Scheduling de-icing vehicles within airport logistics: a heuristic algorithm and performance evaluation

• Published in: The Journal of the Operational Research Society Vol. 63; no. 8; pp. 1116 - 1125
• Main Authors: Norin, A, Yuan, D, Granberg, T A, Värbrand, P
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.08.2012; Palgrave Macmillan; Palgrave Macmillan UK; Taylor & Francis Ltd
• Series: Journal of the Operational Research Society
• Subjects: air transport; Air transportation and traffic; Aircraft; Airlines; Airports; Algorithms; Applied sciences; Aviation; Business and Management; Case studies; Customer services; Efficiency; Exact sciences and technology; General Paper; General Papers; Ground, air and sea transportation, marine construction; Heuristics; Ice; Logistics; Management; multi-objective; Operational research and scientific management; Operational research. Management science; Operations Research/Decision Theory; Performance evaluation; Planning; Queuing theory. Traffic theory; Schedules; Scheduling; Scheduling, sequencing; TECHNOLOGY; TEKNIKVETENSKAP; Vehicle capacity; Vehicle operation; Vehicle performance; vehicle routing; Vehicles; Vehicular flight; planning; air transport; logistics; heuristics; vehicle routing; multi-objective; Performance evaluation; Travel time; Vehicle routing problem; Multiobjective programming; Scheduling; Randomized algorithm; Adaptive method; Waiting time; Airplane; Airport; Heuristic method; Greedy algorithm; Air transportation; Delay time; Planning; Dynamic model; Aircraft; Logistics
• ISSN: 0160-5682; 1476-9360; 1476-9360
• DOI: 10.1057/jors.2011.100

Most delays in the air transport occur at the airport. A particular reason is the complexity of managing the large number of supporting flows in airport logistics. We consider the optimisation problem of scheduling de-icing vehicles that is one of the key supporting logistic flows in the turn-around process of aircraft. The objective is to minimise the delay of flights due to de-icing, and the travel distance of the de-icing vehicles. We study the complexity of the problem, and develop a solution algorithm using greedy randomised adaptive search. A case study of real-life data from Stockholm Arlanda Airport shows that optimised schedule leads to significantly better performance in comparison to intuitive and simple scheduling strategies. The benefit of optimisation in reducing the waiting time for de-icing is further demonstrated via dynamic simulations.