Fare Classes Research Articles

The impact of low-cost carriers on high-quality producers in the aviation industry: cost-benefit analysis

PurposeThis paper aims to examine the low-cost carriers (LCC) impact on the high-quality carriers (HQC) in the aviation industry. The impact of LCCs on high-quality producers in the aviation industry has been a significant and multifaceted phenomenon.Design/methodology/approachThe study employs a captivating case study approach, investigating into the intricate fabric of the subject matter. Interviews serve as the cornerstone of primary evidence, offering first-hand insights, while secondary data sourced from documents adds depth to the exploration of the challenges encountered by the HQC.FindingsThe study concludes that LCCs have disrupted the traditional aviation landscape by offering low fares, simplified service models and aggressive cost-cutting strategies. This disruption has affected both the high-quality producers, such as full-service airlines. Full-service airlines have adopted a strategy of segmenting their market by offering multiple fare classes, with varying levels of service and flexibility. This allows them to target both price-sensitive travelers and those seeking premium services, catering to a broader customer base. The competition from LCCs has spurred innovation within the aviation industry, leading to advancements in technology, digital services and operational efficiency. Airlines, both LCCs and traditional carriers, have had to adapt to evolving consumer preferences and embrace digital solutions for booking, check-in and in-flight services.Research limitations/implicationsWhile this study provides a valuable cost-benefit analysis of the impact of LCC on high-quality producers in the aviation industry, it is essential to acknowledge its limitations and recognize the avenues for future research to further enhance our understanding of this complex and evolving industry landscape. While this study contributes valuable insights into the impact of LCCs on high-quality producers in the aviation industry, it is essential to recognize its limitations and identify opportunities for future research to expand our understanding of this complex and dynamic landscape. By addressing these limitations and exploring new avenues of inquiry, we can continue to advance our knowledge and inform evidence-based decision-making within the industry.Originality/valueThis study pioneers an exploration into the intricate tapestry of factors molding the future of the aviation sector. Through its groundbreaking analysis, it furnishes indispensable insights for industry stakeholders, policymakers and the discerning traveling public, setting a new benchmark for understanding and navigating the aviation landscape.

Pricing Optimization for Perishable Primary and Ancillary Items with Time-Dependent Inventory Drawdown

There are many industrial applications where attempts are made to sell ancillary products or services with a primary item. For instance, a warranty is often offered as an ancillary product to those who buy television sets (the primary item). This has become even more important in the airline industry where ancillary products such as baggage fees have become crucial to the financial health of the industry. Rapid depletion of available seat inventory, akin to perishable assets, necessitates careful consideration of pricing strategies to maximize revenue. It can make a big difference to revenue if certain fare classes are likely to sell out quickly. For instance, if most of the fares without seat assignment sell out earlier than those with seat assignment, the airline would forego a lot of the revenue from the ancillary item. We formulate the problem allowing for time dependence. In this paper, the question of what price to charge for the primary and ancillary products will be investigated.

Airline revenue management with segmented continuous pricing: methods and competitive effects

With the introduction of IATA’s New Distribution Capability (NDC), airlines will no longer be limited to discrete fare classes for their fare product distribution but could show fare quotes from continuous ranges to booking requests. NDC will also allow airlines to present different fare quotes to passengers from different demand segments as identified by the airline. In theory, airlines can better extract passenger willingness to pay, and thus, see gains in revenue, by offering segmented continuous fare quotes to different passengers requesting to book. This paper describes the revenue management (RM) methods for Segmented Continuous Pricing and examines their potential effects on airlines’ revenue through simulations in the Passenger Origin–Destination Simulator (PODS). We describe a class-based algorithm for continuous pricing, a straightforward extension from the traditional methods used with existing RM systems. Our simulation results show that in a calibrated scenario in which only one airline adopts Segmented Continuous Pricing and has an 80% accuracy in identifying business versus leisure passenger booking requests, the first-mover airline can see as much as a 17% revenue gain, at the expense of competitors. The revenue gains come primarily from the leisure passenger segment by offering lower fares than competitors closer to departure. The first-mover airline loses bookings but does not see losses in revenue from the business passenger segment. We also explore potential response strategies by the competing airlines. We discover that competitors can reverse the first-mover’s revenue gain by removing their fare restrictions while still using traditional RM methods. We conclude that although adopting Segmented Continuous Pricing is promising in theory, its gains in practice will depend heavily on the competitive situation and the responses made by competing airlines.

