|Title:||Demand-driven rostering at Swissport International|
|Authors :||Klinkert, Andreas|
|Conference details:||ECCO XXIX - The 29th Conference of the European Chapter on Combinatorial Optimization, Budapest, Hungary, 2016|
|License (according to publishing contract) :||Not specified|
|Type of review:||Peer review (Abstract)|
|Subjects :||Rostering; Large-scale; Staff scheduling|
|Subject (DDC) :||500: Natural sciences and mathematics |
658.3: Human resource management
|Abstract:||We present a major research and business project aimed at developing efficient and flexible software for automated airport staff scheduling. Industrial partner is Swissport International, the largest ground handling company worldwide, and pilot site is Zurich Airport in Switzerland. Swissport provides services for 230 million passengers a year, with a workforce of 61'000 personnel at 290 airports. Airport ground handling involves a broad range of tasks, including passenger and ramp services. The diversity of activities 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 policy 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 individual work plans. We start with an introduction to the business environment of the project, and show its planning context which comprises other software tools and human planning activities. We discuss the various project requirements, challenges and goals that shaped the project, and the methods used. Employee scheduling involves a number of subproblems including demand modeling, task generation, shift design, days-off scheduling, and shift assignment. The rostering process considered here focuses on the days-off planning and shift assignment phase. The methodology used for solving the associated complex large-scale optimization problems comprises a broad range of optimization techniques including preprocessing, decomposition and relaxation, large-scale integer programming and various heuristic procedures. We provide insight into several aspects of the solution process, including the analysis and preprocessing phase which turned out to be crucial for the entire planning system. An important purpose of this phase is to deal with feasibility issues related to incorrect or inconsistent input data. In fact, experience shows that most of the operational instances submitted to the planning tool are infeasible, and detecting and patching infeasibility is difficult. The tools developed for this planning phase range from data checking and analysis modules to sophisticated mathematical models for bottleneck analysis, identification of minimal infeasible constraint systems, and rapid presolving techniques. Finally, we present computational real-world experience and discuss operational impacts of the developed planning tool. Operational deployment started at Zurich Airport and is continually being expanded to other airports. Bottom line benefits include faster and more robust planning processes, improved roster quality, better fairness, reduced planning effort, and as a result, substantial financial savings.|
|Departement:||School of Engineering|
|Publication type:||Conference Other|
|Appears in Collections:||Publikationen School of Engineering|
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