Mathematical modeling in the airline industry: optimizing aircraft assignment for on-demand air transport

Abstract

Abstract. This paper addresses a problem faced by air companies that offer on-demand flight services. Given a list of flight requests, the company has to assign its aircrafts to these requests, so that operational costs are minimized. The main issue in this planning process regards the repositioning of aircrafts when they are not available at the airports of customer departure. The cost of this repositioning should be the least possible, as customers pay proportionally to their requested flights only. We propose an optimization model to support decision making in these situations. Computational experiments with real-life data provided by an on-demand air transport company indicate that the proposed model is appropriate and may significantly reduce the time spent on repositioning flights.Keywords. airline industry, aircraft assignment, mathematical model, optimization. IntroduçãoThe airline industry is well known by its complexity and costly activities. To run efficiently, an air company must plan its operations very carefully, from aircraft choices to ticket prices. This need for efficiency has become even stronger in the last years, due to an increasing in competitiveness and to the current economic crisis. As a result, air companies are recurring to scientific methods to support decision making. Mathematical models and computational methods have became a must in their planning process [1,2].Another phenomenon observed in the last years concerns the increasing of on-demand air transport services, mainly represented by air taxi and fractional ownership companies [2][3][4]. Differently from the traditional commercial airlines these services are oriented to individual customers and do not work with pre-scheduled flights. Customers impose their departure times and airports and the company has to assign aircraft and crew to each customer request or to a group of them. The flights are point-to-point, outside hub airports and allow around six passengers. In per-seat services, the customer buys a single seat on the flight, while in per-aircraft services the customer gets the whole aircraft.In this paper, we address the aircraft allocation problem that arises in the planning process of per-aircraft on-demand air transport services. This research have been motivated by a case study developed with an air company that offers this service to European

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

“…Besides, most prior works considered the air taxi service provider to own a homogenous fleet and have previous knowledge of the demand (static DAFP). Typically, small problem instances are formulated as integer linear program (e.g., Espinoza et al 2008a;Munari, 2017;Campbell et al, 2020), while large cases are modeled using local search heuristics (Espinoza et al, 2008b).…”