Aircraft routing for on-demand air transportation with service upgrade and maintenance events: Compact model and case study. (March 2019) - Munari, Pedro; Alvarez, Aldair
Abstract: This paper addresses a problem faced by airline companies that offer on-demand flight services. Given a list of flight requests, the company has to assign its aircraft to these requests while minimizing operational costs. The main issue in this planning process involves the positioning of aircraft when they are not available at the airports of customer departure. The cost of this positioning should be as low as possible, as the customers' expenditures are proportional to the requested flight hours only. We propose a compact optimization model to support decision making in this situation. It takes into account the mandatory aircraft maintenance events and the possibility of flight upgrades according to the their impact on the operational costs. One important and novel feature of this model is that it allows the anticipation or postponement of the beginning of flights and maintenance events within a given tolerance, giving more freedom to the decision making process. This research is motivated by a case study carried out with a fractional management airline company that operates in European and Asian countries. Computational experiments using real-life data collected from the company show that the proposed model can be solved quickly using general-purpose optimization software, including open-source alternatives. The results indicate that the obtained solutions lead to significant reductions in the operational costs and hence can be used in practice for effective decision making. Highlights: We address a problem faced by airline companies that offer on-demand flight services. We propose a compact optimization model to support decision making. The model takes into account aircraft maintenance events and flight upgrades. We consider a case study carried out with a fractional management airline company. The obtained solutions lead to significant reductions in the operational costs.