Single-Leg Air-Cargo Revenue Management

Abstract

W e consider a cargo booking problem on a single-leg flight with the goal of maximizing expected contribution. Each piece of cargo is endowed with a random volume and a random weight whose precise values are not known until just before the flight's departure. We formulate the problem as a Markov decision process (MDP). Exact solution of the formulation is impractical, because of its high-dimensional state space; therefore, we develop six heuristics. The first four heuristics are based on different value-function approximations derived from two computationally tractable MDPs, each with a one-dimensional state space. The remaining two heuristics are obtained from solving related methematical programming problems. We also compare the heuristics with the first-come, first-served (FCFS) policy. Simulation experiments suggest that the value function approximation derived from separate "volume" and "weight" problems offers the best approach. Comparisons of the expected contribution under the heuristic to an upper bound show that the heuristic is typically close to optimal.

Close integration of pricing and supply chain decisions has strategic as well as operations level benefits

“…Often RM is practiced less formally by sales or marketing groups who adjust product prices, advertising programs, and develop sales channels or market segments with a view to enhancing revenues. RM as a decision-making protocol is widely established in the service sector, including airlines (Smith et al 1992), hotels (Bitran and Mondschein 1995;Bitran and Gilbert 1996;Baker and Collier 1999), car rental (Geraghty and Johnson 1997), air-cargo (Amaruchkul et al 2007), and apartment leases (Chen et al 2014) and has been spreading rapidly to manufactured goods and the retail sector (for example, Federgruen and Heching 1999;Chen and Bell 2009). Leibs (2000) reported that:…”

Aggregate–disaggregate approach to an airfreight forwarder’s planning under uncertainty: a case study

“…To directly acquire airspace from carriers, the most common booking method is allotment booking-carriers selling their capacity using a long-term contract (Slager and Kapteijns, 2004;Amaruchkul et al, 2007;Feng et al, 2015a).…”

Optimal solution for a cargo revenue management problem with allotment and spot arrivals

“…The DP formulation we present, in the second part of our paper, for the dynamic spot booking control model is close to that in Amaruchkul et al (2007) in the sense that the state of our DP is based on the weight and volume of the showing up bookings. However, three features distinguish our work from theirs: First, this paper studies the allotment and spot booking allocation problems which are inter-related.…”

“…Thus, our DP model tracks how much of the total weight and volume accepted is due to allotment show-ups and how much is due to spot show-ups. Amaruchkul et al (2007) only consider the spot booking allocation, without the consideration of show-ups, thus, the state of their Markov Decision Process (MDP) is based on the weight and volume of the spot bookings only. Second, the time-dependent arrival probabilities of the spot booking requests of our DP model are derived using the static demands modeled by Poisson random variables of the static capacity allocation problem that occurs at the beginning of the booking horizon.…”