Optimal Statistical Estimation and Dynamic Adaptive Control of Airline Seat Protection Levels for Several Nested Fare Classes under Parametric Uncertainty of Customer Demand Models

Assigning seats in the same compartment to different fare classes of passengers is a major problem of airline seat allocation. Airlines sell the same seat at different prices according to the time at which the reservation is made and other conditions. Thus the same seat can be sold at different prices. The problem is to find an optimal policy that maximizes total expected revenue. To solve the above problem, this paper presents the novel computational approach to optimization and dynamic adaptive prediction of airline seat protection levels for multiple nested fare classes of single-leg flights under parametric uncertainty. It is assumed that time T (before the flight is scheduled to depart) is divided into h periods, namely a full fare period and h-1 discounted fare periods. The fare structure is given. An airplane has a seat capacity of N. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flight with two fare classes (business and economy). The proposed airline's inventory management policy is based on the use of the proposed computational models. These models emphasize pivotal quantities and ancillary statistics relevant for obtaining statistical predictive limits for anticipated quantities under parametric uncertainty and are applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The proposed technique is based on a probability transformation and pivotal quantity averaging. It is conceptually simple and easy to use. Finally, we give illustrative examples, where the proposed analytical methodology is illustrated in terms of the two-parameter exponential distribution. Applications to other log-location-scale distributions could follow directly.

Estimating the impact of behavior heterogeneity on airfare choices

Previous studies on airfare choices have discussed various types of heterogeneity. Based on these findings, this study investigated a type of non-trading behavior observed in the context of airfare booking. Required by the pricing and revenue management decisions, airline often offers hierarchical airfares subject to capacity constraints. This presents challenges in identifying the heterogeneous choice behaviors among air travelers and in the consequent demand estimation. To differentiate the trading and non-trading behavior of respondents, a stated preference (SP) experiment that allows variation of choice set composition was applied. Among the data, over 40% of respondents were observed to choose only from the non-flexible fare class. Identifying such heterogeneity can improve choice modelling and help better understand airfare choice behaviors. In addition, taking into account behavior heterogeneity may produce significantly different demand forecasts, according to which airlines may devise distinctive marketing strategies. Despite the managerial implications, there are some potential privacy and safety issues that may raise the awareness of policy-makers, and this has also been discussed in this research.

Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability

Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes.

Resource allocation problem is one of key issues in the field of revenue management. The traditional models usually rely on some restrictive assumptions about demand information or arrival process, which is sometimes out of line with reality. To overcome this shortcoming, the method of competitive analysis of online algorithms, which eliminates the need for the assumptions on demand and arrivals, is adopted to deal with the quantity-based revenue management problem. The current model in this paper considers two downgrade compatible levels of resources. Given the capacities and fares of both levels of resources, the objective is to accept appropriate customers and assign them to appropriate resources so as to maximize revenues. Compared with the existing literature, this paper generalizes the concerned resource allocation problem by considering multiple fares for each level of resources. From the perspective of online algorithms and competitive analysis, both an upper bound and an optimal online strategy are derived in this paper.

Choice-Based Airline Schedule Design and Fleet Assignment: A Decomposition Approach

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision that greatly influences airline profits. As passenger demand is often substitutable among available fare products (defined as a combination of an itinerary and a fare class) between the same origin–destination pair, we present an optimization approach that includes a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependencies in network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to allocate optimally the fares of products that are shared by flights in different subnetworks. We present several reformulations that represent fleet assignment and schedule decisions and formally characterize their relative strengths. This gives rise to a new reformulation that is able to trade off strength and size flexibly. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over all benchmark implementations and previous models in the literature.

