Developing a Workforce Scheduling Model for the Nigerian Immigration Service Using Stochastic Demand Forecasting

The Nigerian Immigration Service faces complex workforce scheduling challenges at international airports, land borders, and seaports where passenger and goods processing demand fluctuates significantly across hours, days, and seasonal periods. This study develops a workforce scheduling optimization model for the Nigerian Immigration Service that integrates stochastic demand forecasting with shift scheduling optimization. Passenger arrival and processing demand data are collected from the immigration desks at Murtala Muhammed International Airport over a 12-month period, covering inter-terminal and inter-service-desk variation. Stochastic demand forecasting using seasonal ARIMA models generates probabilistic hourly demand forecasts that are used to derive shift-staffing requirements with explicit service level constraints. A binary integer programming model determines the optimal weekly shift schedule that minimizes total staffing cost subject to minimum coverage constraints derived from demand forecasts, regulatory rest period requirements, officer preference constraints, and maximum consecutive shift limits. The model is validated by simulating actual staffing outcomes against historical demand realizations. Results indicate that the optimized schedule achieves the target service level of 95 percent of passengers processed within 20 minutes using 14 percent fewer officer-shifts than the current schedule, generating significant salary savings without service quality degradation. Recommendations include implementing the scheduling model at pilot border posts before national rollout and providing scheduling software training for immigration human resources officers. Keywords: workforce scheduling, Nigerian Immigration Service, stochastic forecasting, integer programming, airport operations