In manufacturing organizations with various shifts, workers utilize shuttle services to transport employees safely and on time to and from work. In such instances, if shuttle services are planned and organized by human resource staff using calculative tools like spreadsheets, it may lead to effective shutdown and increased fuel costs. The study aimed to integrate an integer linear programming formulation in workforce shuttle service scheduling system at a semiconductor firm with seven shift schedules. To improve data-driven, economical, and sustainable workforce transportation planning systems, this study supports SDG 17 by encouraging cross- functional collaboration and integrating optimization with digital technologies. The integrated system comprises a React frontend layer, a Python FastAPI server layer, and a Supabase PostgreSQL database to support secure and real-time planning and auditing processes. Google Sheets provides the administrative control interface for tariffs and policies. An eight- week evaluation indicated the system to have a high degree of planning accuracy with respect to the operational and fiscal parameters. Total cost accuracy reached 99.61%, and the time spent on weekly planning decreased by 91.25%, as did the shuttle costs by 43.57%. The Wilcoxon signed-rank test showed significant reductions in the time and costs of planning. Results indicate that combining operations-research optimization with modern data infrastructure can significantly improve workforce transportation planning in complex, multi-shift manufacturing environments.

Keywords

Integer linear programming, workforce transportation, shuttle scheduling, operations research, decision support systems.