MATHEMATICAL MODELING AND OPTIMIZATION OF PRODUCTION SCHEDULING: A CASE STUDY OF NIGERIA TELECOMMUNICATIONS LTD

This study focuses on network infrastructure operations and maintenance using mathematical modeling. The research adopted the Mixed Integer Linear Programming (MILP) model to assign technical tasks for available technician teams, for base station servicing, router upgrades, and fiber link maintenance in order to minimize the overall job completion time and guaranteed the timely completion of high- priority tasks. The optimization model was developed and put into practice with Python's PuLP library, using operational data gathered from MTN's regional offices in the form of task logs and scheduling reports. The model considered practical limitations like daily working hours, team availability, and job durations. Significant improvements were found when MTN's current scheduling system with that of the optimized model where compared. These include improved technician workload balance, a 95% compliance rate with job deadlines, and a 30% decrease in average task delay. This study may be taken as a workable, data-driven framework for enhancing operational efficiency in the telecom industry by adopting mathematical modeling for better scheduling choices that may result in cost savings, increased worker productivity, and higher customer satisfaction.