Optimisation of Airline Dynamic Multileg Capacity Control Problem considering Competition from High-Speed Rail

This paper develops an optimisation model to address the dynamic multileg capacity control problem for the airline, taking into account the competition from the high-speed rail (HSR). Conventional capacity control models assume that the passenger demand is independent and that airlines control capacity independently. The proposed model considers the semi-independent demand, i.e., a customer of certain fare class arrives and makes choices from available alternatives provided by both airline and HSR. If the customer’s first option is rejected, their booking request will be diverted both horizontally to the other operator and vertically to parallel flights within the same operator. Making use of this assumption, the customer’s second and third choices are captured. With this model, airlines can control the sales process to determine whether or not to accept an initial booking request or diverted booking request to maximise their revenue. The deterministic linear programming (DLP) method is proposed as a model solution, enabling the computation of partitioned allocation and the bid price of each product. The optimal capacity control strategy is then obtained based on bid price control. To validate the model, numerical experiments are conducted to simulate customers' actual arrival and to show that the model is capable of producing better revenue, especially in the event that demand exceeds supply. Moreover, the demand diversion effect brings more revenue for both airline and HSR, and this potential revenue source has been ignored by previous research studies.

Hybrid nesting control strategy for passenger railway with one‐seat‐one‐ticket restriction

AbstractNesting control is one of the most prevalent quantity‐based controls for the revenue management problem. A popular nesting control strategy for multi‐resource problem is the virtual nesting control, which sets nested booking limits on each resource. However, this control was originally developed for the airline and cannot be directly used in the passenger railway with one‐seat‐one‐ticket restriction. Therefore, this paper develops a new nesting control that is applicable to the railway. The proposed control can nest the capacity over different fare classes and origin‐destination pairs, which overcomes the shortcomings of existing railway booking‐limit controls. Numerical experiments are conducted in various scenarios to evaluate the performance. The results show that the hybrid nesting control outperforms the others in all situations. In addition, the revenue improvement increases with the randomness of demand and discount percentage.

CHAIRS: A choice-based air transport simulator applied to airline competition and revenue management

In Revenue Management (RM) systems, information censoring and the interaction between the forecasting and optimization stages, increases the costs and complexity of performance analysis using historical data. An affordable and suitable alternative is using simulations, but appropriate behavioral models must be considered. In the following document, we discuss and test the implementation of a dynamic air transport market simulator, designed to analyze RM systems. The simulator replicates the behavior of passengers that book seats offered in multiple flights by different airlines. We use discrete choice models to replicate the demand behavior, accounting for preferences and decision rule heterogeneity, and including a temporal evolution of the preference throughout the selling horizon. To replicate the supply behavior, a number of airlines modify the price and quantity of different fare classes offered in each flight, using a variety of RM forecasting, un-constraining, and optimization techniques. The simulator allows analysts to study the economic benefit of RM systems under predefined assumptions in an artificial and controlled environment. This increases the benefits obtained by the correct selection of context-appropriate RM systems and the likelihood of successfully implementing new and complex systems. We test and showcase the simulator performance, studying the entrance of a new airline in a competitive context. We generate, implement and evaluate different RM strategies in response to the introduction of new competition, and discuss the results, highlighting the interpretability and accuracy of the proposed framework.

Seat assignment problem with the payable up-grade as an ancillary service of airlines

The de-bundling and re-bundling of service products offered by low cost carriers from service innovation have become 'à-la-carte' services and chargeable ancillary services to generate additional revenues in the airline industry. In this paper, we focus on the payable upgrade option in airlines to increase revenue when customers' demands are uncertain. This study is as an extension of planned upgrade, which guarantees the use of low-quality services at a low price, but requires customers to pay to use upgraded benefits. In this paper, we focus on the seat assignment problem with the payable upgrade option to maximize revenue. Also, we set the condition under which payable upgrade options can generate more revenue. Furthermore, applying Belobaba's EMSR approach, we suggest an effective seat assignment method for multiple fare classes with a payable upgrade option to increase the total revenue. With a simple numerical example, we find that introducing a payable upgrade option can increase the revenue. Our method for allocating seats for multiple fare classes with a payable upgrade option can contribute effectively to revenue increases in airlines' branded fare products, as well as in other service industries.

