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Strategies for managing the flexible capacity in the airline industry
Abstract
The ability to effectively match supply and demand under uncertainty can result in significant revenue benefits in the airline industry. We study the benefits of a Demand Driven Swapping (DDS) approach that takes advantage of the flexibilities in the system and dynamically swaps aircraft as departures near and more accurate demand information is obtained. We analyze the effectiveness of different DDS strategies, characterized by their frequency (how often the swapping decision is revised), in hedging against demand uncertainty. Swapping aircraft several weeks prior to departures will not cause much disturbance to revenue management and operations, but will be based on highly uncertain demands. On the other hand, revising the swapping decision later will decrease the possibility of bad swaps, but at a higher cost of disrupting airport services and operations. Our objective is to provide guidelines on how the flexible (swappable) capacity should be managed in the system. We study analytical models to gain insights into the critical parameters that affect the revenue benefits of the different swapping strategies. Our study determines the conditions under which each of the different DDS strategies is effective. We complement our analysis by testing the proposed DDS strategies on a set of flight legs, using data obtained from United Airlines. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.
... More recently, researchers have developed procedures that allow for relatively late changes in aircraft assignments without changing the crew schedule, by only swapping aircraft within the same aircraft family (e.g. Bish et al. 2004, Sherali et al. 2005). ...
... Flight schedules are developed a year prior to departure and updated every 3 months. Due to government regulations and contractual obligations, crew scheduling decisions are made well in advance of the departure date – e.g. 8 to 12 weeks out at United Airlines (Bish et al. 2004). Since aircraft assignments are an essential input into crew scheduling, these decisions must be made even earlier. ...
... Berge and Hopperstad 1993). More recently, researchers have developed procedures that allow for relatively late changes in aircraft assignments without changing the crew schedule, by only swapping aircraft within the same aircraft family (e.g. Bish et al. 2004, Sherali et al. 2005). Aside from demand-driven swapping of aircraft in the weeks prior to departure, on-the-fly modification of aircraft routes also occurs frequently on the day of departure itself. ...
We investigate the tradeoff between aircraft capacity utilization and on-time performance, a key measure of airline quality. Building on prior theory (Porter 1996, Schmenner and Swink 2004) and empirical work (Lapre and Scudder 2004) we expect that airlines that are close to their productivity or asset frontiers would face steeper tradeoffs between utilization and performance, than those that are further away. We test this idea using a detailed 10-year airline industry data set, drawing on queuing theory to disentangle the confounding effects of variability in travel time and capacity flexibility along an aircraft's route. In accord with and building on the findings of Lapre and Scudder (2004), we find that greater aircraft utilization results in higher delays, with this effect being worse for airlines that are close to their asset frontiers in terms of already being at high levels of aircraft utilization. Also, we find that the negative effect of utilization on delays is greater for aircraft that face higher relative variability in travel time along their routes, and is lower for aircraft on routes with higher capacity flexibility -in terms of the ability to substitute in a different aircraft for a particular flight than the one that was originally scheduled. Additionally, we examine how load factor, a measure of how full an airline's flights are and therefore a key revenue driver, affects on-time performance. Our analysis enables us to explain differences in on-time performance across airlines as a function of key operational variables, and to provide insight on how airlines can improve their on-time performance or their aircraft utilization.
... For a network having n nodes and m arcs, TalluriÕs algorithm has a running time of O(m) compared to the running time of O(mn) for Berge and HopperstadÕs method. From a different perspective, Bish et al. (2004) study the benefits of several demand driven swapping (DDS) mechanisms characterized in terms of their timing (when the swapping decision should be made) and frequency (how often the swapping decision should be revised). As with any re-fleeting decision, swapping aircraft early in time will cause less disturbance to revenue management and operations, but will be based on more uncertain demand. ...
... Note that the proposed DDS approach is intended to be implemented at close proximity to departures (i.e., six weeks) so as to incorporate the updated demand forecasts into the fleeting. Accordingly, Bish et al. (2004) consider swapping aircraft that are assigned to simple loops within similar time-frames where each such loop consists of a round-trip that originates and terminates at a common airport. Using both analytical models as well as numerical integration and simulation studies, they determine conditions under which each of the different DDS strategies is effective. ...
... Clearly, the initial fleeting decisions and the re-fleeting processes are highly dependent on each other. The initial fleeting decisions greatly constrain the downstream refleeting possibilities, in that it determines the flexibility with which the re-fleeting process can exploit (for example, most re-fleeting is limited to aircraft of the same family due to crew concerns; see Berge and Hopperstad (1993), Bish et al. (2004), and Sherali et al. (in press)). On the other hand, the subsequent re-fleeting prospects for performing revisions to the fleeting solution need to be considered upfront in the initial fleeting decision in order to retain a sufficient degree of flexibility in the system. ...
