|Title:||Large-scale rostering in the airport Industry|
|Authors :||Klinkert, Andreas|
|Conference details:||CSM 2014 - 11th International Conference on Computational Management Science, Lisbon, Portugal, 2014|
|Language :||Englisch / English|
|Subjects :||Integer programming; Rostering; Large scale|
|Subject (DDC) :||500: Naturwissenschaften und Mathematik|
|Abstract:||We present a major research and business project aimed at developing efficient and flexible software for automated airport staff rostering. Industrial partner is Swissport International, one of the largest ground handling companies worldwide, which provides services for 224 million passengers and 4 million tons of cargo a year, with a workforce of 55'000 personnel at 255 airports. Pilot site for the project is Zurich Airport in Switzerland. The diversity of the ground handling functions at Zurich Airport, the large number of operational duties, and the around-the-clock business hours result in hundreds of different types of shifts to be planned every month, and an employee base consisting of several thousand persons with numerous different skills. Further challenges come from a dynamic, demand-driven planning approach which does not rely on repetitive shift patterns rolled out over a long-term horizon, and from a so-called shift-bidding approach which attributes high importance to employee preferences regarding the individual work plans. We start with an introduction to the business environment of the project and discuss the various project requirements and the challenges and goals that shaped the project and the methods used. We give insight into the solution methodology which involves preprocessing, decomposition and relaxation techniques, large-scale integer programming models and various heuristic procedures. Finally, we present computational experience and discuss operational impacts of the developed planning tool. The operational deployment started in 2011 in Zurich Airport and has continually been extended since then. Bottom line benefits include faster and more robust planning processes, improved roster quality, better fairness, reduced planning capacity requirements, and as a result, substantial financial savings.|
|Departement:||School of Engineering|
|Organisational Unit:||Institut für Datenanalyse und Prozessdesign (IDP)|
|Publication type:||Konferenz: Sonstiges / Conference Other|
|Type of review:||Peer review (Abstract)|
|License (according to publishing contract) :||Keine Angabe / Not specified|
|Appears in Collections:||Publikationen School of Engineering|
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