Scheduled service network design with revenue management considerations and an intermodal barge transportation illustration

The objective of the paper is to study the integration of revenue management considerations into service network design models targeting the tactical planning of intermodal consolidation-based freight transportation carriers. Revenue management strategies and mechanisms are broadly used within passenger transportation. Although identified as a desirable feature for freight transportation, interest growing within the industry, few contributions have addressed the topic. Moreover, almost none of those target the challenging issue of the interactions between the planning of the carrier’s services and operations, on the one hand, and the revenue-management strategy it could implement, on the other hand. We propose a new scheduled service network design model with resource and revenue management model, which selects the services and schedule to be repeatedly operated over the next season, allocates and routes the main resources supporting the selected services, and routes the demand flows between their respective origins and destinations. The objective of the model is the maximization of the expected net revenue of the carrier when several customer categories, with specific service requirements, as well as several tariff and operation classes are considered. Our interest goes beyond the modeling challenges raised by the problem setting, to exploring the impacts of this new approach on the decision types and on the structure of the service network solutions obtained. The results of extensive experiments, in terms of demand distribution, network topology, fare class and quality-of-service, provide a proof of concept of the proposed modeling framework and its capability for insightful analyses. Experimentation was conducted using an off-the-shelf software to solve the corresponding mixed-integer linear programming formulation for realistically dimensioned barge intermodal transportation instances.

IoT-Based Electric Vampire Remover to Overcome Electric Vampire On Electronic Equipment

Based on data from PLN, in 2020 the number of PLN customers has reached 77,19 million or increase of 3.59 million customers compared to 2019 which amounted to 73,6 million customers. Along with modernization in Indonesia, without realizing it there is still a lot of wasted electrical energy from electronic devices that are left on standby and not used or electric vampires. The purpose of this research created a tool to overcome the problem of electric vampires in electronic equipment in order to reduce the number of losses below the national electric losses of 8%. The implementation method used for the design and manufacture of the Electric Vampire Remover is the Research and Development (R&D) research method. The steps taken are: 1) analysis of tool requirements, 2) design of tool, 3) manufacture of tool in the laboratory, 4) testing of tool functions and performance, 5) concluding the results. These stages are conducted in cycles to get the best result. The result of the research is the creation of an Electric Vampire Remover which is functionally proven to be able to control electrical equipment properly. This tool can be operated stand-alone or based on internet network. The results showed that the tool was able to reduce losses caused by electric vampires by 99%. The application of this tool at home is able to save 36,908 kWh which is equivalent to Rp. 53,320.99/month in the fare class or R-1/1300 VA power.

Continuous pricing algorithms for airline RM: revenue gains and competitive impacts

Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such continuous pricing could increase revenues by extracting more of the consumer surplus, through its ability to offer more granular fares, closer to the customer’s willingness-to-pay (WTP). In this article, we describe several algorithms that lead to the quotation of a single fare from a continuous range. These algorithms either rely on traditional fare classes for the purpose of forecasting and optimization (class-based), or completely abandon the notion of fare classes, instead assuming different WTP distributions within each booking period prior to departure (classless). We describe how these algorithms build upon and differ from their traditional RM counterparts. Performance of these heuristics is then benchmarked against traditional class-based RM, and competitive impacts are analyzed when continuous pricing is adopted by one airline asymmetrically or both airlines symmetrically in a hypothetical 2-carrier network in the passenger origin–destination simulator (PODS). We find that continuous pricing is generally revenue-positive, and the revenue gains can be as high as 2.0% for the first-mover and reach up to 1.2% when both airlines adopt the new method. In addition, we show that these gains depend on the number of fare classes in the traditional fare structure used as a baseline, and that they are smaller under lower demand-to-capacity ratios.

Preference-Based Revenue Optimization for App-Based Lifestyle Membership Plans

The demand for a product is rooted in the consumers’ needs and preferences. Therefore, a pricing optimization model will be more valid if the demand function is represented under this basic notion. A preference-based revenue optimization model for an app-based lifestyle membership program is developed and solved in this research. The model considers competitor products and cannibalization effect from products in other fare-class, where both are incorporated using a preference-based demand function. The demand function was derived through a randomized first choice simulation that converts individual utility values into personal choices based on the random parameter logit model. Cannibalizing products are considered as competing products in the simulation scenario. In the pricing optimization, two and three fare classes based on the membership period are considered. The corresponding pricing optimization problem is a mixed-integer nonlinear programming problem with a solution-dependent objective function. Using enumeration, the three-fare-class optimal prices of Rp420,000, Rp300,000, and Rp60,000 for 12-month, 6-month, and 1-month membership, respectively, are better than those of the two-fare-class. Under this policy, the estimated total revenue is Rp30.56 billion, 41.74% greater than that of the current condition.

