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2342. Disruption Management in Airline Luggage Handling using Local Search

Invited abstract in session TC-54: Disruption management and recovery, stream Public Transport Optimization.

Tuesday, 12:30-14:00
Room: S01 (building: 101)

Authors (first author is the speaker)

1. Lisanne Heuseveldt
Information and Computing Sciences, Utrecht University
2. Marjan van den Akker
Information and Computing Sciences, Utrecht University
3. Ali Poursaeidesfahani
KLM Royal Dutch Airlines
4. Philip de Bruin
Information and Computing Sciences, Utrecht University
5. Kunal Kumar
KLM Royal Dutch Airlines

Abstract

Flight times are influenced by many factors. Examples are weather conditions or congestion at the airport, leading to ATC delays or limited gate availability. These factors influence the arrival time at the gate, which can thus vary considerably.

Since COVID, ground personnel for loading and unloading luggage to and from an aircraft are scarcely available, so we need to deploy them efficiently. Because of this scarcity, the process is highly sensitive to the variation of arrival times. To deal with this, a fast algorithm to (re)schedule the allocation of the luggage handlers is required.

We consider the assignment of luggage handlers to tasks arising from arriving and departing aircraft on the day of operations. We aim to make real-time updates that solve disruptions and are cost-efficient. This problem can be considered as a technician routing and scheduling problem (TRSP), where we consider teaming, skill and time window constraints in a dynamic setting. We have to synchronise multiple tasks corresponding to the same flight, which is a new aspect in regard to TRSP literature. Our algorithm proposes delays or cancellations of flights; these levers are often unexplored from a ground operations perspective.

We solve these disruptions using a simulated annealing algorithm, with the original solution as a starting state. This solves the problem in real-time. We will present computational results from experiments with real-life data provided by KLM Royal Dutch Airlines.

Keywords

Status: accepted

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