A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

• Published in: Omega (Oxford) Vol. 53; pp. 97 - 103
• Main Authors: Parisio, Alessandra, Neil Jones, Colin
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2015; Pergamon Press Inc
• Subjects: Decision making/process; Decision support systems; Demand; Integer programming; Linear programming; Operational/OR; Optimization; Resource management; Scheduling; Sequential scheduling; Simulation; Stochastic models; Stochastic programming; Studies; Switzerland; Decision support systems; Integer programming; Operational/OR; Decision making/process; Scheduling; Resource management; Optimization; Stochastic programming
• ISSN: 0305-0483; 1873-5274; 1873-5274
• DOI: 10.1016/j.omega.2015.01.003

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland. •We propose an integrated optimization-based framework for workforce planning in the retail market.•Sophisticated forecasting methods are integrated with stochastic programming.•Adjustment in reaction to deviations from demand forecasts is incorporated.•We solve a computationally tractable mixed integer scenario-based stochastic program.•Our scheduler is compared with a deterministic package currently used in several Swiss outlets.