The fleet assignment problem (FAP) deals with assigning aircraft types, each having a different capacity, to the scheduled flights, based on equipment capabilities and availabilities, operational costs, and potential revenues. An airline’s fleeting decision highly impacts its revenues, and thus, constitutes an essential component of its overall scheduling process. However, due to the large number of flights scheduled each day, and the dependency of the FAP on other airline processes, solving the FAP has always been a challenging task for the airlines. In this paper, we present a tutorial on the basic and enhanced models and approaches that have been developed for the FAP, including: (1) integrating the FAP with other airline decision processes such as schedule design, aircraft maintenance routing, and crew scheduling; (2) proposing solution techniques that include additional considerations into the traditional fleeting models, such as considering itinerary-based demand forecasts and the recapture effect, as well as investigating the effectiveness of alternative approaches such as randomized search procedures; and (3) studying dynamic fleeting mechanisms that update the initial fleeting solution as departures approach and more information on demand patterns is gathered, thus providing a more effective way to match the airline’s supply with demand. We also discuss future research directions in the fleet assignment arena.
... An improvement of 1-5% in operating profits using D 3 is reported. Bish et al. (2004) further restrict re-fleeting to be aircraft swaps between two swappable loops, each of which consists of a round-trip originating and terminating at a common airport with similar time frames. Such a restriction ensures that the aircraft assigned to those routes can be swapped without violating aircraft flow balance. ...
... The work of Berge and Hopperstad (1993), Bish et al. (2004), and Sherali et al. (2005) are most pertinent to our research in a dynamic scheduling context. Because the re-optimization model applies re-fleeting and re-timing simultaneously, a brief review of re-fleeting and retiming in other areas of airline scheduling is presented below. ...
... Our schedule re-optimization model is a generalization of models presented in Berge and Hopperstad (1993) and Bish et al. (2004) with enhanced modeling aspects. The major distinction is that in our model, flight copies are created and scheduled near the original flight departure time to allow for flight leg re-timing. ...
Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights on departure have empty seats while others suffer a lack of seats to accommodate passengers who desire to travel. We approach this challenge, recognizing that demand forecast quality for a particular departure date improves as it approaches, by developing a dynamic scheduling approach that reoptimizes elements of the flight schedule during the passenger booking process. The goal is to match capacity to demand given the many operational constraints that restrict possible assignments. We leverage flight retiming as a new dynamic scheduling mechanism and develop a reoptimization model that integrates both flight retiming and refleeting. Our reoptimization approach, redesigning the flight schedule at regular intervals, uses information from both revealed booking data and improved forecasts available at later reoptimizations. We conduct experiments using data from a major U.S. airline and demonstrate that significant potential profitability improvements are achieved.
... Nevertheless, no matter how sophisticated these systems are, the stochastic nature of passenger demand still results in many flight legs having empty seats upon departure, while others suffer a lack of seats to accommodate passengers who desire to travel. Because forecast quality improves dramatically as the time of departure approaches (see Berge and Hopperstad 1993, Feldman 2000, Bish et al. 2004, and Sherali et al. 2005, an interesting question is how an airline can utilize improved demand forecasts to re-optimize the original schedule and move excess capacity to flight legs with a shortage of capacity. We focus on developing mechanisms to be used in matching supply and (fluctuating) demand, by making only small changes to the schedule so as to minimize the complication in operations. ...
... Using improved demand forecasts, the fleeting of flights in the schedule are adjusted later in the booking process to match improved demand forecasts. Berge and Hopperstad (1993) are the first to provide an in-depth presentation of this concept, providing implementation and performance evaluation details. Bish et al. (2004) and Sherali et al. (2005) later provide additional insights regarding dynamic re-fleeting approaches. ...
... The work of Berge and Hopperstad (1993), Bish et al. (2004), and Sherali et al. (2005) are most pertinent to our research in a dynamic scheduling context. Because the reoptimization model applies re-fleeting and re-timing simultaneously, a brief review of re-fleeting and re-timing in other areas of airline scheduling is presented below. ...
Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights have empty seats at departure, while others suffer a lack of seats to accommodate passengers who desire to travel. Recognizing that demand forecast quality for a particular departure date improves as the date comes close, we tackle this challenge by developing a dynamic scheduling approach that re-optimizes elements of the flight schedule during the passenger booking period. The goal is to match capacity to demand, given the many operational constraints that restrict possible assignments. We introduce flight re-timing as a dynamic scheduling mechanism and develop a re-optimization model that combines both flight re-timing and flight re-fleeting. Our re-optimization approach, re-designing the flight schedule at regular intervals, utilizes information from both revealed booking data and improved forecasts available at later re-optimizations. Experiments are conducted using data from a major U.S. airline. We demonstrate that significant potential profitability improvements are achievable using this approach.
... Most existing revenue management contributions endogenously adjust capacity in response to demand variation. Related concepts are termed demand driven dispatch [1], demand driven swapping [2], or dynamic capacity management [5]. ...
... They claim a resulting revenue improvement of 1-5%. Bish et al. [2] only allow swaps of two aircrafts within one aircraft family. Wang and Regan [8] also study aircraft swaps as an extension of leg-based revenue management, albeit from a perspective of continuous time. ...
