Improving Seaport Performance Using Predictive and Optimization Algorithms

Student: Sukharkov Alexander

Educational Programme: Business Analytics and Big Data Systems (Master)

Year of Graduation: 2024

When planning employee schedules, it is always necessary to understand the upcoming workload that must be covered by the withdrawn employees. In a seaport, this load is the ships and the number of containers on them. In this work, we automate the employee scheduling process using time series models and optimization algorithms. The results of our work will help more accurately and quickly create a schedule for port employees.

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