Premium cabin capacity sharing strategies: airline RM insights

This paper explores revenue management strategies for premium cabin capacity sharing, that is, accepting additional bookings in economy fare classes when some premium cabin seats are expected to remain empty. Two heuristics are presented and tested to evaluate the impacts of premium cabin capacity sharing on airline revenues and loads. These RM strategies are tested using the Passenger Origin Destination Simulation (PODS), which replicates the interactions between airline revenue management systems and passenger choice of airline itineraries, fare types and service offerings. Based on the simulations, the methodologies proposed can generate total revenue gains of up to 1.1%. However, two caveats are identified: first, losses in the revenue captured from premium fare classes are likely to be experienced due to displacement by economy fare class passengers. Second, premium cabin capacity sharing should only be implemented in the final stages of the booking process; otherwise, the sharing heuristics can result in revenue losses for the airline.

A deep reinforcement learning approach to seat inventory control for airline revenue management

Commercial airlines use revenue management systems to maximize their revenue by making real-time decisions on the booking limits of different fare classes offered in each of its scheduled flights. Traditional approaches—such as mathematical programming, dynamic programming, and heuristic rule-based decision models—heavily rely on external mathematical models of demand and passenger arrival, choice, and cancelation, making their performance sensitive to the accuracy of these model estimates. Moreover, many of these approaches scale poorly with increase in problem dimensionality. Additionally, they lack the ability to explore and “directly” learn the true market dynamics from interactions with passengers and adapt to changes in market conditions on their own. To overcome these limitations, this research uses deep reinforcement learning (DRL), a model-free decision-making framework, for finding the optimal policy of the seat inventory control problem. The DRL framework employs a deep neural network to approximate the expected optimal revenues for all possible state-action combinations, allowing it to handle the large state space of the problem. Multiple fare classes with stochastic demand, passenger arrivals, and booking cancelations have been considered in the problem. An air travel market simulator was developed based on the market dynamics and passenger behavior for training and testing the agent. The results demonstrate that the DRL agent is capable of learning the optimal airline revenue management policy through interactions with the market, matching the performance of exact dynamic programming methods. The revenue generated by the agent in different simulated market scenarios was found to be close to the maximum possible flight revenues and surpass that produced by the expected marginal seat revenue-b (EMSRb) method.

A fluid approximation for the single-leg fare allocation problem with nonhomogeneous poisson demand

Fare allocation for legs and O&D pairs plays a crucial role in airline revenue management. Despite a large number of dynamic pricing studies, there are only a few widely adopted studies in which assumptions affect most tactical decisions with potentially large impacts on airline profitability. These decisions involve approximating future pricing schemes, allocation of fare classes, and setting booking limits. We propose a fare allocation model for a single leg in the presence of a realistic nonhomogeneous Poisson demand with an increasing rate. We aim to compute when and how to markup the price for an airfare product (switch to an upper fare class) to maximize the expected revenue. We study a fluid approximation of the underlying stochastic problem considering independent demand from each customer segment and examine different properties that lead to several important insights. Finally, we propose a dynamic look-ahead pricing scheme to compare our fluid approximation results against the well-known EMSRb heuristic and a dynamic programming solution on randomly generated booking request data. Numerical examples illustrate the effectiveness of our proposed approach.

A network group booking model for airline revenue management

PurposeAirlines use various analytical techniques to optimally control seats in its various fare classes. Most literature on inventory control does not discuss policies for group arrivals. This study aims to address the literature gap in determining an optimal booking policy which considers the dynamics of group behaviour with respect to cancellations and refunds.Design/methodology/approachAn analytical model by which bid price control mechanism can be applied to group bookings is developed through this study.FindingsNumerical experiments are performed to demonstrate the analytical model under various scenarios. A comparison of the model with the traditional model indicates an increase in the total revenue generated.Originality/valueThe novelty of this work is in modelling the dynamics of group behaviour in a network of flights, and it contributes to the literature on revenue management.