In airline revenue management, capacity is usually assumed to be fixed. However, capacity changes are common in practice. This contribution quantifies the value of information when systematically considering possible capacity changes in revenue optimization. It solves a stochastic model that anticipates capacity changes, given different levels of information. A computational study compares solution approaches with respect to the resulting revenue, seat load factor, and denied boarding.
... It then tests delaying swaps until later in the booking process and changing the RM input capacities to the minimum and maximum possible. Bish et al (2004) develops strategies for managing flexible capacity, including what they term demand driven swapping. Sherali et al (2005) developed a polyhedral analysis and algorithms for re-fleeting; they restrict swaps between strings of flights that begin and end at the same airport at the same times. ...
... Sherali et al (2005) developed a polyhedral analysis and algorithms for re-fleeting; they restrict swaps between strings of flights that begin and end at the same airport at the same times. Jiang (2006) explored techniques for optimizing re-fleeting and de-peaking hub-and-spoke systems, primarily using D 3 models generalized by Berge and Hopperstad (1993) and Bish et al (2004) for its schedule re-optimization model. RM was not simulated. ...
Demand Driven Dispatch (D³) is the reassignment of aircraft to flights close to departure to improve operating profitability, primarily by utilizing improved knowledge of expected demand from the airline’s revenue management (RM) system. Previous studies of D³ have not incorporated competition and have typically ignored or significantly simplified RM. In this study, the implementation of D³ is tested with complete representations of RM systems in a network environment with competition. Results are from the Passenger Origin Destination Simulator, where stochastic demand by market chooses between competing airlines with alternative schedules and fare products. Findings include important competitive feedback effects from D³ and insights about relationships between D³ and both RM and pricing. Our findings indicate that the benefits of D³ can be estimated at operating profit gains of 0.04–2.03 per cent, revenue gains of 0.02–0.88 per cent, and changes in operating costs of −0.08 to 0.13 per cent. However, use of D³ may harm competitor airlines more than it aids the implementer. D³ swaps early in the booking process can lead to heavy dilution. Late swaps lead to smaller increases in loads but substantial increases in revenue. The relationship between revenue-maximization and cost-minimization in profit-maximizing D³ is highly influenced by the timing of swaps and revenue estimation.
... This reflects how demand driven dispatch has been implemented more on an ad hoc basis using decision support tools rather than in a systematic way. Bish, Suwandechochai, and Bish (2004) wrote on strategies for managing flexible capacity, including what they term demand driven swapping (DDS). They claim that swaps more than four weeks out will not disturb revenue management but utilize poorer forecasts, while swaps nearer in disrupt airline operations. ...
... They also cite United Airlines and Continental Airlines testing the swapping of aircraft for altered demand forecasts and that both airlines experienced significant gains as a result. Jiang (2006) explored techniques for optimizing re-fleeting and de-peaking hub-andspoke systems, primarily using demand driven dispatch models generalized by Berge and Hopperstad (1993) and Bish et al (2004) for its schedule re-optimization model. It also seeks to de-peak the hubs busiest times to increase flexibility for dynamic re-fleeting. ...
The focus of this thesis is on the integration of and interplay between demand driven dispatch and revenue management in a competitive airline network environment. Demand driven dispatch is the reassignment of aircraft to flights close to departure to improve operating profitability. Previous studies on demand driven dispatch have not incorporated competition and have typically ignored or significantly simplified revenue management. All simulations in this thesis use the PODS simulator, where stochastic demand by market chooses between competing airlines with alternative paths and fare products whose availability is determined by industry-typical revenue management systems. Demand driven dispatch (D³) is tested with a variety of methods and objectives, including a bookings-based method that assigns the largest aircraft to the flights with the highest forecasted demands. More sophisticated methods include revenue- and profit-maximizing fleet optimizations that directly use the output of leg-based and network-based RM systems and a minimum-cost flow specification. D³ is then tested with a variety of aircraft swap timings, RM systems, and competitive scenarios. Sensitivity testing is performed at a variety of demand levels, demand variability levels, and with an optimized static fleet assignment. Findings include important competitive feedbacks from D³, relationships between D³ and both revenue management and pricing, and important nuances to D³'s relationship with the level and variability of demand. Depending on how it is implemented, D³ may harm competitor airlines more than it aids the implementer. Early swaps in D³ lead to heavy dilution. Late swaps lead to smaller increases in loads but substantial increases in revenue. The relationship between revenue-maximization and cost-minimization in profit-maximizing D³ is highly influenced by the timing of swaps, revenue estimation, and demand levels. Finally, early swaps are susceptible to high variability of demand while late swaps are more robust. Findings indicate that the benefits of D³ can be estimated at operating profit gains of 0.04% to 2.03%, revenue gains of 0.02% to 0.88%, and changes in operating costs of -0.08% to 0.13%.
