Optimizing and Analyzing Staff Scheduling Under Disruptions in Airport Baggage Handling

Abstract

Efficient staff scheduling is crucial for smooth baggage handling at Schiphol Airport. Each day, KLM must allocate hundreds of employees to hundreds of time-sensitive loading and unloading tasks. Even small disruptions, such as flight delays or staff shortages, can quickly lead to infeasible schedules and unhandled flights. This thesis develops an optimization framework to provide more insight into the robustness of KLM’s day-ahead baggage handling schedule.

The problem is modeled as a Vehicle Routing Problem with Time Windows (VRPTW), extended mode selection, travel times, and mandatory breaks. To handle the model’s complexity, a Fix-and-Optimize approach is applied, iteratively optimizing subsets of the problem while keeping others fixed. Two extensions are introduced to improve task coverage: allowing staff to assist across work areas (cross-zone assignments) and slightly delaying flights (postponements) when necessary.

Scenario analyses evaluate how disruptions, flight delays and staff shortages, affect task coverage. Results based on real KLM data show that the Base Model already increases the number of planned tasks compared to the current planning tool when comparing optimization for a full shift. Allowing postponements further improves coverage, often enabling full task completion. Under disruptions, the model identifies critical time periods and areas where additional flexibility or actions are required.

This research combines mathematical modeling and analysis to better understand the structure and resilience of KLM’s daily planning. The proposed approach highlights where schedules are vulnerable and demonstrates how optimization can support planners in preparing for operational uncertainty.