Chair: Maria Antónia Carravilla, INESC TEC, Faculdade de Engenharia, Universidade do Porto, Portugal
A GRASP algorithm for the vehicle-reservation assignment problem
Beatriz Brito Oliveira (firstname.lastname@example.org), José Fernando Oliveira (email@example.com), Maria Antónia Carravilla (firstname.lastname@example.org)
Car rental companies face a critical problem related to the empty repositions (or “deadheading trips”) of their vehicles, which comprise a signiﬁcant and avoidable ﬁnancial and environmental impact. Especially when dealing with special types of vehicles whose number of units is small, the company is forced to empty reposition them between rental stations in order to meet reservations requirements concerning available time and location. This paper proposes a GRASP algorithm to generate a global vehicle schedule that maximizes the company’s proﬁt whilst reducing the costs of empty transfers. Using real instances, the value of this approach is established.
Keywords: vehicle-reservation assignment, empty transfers, GRASP
Demand Uncertainty for the Location-Routing Problem with Two-dimensional Loading Constraints
Thiago Alves de Queiroz (email@example.com), José Fernando Oliveira (firstname.lastname@example.org), Maria Antónia Carravilla (email@example.com), Flávio Keidi Miyazawa (firstname.lastname@example.org)
In this work, we investigate the location-routing problem with two-dimensional loading constraints under uncertainty on customer’s demand. Uncertainty on the demand is modeled by a scenario approach in order to get a solution satisfying all the scenarios. An integer programming model concerning decisions on strategic (depot locations), tactical (which customers are serving each depot) and operational levels (routes from depots to customers including the two-dimensional arrangement of items) is proposed. A cut is inserted whenever an infeasible packing is found and the model is applied to solve one real case based example considering three different scenarios.
Keywords: Facility location, vehicle routing, demand uncertainty
Large-Scale Rostering in the Airport Industry
Andreas Klinkert (email@example.com)
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.
Keywords: Rostering, Airport, Large-Scale Optimization