... Yao et al. (2008a) presented a methodology for efficiently scheduling available resources of a fractional jet management company. Bish et al. (2004) proposed a dynamic aircraft swapping approach, which took advantage of system flexibility and accurate forecasting of demands. Goto et al. (2004) developed and analysed a finite Markov model for the airline meal provisioning activity focusing on developing policies to determine and revise the number of meals to be uploaded. ...
... Airline industry Flight scheduling (Barnhart and Cohn, 2004;Kim and Barnhart, 2007;Lee et al., 2007;Yan et al., 2007;Abdelghany et al., 2008) Runway planning (Hansen, 2004;Pinol and Beasley, 2006;Atkin et al., 2007;Bäuerle et al., 2007) Fleet planning (Rosenberger et al., 2004;Sherali et al., 2005;Belanger et al., 2006;Grönkvist, 2006;Sherali et al., 2006;Smith and Johnson, 2006;Pilla et al., 2008;Haouari et al., 2009) Crew planning (Abdelghany et al., 2004;Freling et al., 2004;Hao et al., 2004;Kohl and Karisch, 2004;Sohoni et al., 2004;Guo et al., 2006;Xu et al., 2006;Lu cić and Teodorović, 2007;Yang et al., 2008) Terminal performance (Andreatta et al., 2007;Bazargan, 2007;Chu, 2007;Dorndorf et al., 2007;McLay et al., 2007;Drexl and Nikulin, 2008) Seat-inventory policy (Zhang and Cooper, 2005;Cooper and Gupta, 2006;Mukhopadhyay et al., 2007;Schipper et al., 2007;Currie et al., 2008;Marcus and Anderson, 2008;Zhang and Cooper, 2009) Revenue management (Bish et al., 2004;Liu and van Ryzin, 2008;van Ryzin and Vulcano, 2008;Yao et al., 2008a) Other operations (Goto et al., 2004) Strategic planning (Wen and Hsu, 2006) Evaluation (Ouellette et al., 2010) ...
The share of gross domestic product from the service industry reflects the competitiveness of a nation; the service industry in the USA accounts for around 80% of its gross domestic product, and it has been increasing gradually. Continual innovations and advances in enabling technologies for the service industry are crucial for developed countries to sustain their leading positions in the globalized economy. To clarify future research directions of operations research (OR) in the service industry, the state of art of OR has been examined systematically, the new requirements of OR are identified for its applications in service industries in comparison with those in manufacturing industries, and the limitations of existing methodologies and tools have been discussed. This paper was intended to provide an updated review on how OR has been applied in the service sector in recent years and what directions the study of OR will be carried forward in the near future. Under a proposed research framework, recent OR‐related articles were collected from 17 leading OR journals and classified into the five most active sectors, that is, transportation and warehousing, information and communication, human health and social assistance, retails and wholesales, and financial and insurance services. The conclusions on the limitations of existing studies and the demanding ORs in the service have been drawn from our summaries and observations from a comprehensive review in this field. Copyright © 2013 John Wiley & Sons, Ltd.
... For a number of years, logistics research in air transport focused more on cost reduction than on revenue generation (and its management) (see Ballou, 2006). With its focus on demand management (in effect, the need to balance customer requirements with supply chain capabilitiessee Croxton et al., 2002;Bish et al., 2004), revenue management sought to optimize the selling of commercial airline seats within a specific time. The importance of revenue management to the commercial airline industry stems from the reality that commercial airlines have operated their services almost solely on the basis that the customer is obliged to book for a service prior to its use (in effect, customers must make advanced bookings for flights). ...
This paper studies a voluntary overbooking model under rational expectation equilibrium to promote cooperation between customers and airlines, maintain goodwill of customers, and maximize the expected total returns to airlines. A decision tree analysis is constructed for both customers and airlines. Sensitivity analysis is conducted in both realistic and simulated no-show random variables for validation. The findings suggest considerable mutual benefits associated with a ‘voluntary overbooking’ policy that emphasizes mutual cooperation between passengers and commercial airlines. The main underlying assumption of the paper is that customers are willing to provide valuations to airlines seeking volunteers for overbooking. The originality of the proposed model is the incorporation of elements of the Rational expectations hypothesis into classical overbooking models gleaned from the literature.
... One of the first to propose implementing this concept using aircraft families are Berge and Hopperstad (1993). By reducing the problem, Bish et al (2004) only take swaps of two aircraft within one aircraft family into account, calling the approach ''demand driven swapping''. Wang and Regan (2006) also study aircraft swaps as an extension of leg-based revenue management, albeit from a perspective of continuous time. ...
Recently, resilience has emerged as a concept that describes a system’s ability to persist and adapt under uncertainty. Revenue management is a textbook example of planning under uncertainty – any revenue optimisation model relies on a range of assumptions, among them the accuracy of the demand forecast. Revenue management’s objective is to maximise revenue given uncertain market conditions, capacity, and even fares. This contribution reviews recent advances in making revenue management more resilient. To this end, it identifies and categorises uncertainties that affect the revenue management process. In the resulting framework, we review contributions aiming to increase solutions’ ability to persist or adapt, listing relevant references by their focus and character. Thereby, we contribute a comprehensive review of research accumulated in the last ten years, outline a research agenda, and thus prepare the ground for further research efforts.
