OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems, including designing flight schedules that maximize profitability while satisfying rules related to aircraft maintenance; generating cost-minimizing, feasible work schedules for pilots and flight attendants; and identifying implementable, low-cost changes to aircraft and crew schedules as disruptions render the planned schedule inoperable. The complexities associated with these problems are immense, including long-and short-term planning horizons; and multiple resources including aircraft, crews, and passengers, all operating over shared airspace and airport capacity. Optimization approaches have played an important role in overcoming this complexity and providing effective aircraft and crew schedules. Historical optimization-based approaches, however, often involve a sequential process, first generating aircraft schedules and then generating crew schedules. Decisions taken in the first steps of the process limit those that are possible in subsequent steps, resulting in overall plans that, while feasible, are typically sub-optimal. To mitigate the myopic effects of sequential solutions, researchers have developed extended models that begin to integrate som

Integrated aircraft scheduling problem: An auto-adapting algorithm to find robust aircraft assignments for large flight plans

The overall airline scheduling process involves hierarchical steps starting with the network design and ending with crew assignment. Aircraft routing is especially important with respect to timing and costs for an airline. In this contribution, we focus on aircraft routing where aircraft are assigned to flight legs further considering maintenance requirements. We developed and implemented algorithms that extend the aircraft routing problem (ARP) by including profit and robustness. The latter objective is important as the dependencies of flights and airlines increases and deviations to the original time plan as unexpected events like volcano eruptions or heavy weather-related issues are difficult to handle. A robust aircraft routing ensures that unforeseen events have less impact. The results are compared to current state-of-the-art solutions. We developed a test instance-generator to create specific problems and build a library for future benchmarking tests

Simultane Erstellung von Flugplänen mit Genetischen Algorithmen : Konzeptioneller Planungsansatz und Entwicklung eines Prototypen

Algorithmus , Flugplanun

A rolling stock circulation model for combining and splitting of passenger trains.

This paper addresses the railway rolling stock circulation problem. Given the departure and arrival times as well as the expected numbers of passengers, we have to assign the rolling stock to the timetable services. We consider several objective criteria that are related to operational costs, service quality and reliability of the railway system. Our model is an extension of an existing rolling stock model for routing train units along a number of connected train lines. The extended model can also handle underway combining and splitting of trains. We illustrate our model by computational experiments based on instances of NS Reizigers, the main Dutch operator of passenger train

Improving the Air Mobility Command\u27s Air Refueler Route Building Capabilities

We consider the problem of routing an aircraft (receiver) from a starting location to a target and back to an ending location while maintaining a fuel level above a predetermined level during all stages of the route and avoiding threat and no-fly zones. The receiver is routed to air refueling locations to refuel as required. The development of the network requires the processing of threat and no-fly zones to create the set of nodes that includes the bases (starting and end locations), the targets, and air refueling locations in addition to the restricted zone nodes. We develop a greedy heuristic that builds the route using arc paths and the on board fuel level to determine the termination of each sequential arc path. Post processing of the routes reduces the fuel remaining on board by shifting the time at target or reversing the route. The results from the greedy heuristic are compared to the results from the current methodology and show that the heuristic requires less time to produce routes that require less fuel

A mixed integer linear programming formulation for the vehicle routing problem with backhauls

The separate delivery and collection services of goods through different routes is an issue of current interest for some transportation companies by the need to avoid the reorganization of the loads inside the vehicles, to reduce the return of the vehicles with empty load and to give greater priority to the delivery customers. In the vehicle routing problem with backhauls (VRPB), the customers are partitioned into two subsets: linehaul (delivery) and backhaul (pickup) customers. Additionally, a precedence constraint is established: the backhaul customers in a route should be visited after all the linehaul customers. The VRPB is presented in the literature as an extension of the capacitated vehicle routing problem and is NP-hard in the strong sense. In this paper, we propose a mixed integer linear programming formulation for the VRPB, based on the generalization of the open vehicle routing problem; that eliminates the possibility of generating solutions formed by subtours using a set of new constraints focused on obtaining valid solutions formed by Hamiltonian paths and connected by tie-arcs. The proposed formulation is a general purpose model in the sense that it does not deserve specifically tailored algorithmic approaches for their effective solution. The computational results show that the proposed compact formulation is competitive against state-of-the-art exact methods for VRPB instances from the literature