochastic Programming, Joint Chance Constraints, Mixed-Integer Linear Programming, Staffing, Shift-Scheduling, Call Centers, Queuing Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Queuing Theory
;
Stochastic Optimization
;
Symbolic Systems
Abstract:
We focus on the staffing and shift-scheduling problem in call centers. We consider that the call arrival rates are subject to uncertainty and are following unknown continuous probability distributions. We assume that we only know the first and second moments of the distribution. We propose to model this stochastic optimization problem as a distributionally robust program with joint chance constraints. We consider a dynamic sharing out of the risk throughout the entire scheduling horizon. We propose a deterministic equivalent of the problem and solve linear approximations of the program to provide upper and lower bounds of the optimal solution. We applied our approach on a real-life instance and give numerical results.