... Capacity demand for air cargo operations is measured in terms of two primary dimensions: volume or size, and weight. Generally, such demand is articulated up-front by freight companies through an open-bidding system, in an operational process described in greater detail by Bish, Suwandechochai and Bish (2004) and Popescu et al. (2006). This bidding process for cargo capacity is dominated by a small number of large freight forwarding companies (Slager & Kapteijns 2004). ...
This article sought to facilitate the optimisation of key performance measures utilised for demand management in air cargo operations. The focus was on the Revenue Management team at Virgin Atlantic Cargo and a fuzzy group decision-making method was used. Utilising intelligent fuzzy multi-criteria methods, the authors generated a ranking order of ten key outcome-based performance indicators for Virgin Atlantic air cargo Revenue Management. The result of this industry-driven study showed that for Air Cargo Revenue Management, ‘Network Optimisation’ represents a critical outcome-based performance indicator. This collaborative study contributes to existing logistics management literature, especially in the area of Revenue Management, and it seeks to enhance Revenue Management practice. It also provides a platform for Air Cargo operators seeking to improve reliability values for their key performance indicators as a means of enhancing operational monitoring power
... Finally, the swappable resource flexibility, presented in Section 1.2, is much different from that studied in the academic literature. To our knowledge, research that studies the important factors that affect the value of swappable resource flexibility, as is done in this dissertation, is non-existent, with the exception of Bish, Suwandechochai, and Bish (2002), who analyze swappable resources in the airline industry, which operates quite differently from the service industries considered here. ...
... This sub-model is similar in all models and runs at the end of each period. There are various studies to show the choice of airplanes and capacity management (for example, Givoni and Rietveld, 2006;Bish et al., 2004). In order to keep the simplicity of the model, we assume that each airline buys its airplanes. ...
In network industries such as the airline industry competitions and collaborations between organisations shape the dynamics of the market significantly. The conditions under which firms choose to collaborate instead of competing are of particular importance in understanding the effects of regulatory actions within such industries. In this paper, an agent-based simulation and modelling approach is used to study the dynamics of competition and collaboration among airlines in the USA under different regulatory conditions and corporate strategic choices. The analysis is limited to a single competitive domestic flight corridor (New York City to Los Angeles). The results of the set of developed models show that both individual corporate strategies and government policies can have a significant impact on competition and collaboration dynamics of the system.
... Flight leg re-fleeting changes the fleet type assigned to a flight leg with higher than planned demand to a larger aircraft type, and the fleet type assigned to a flight leg with lower than planned demand to a smaller aircraft type while still maintaining aircraft flow balance. Representative literature describing flight leg re-fleeting can be found in [6][7][8]. In flight leg re-timing, flight departure and arrival times of a flight leg are altered to create new connecting itineraries through the hub to serve markets with higher than expected demands. ...
In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009) [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.
... Sherali et al. (2006) provide an extensive review of various fleet assignment models and algorithms. On the other hand, research in the areas of re-fleeting and aircraft swapping is relatively new, and includes the pioneering work of Berge and Hopperstad (1993), and some further research due to Talluri (1996), Bish et al. (2004), and Sherali et al. (2005). Smith and Johnson (2006) and Smith (2004) have shown that although limiting the number of different aircraft types serving a station (station purity) creates more re-fleeting or swapping opportunities, it significantly hinders the solution of the FAM. ...
An airline's fleet typically contains multiple aircraft families, each having a specific cockpit design and crew requirement. Each aircraft family contains multiple aircraft types having different capacities. Given a flight schedule network, the fleet assignment model is concerned with assigning aircraft to flight legs to maximize profits with respect to captured itinerary-based demand. However, because of related yield management and crew-scheduling regulations, in particular, this decision needs to be made well in advance of departures when market demand is still highly uncertain, although subsequently at a later stage, reassignments of aircraft types within a given family can be made when demand forecasts improve, while preserving crew schedules. In this paper, we propose a two-stage stochastic mixed-integer programming approach in which the first stage makes only higher-level family-assignment decisions, while the second stage performs subsequent family-based type-level assignments according to forecasted market demand realizations. By considering demand uncertainty up-front at the initial fleeting stage, we inject additional flexibility in the process that offers more judicious opportunities for later revisions. We conduct a polyhedral analysis of the proposed model and develop suitable solution approaches. Results of some numerical experiments are presented to exhibit the efficacy of using the stochastic model as opposed to the traditional deterministic model that considers only expected demand, and to demonstrate the efficiency of the proposed algorithms as compared with solving the model using its deterministic equivalent.
