A stochastic integer programming approach to reserve staff scheduling with preferences

TL;DR

This paper introduces a stochastic integer programming model for reserve staff scheduling that considers employee preferences and uncertainties in demand and absences, aiming to improve satisfaction and operational efficiency.

Contribution

It develops a novel two-stage stochastic integer programming approach for reserve staff scheduling incorporating employee preferences and multiple uncertainty sources.

Findings

Model effectively captures employee preferences and uncertainties.

Scheduling decisions improve employee satisfaction and operational robustness.

Framework applicable to transit industry personnel planning.

Abstract

Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel scheduling problem under unknown demand by considering employee satisfaction via endogenous uncertainty depending on the combination of their preferred and received schedules. We address this problem in the context of reserve staff scheduling, an unstudied operational problem from the transit industry. To handle the challenges brought by the two uncertainty sources, regular employee and reserve employee absences, we formulate this problem as a two-stage stochastic integer program with mixed-integer recourse. The first-stage decisions consist in finding the days off of the reserve employees. After the unknown regular employee absences are revealed, the second-stage decisions are to schedule the reserve staff duties. We incorporate reserve employees' days-off preferences into the model to examine how employee satisfaction may affect their own absence rates.