Research Information System
8th European International Conference on Industrial Engineering and Operations Management (IEOM Paris 2025), Paris, France, 2 - 04 July 2025, vol.258, pp.388-402, (Full Text)
Retailers face significant challenges in workforce scheduling due to highly
variable demand, rising labor costs and strict service requirements. This study
presents a novel, two-stage optimisation framework that integrates
ensemble-based machine learning forecasting with the mathematical programming
approach CPLEX for shift scheduling in the retail sector. In the first stage,
advanced machine learning models (XGBoost, Random Forest and SARIMAX) generate
probabilistic foot traffic forecasts by leveraging extensive feature
engineering. These forecasts are then used as inputs for a shift optimization
model that considers complex operational constraints, such as labor laws, union
agreements and employee preferences. Deployment in a major Turkish retail chain
yielded empirical results demonstrating an 11.3% reduction in labor costs, a
47% decrease in understaffing and an 8.2% improvement in service levels across
more than 200 stores. The proposed approach yields scalable cost savings and
service enhancements, providing a robust decision-support tool for workforce
management in dynamic retail environments.