We consider a distribution logistics scenario where a shipping operator, managing a limited amount of resources, receives a stream of service requests, issued by a set of customers along a booking time-horizon, that are referred to a future operational period. The shipping operator must then decide about accepting or rejecting each incoming request at the time it is issued, accounting for revenues, but also considering resource consumptions. In this context, the decision process is based on dynamically finding the best trade-off between the immediate return of accepting the request and the convenience of preserving capacity to possibly exploit more valuable future requests. We give a dynamic formulation of the problem aimed at maximizing the operator revenues, accounting also for the operational distribution costs. Due to the “curse of dimensionality”, the dynamic program cannot be solved optimally. For this reason, we propose a mixed-integer linear programming approximation, whose exact or approximate solutions provide the relevant information to apply some commonplace revenue management policies in the real-time decision-making. Adopting a capacitated vehicle routing problem as an underlying distribution application, we analyze the computational behaviour of the proposed techniques on a set of academic test problems.
Most airline revenue optimization models assume capacity to be fixed by fleet assignment, and thus treat it as deterministic. However, empirical data show that on 40% of flights, capacity is updated at least once within the booking horizon. Capacity updates can be caused by fleet-assignment reoptimizations or by short-term operational problems. This paper proposes a first model to integrate the resulting capacity uncertainty in the leg-based airline revenue management process. While assuming deterministic demand, the proposed model includes stochastic scenarios to represent potential capacity updates. To derive optimal inventory controls, we provide both a mixed-integer program and a combinatorial solution approach, and discuss efficient ways of optimizing the special case of a single capacity update. We also explore effects of denied boarding cost and the model’s relationship to the static overbooking problem. We numerically evaluate the model on empirically calibrated demand instances and benchmark it on the established deterministic approach and an upper bound based on perfect hindsight. In addition, we show that the combinatorial solution approach reduces the computational effort. Finally, we compare the static overbooking approach derived from the capacity uncertainty model to existing approaches based on the expected marginal seat revenue (EMSR).
Purpose
– Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.
Design/methodology/approach
– A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.
Findings
– Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.
Originality/value
– The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.
A well-studied problem in airline revenue management is the optimal allocation of seat inventory among different fare-classes, given a capacity for the flight and a demand distribution for each class. In practice, capacity on a flight does not have to be fixed; airlines can exercise some flexibility on the supply side by swapping aircraft of different capacities between flights as partial booking information is gathered. This provides the airline with the capability to more effectively match their supply and demand. In this paper, we study the seat inventory control problem considering the aircraft swapping option. For theoretical and practical purposes, we restrict our attention to the class of booking limit policies. Our analytical results demonstrate that booking limits considering the swapping option can be considerably different from those under fixed capacity. We also show that principles on the relationship between the optimal booking limits and demand characteristics (size and risk) developed for the fixed-capacity problem no longer hold when swapping is an option. We develop new principles and insights on how demand characteristics affect the optimal booking limits under the swapping possibility. We also develop an easy to implement heuristic for determining the booking limits under the swapping option and show, through a numerical study, that the heuristic generates revenues close to those under the optimal booking limits. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011
Purpose
In order to hedge against fluctuation in actual “show” for air freight services, cargo airlines engage in the allocation of more space capacity than they actually have. This practice can lead to overbooking of capacity which can incur costs to the airline when a cargo does show that is larger than predicted. In this study, the authors set out to model an optimised value for air cargo booking which is tested against five different cargo case‐representative scenarios. The paper aims to discuss these issues.
Design/methodology/approach
The methodology which is primarily discursive utilises a synthesis of existing literature to develop a model to replicate the overbooking problem.
Findings
This paper finds that the optimised value of cargo size may not necessarily depend on the probability of actual “show”. Instead, this variation appears dependent on a random demand for larger sized cargo, and thus, price.
Practical implications
The model which is developed serves as a potential framework for airlines to avoid uncertainty associated with cargo capacity spoilage and overbooking.
Originality/value
In this study, the passenger overbooking model under the cancellation and no‐show problems in the static single leg case was adapted for use during the modelling of air cargo overbooking.
Generation reserves are needed to maintain the real time balance between power supply and power demand. Because power is noninventoriable, power generation follows power demand. Demand for power varies considerably depending on the time of day, day of the week and season. The predictable portion of power demand is met by purchasing firm energy on a day ahead or real time market. The random unpredictable portion of demand is met by purchasing a set of online and offline generation reserves on an ancillary market. The total energy purchasing cost includes payments for firm energy and payments for generation reserves. The latter include fixed capacity payments for reserve generation capacities and variable payments for the random energy produced from these reserves. The main contribution of this paper is to present an optimization model that captures the dynamism in the selection of the dispatch interval to determine the amount of firm energy and reserve capacities given a set of market prices. This is done by explicitly including in the model the duration of the dispatch period and the frequency this decision is reevaluated. In this model the randomness of the demand is captured by using a Doubly Truncated Normal Distribution. The cost incurred to activate generation reserves is modeled as a Poisson process. The total model captures the price differences from using different reserve sources. An empirical example is presented to illustrate the cost benefits of using the method proposed in this research with two different strategies: a static strategy and a dynamic strategy. It is shown that dynamically setting generation reserves results in cost savings.
The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems. These problems have several challenging characteristics, the two most challenging of which include: 1) they span long- and short-term horizons, from strategic planning of flight schedules operated several months into the future, to real-time operations in which strategies must be developed and implemented immediately to recover scheduled operations from disruptions; and 2) they include multiple resources that must be coordinated, such as aircraft, crews, and passengers. While optimization approaches have been essential to the airline industry and effective in developing aircraft and crew schedules, historical models and approaches often fail to capture the complexity of airline operations. For example, approaches, often by necessity, involve a sequential, rather than an integrated process to develop schedules for aircraft and crews, and moreover, the process involves simplifying assumptions, including that future demands are known and deterministic, and that schedules are operated as planned. In more recent research on airline schedule optimization, advances have led to new schedule optimization models and approaches that more accurately reflect reality. As described in this presentation, the most notable contributions to these advances include: 1. Integrated Aircraft and Crew Schedule Optimization Approaches in which some of the aircraft and crew schedule decisions previously taken sequentially are integrated, moving closer to producing globally optimal schedules; 2. Dynamic Scheduling Approaches in which schedules are adjusted during the passenger booking period to reflect increased knowledge of booking patterns and to increase the schedules associated total revenue; and 3. Robust Optimization Approaches in which the stochastic nature of airline operations is modeled and realized schedule performance is optimized. @InProceedings{barnhart_et_al:DSP:2009:2188, author = {Cynthia Barnhart and Hai Jiang and Lavanya Marla}, title = {OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery}, booktitle = {Models and Algorithms for Optimization in Logistics}, year = {2009}, editor = {Cynthia Barnhart and Uwe Clausen and Ulrich Lauther and Rolf H. M{"o}hring}, number = {09261}, series = {Dagstuhl Seminar Proceedings}, ISSN = {1862-4405}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, address = {Dagstuhl, Germany}, URL = {http://drops.dagstuhl.de/opus/volltexte/2009/2188}, annote = {Keywords: Airline aircraft and crew optimization, robust scheduling, dynamic scheduling} }
We consider a firm that uses two perishable resources to satisfy two demand types. Resources are flexible such that each resource can be used to satisfy either demand type. Resources are also indivisible such that the entire resource must be allocated to the same demand type. This type of resource flexibility can be found in different applications such as movie theater complexes, cruise lines, and airlines. In our model, customers arrive according to independent Poisson processes, but the arrival rates are uncertain. Thus, the manager can learn about customer arrival rates from earlier demand figures and potentially increase the sales by postponing the resource allocation decision. We consider two settings, and derive the optimal resource allocation policy for one setting and develop a heuristic policy for the other. Our analysis provides managerial insights into the effectiveness of different resource allocation mechanisms for flexible and indivisible resources.
The quality of an airline’s product is measured by its timeliness, accuracy, functionality, quality, and price. For the air transportation customers, these criteria translate into flexible schedules, on-time flights, safety, satisfactory in-flight services, proper baggage handling, reasonable prices, and convenient ticket purchases To provide this high-quality, low-cost product, airlines rely on optimization-based decision support systems to generate profitable and cost-effective fare classes, flight schedules, fleet plans, aircraft routes, crew pairings, gate assignments, maintenance schedules, food service plans, training schedules, and baggage handling procedures.
This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references. In the forty years since the first publication on overbooking control, passenger reservations systems have evolved from low level inventory control processes to major strategic information systems. Today, airlines and other transportation companies view revenue management systems and related information technologies as critical determinants of future success. Indeed, expectations of revenue gains that are possible with expanded revenue management capabilities are now driving the acquisition
A bundle algorithm is presented to solve a multicommodity network model for determining a recovery plan for a single carrier with multiple fleets following a hub closure. The algorithm is shown to provide feasible near-optical solutions much more quickly than can be obtained using a standard commercial mixed-integer programming, code (CPLEX). In this application, a bundle method is used to solve a Lagrangian relaxation of the integer programming formulation. The full algorithm includes heuristic techniques for finding feasible solutions from the solutions to the relaxed problems. Extensive computations were performed using data from Continental Airlines. The results show that the proposed approach provides faster times to optimality in some cases and always obtains feasible, near-optimal solutions for larger problems much more quickly than can be found using CPLEX. In addition, while a standard commercial code will provide only one solution, this approach provides multiple high-quality solutions.
The ability to effectively match supply and demand can result in significant revenue benefits in the airline industry, where supply and demand mismatches are common. We study the benefits of a decision postponement strategy for aircraft assignment under uncertainty. Our Demand Driven Swapping approach takes advantage of the inherent flexibilities in the system, and dynamically swaps aircraft as departures near and more accurate demand information is gathered. We analyze several swapping strategies characterized in terms of their frequency and timing. Our analytical results show that strategies that make the swapping decision early in time (so as to minimize disturbances to operations) perform very well on routes, where the demand uncertainty is low and the expected demands on the legs are well balanced. Otherwise, a swapping strategy, which revises the swapping decision over time, should be implemented. Our simulation results, based on real data obtained from United Airlines, confirm our analytical findings.
A major problem for the airline industry is the assignment of airplane capacity to flight schedules to meet fluctuating market needs. Demand Driven Dispatch (D3) is an operating concept that addresses this problem. Utilizing a demand forecast which improves as flight departure approaches, aircraft are dynamically assigned to flights to better match the predicted final demands. The result, demonstrated in studies of actual airline systems, is an increase in passenger loads and revenues with simultaneously reduced costs for a net of 1-5% improvement in operating profits. Concept implementation is simplified by the prevalence of yield management systems which provide the forecasting capability, and the emergence of airplane families which provide the necessary operational flexibility. Implementation also requires frequent solution of extremely large aircraft assignment problems. These problems, which can be formulated in terms of a multicommodity network flow, can be solved with heuristic algorithms shown to exhibit an accuracy and efficiency essential to successful concept implementation.
The schedule is an airline's primary product, having the most influence (along with price) on a passenger's choice of an airline. Once an airline decides (at least tentatively) on a schedule, a host of related problems have to be resolved before it can consider the schedule feasible, and can proceed to market the schedule. Among these problems are traffic forecasting and allocation that forecasts traffic on each flight leg for use in the fleet assignment model, fleet assignment that decides the fleet type of the aircraft flying the legs in the schedule, equipment swapping to change an assigned equipment type on a leg if and when necessary, through flight selection for determining which pairs of flights to market as one-stops (without any aircraft change), maintenance routing that develops aircraft rotations to provide adequate opportunities for overnight maintenance, and flight numbering to number flights as consistently as possible with a prior schedule. Considerable methodological and computational advances have been made in the recent past in developing models and solution methods for almost all of the problems mentioned above. In this paper we survey these various models and solution techniques.
In statistical decision theory, computations often involve the partial moments of a random variable. Several methods for determining partial moments are discussed, including direct calculation, the use of general formulas which apply to entire families of distributions, and the use of partial moment generating functions. Using these methods, formulas (some involving recursive relationships) are developed for the partial moments of a number of frequently encountered distributions and compared with the corresponding formulas for complete moments.
An airline’s schedule consists of a set of flight legs that it is scheduled to fly. A fleet assignment is an assignment of equipment types (such as a 757, 767, F100 etc.) to each of the flight legs. The fleet assignment is called a daily fleet assignment if the same assignment of equipment types is used every day of the week. The daily fleet assignment has to satisfy certain coverage, balance, and equipment availability constraints. Given a daily fleet assignment, we consider the problem of changing the assignment of a specified flight leg to a different equipment type while still satisfying all the constraints. We give a simple algorithm for making this swap that will not affect the equipment type composition of aircraft overnighting at the various stations. We describe two further applications of our swapping procedure in the airline schedule development process.
The current airline practice in conducting fleet assignments is to begin assigning aircraft capacity to scheduled flights well in advance of departures. However, the accuracy of the passenger demand forecast improves markedly over time, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. The demand-driven refleeting (DDR) approach proposed in this paper offers a dynamic reassignment of aircraft capacities to the flight network, when improved demand forecasts become available, so as to maximize the total revenue. Because of the need to preserve the initial crew schedule, this reassignment approach is limited within a single family of aircraft types and to the flights assigned to this particular family. This restriction makes it computationally tractable to include more relevant path-level demand information into the DDR model. Accordingly, we construct a mixed-integer programming model for this enhanced problem context and study its polyhedral structure to explore ways for tightening its representation and for deriving certain classes of valid inequalities. Various schemes for implementing such reformulation techniques are investigated and tested using a set of simulated and real instances obtained from United Airlines.
Disruptions in airline transportation systems can prevent airlines
from executing their schedules as planned. Adverse weather conditions,
congestion at airports, and mechanical failures often hinder a flight
schedule. During such events, decision makers must reschedule flight
legs, andr eroute aircraft, pilots, andpassengers. We present an
optimization model that reschedules legs and reroutes aircraft by
minimizing an objective function involving rerouting andcancellation
costs. We develop a heuristic for selecting which aircraft are rerouted,
and we provide proof of concept by evaluating our model using a simulation
of airline operations.Finally, we revise the model to minimize crew
and passenger disruptions.
0367 Pr(# of swaps ϭ 1) ϭ 0.0138 Pr(# of swaps ϭ 2) ϭ 0.7529 Pr(# of swaps ϭ 3) ϭ 0.0816 Pr(# of swaps ϭ 4) ϭ 0
- Pr
Pr(swap) 0.1011 0.8755 0.0542 0.0425 0.0367 Pr(# of swaps ϭ 1) ϭ 0.0138 Pr(# of swaps ϭ 2) ϭ 0.7529 Pr(# of swaps ϭ 3) ϭ 0.0816 Pr(# of swaps ϭ 4) ϭ 0.0198 Pr(# of swaps Ͼ 4) ϭ 0.0074 REFERENCES
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