Network revenue management with product-specific no-shows

Revenue management practices often include overbooking capacity to account for customers who make reservations but do not show up. In this paper, we consider the network revenue management problem with no-shows and overbooking, where the show-up probabilities are specific to each product. No-show rates differ significantly by product (for instance, each itinerary and fare combination for an airline) as sale restrictions and the demand characteristics vary by product. However, models that consider no-show rates by each individual product are difficult to handle as the state-space in dynamic programming formulations (or the variable space in approximations) increases significantly. In this paper, we propose a randomized linear program to jointly make the capacity control and overbooking decisions with product-specific no-shows. We establish that our formulation gives an upper bound on the optimal expected total profit and our upper bound is tighter than a deterministic linear programming upper bound that appears in the existing literature. Furthermore, we show that our upper bound is asymptotically tight in a regime where the leg capacities and the expected demand is scaled linearly with the same rate. We also describe how the randomized linear program can be used to obtain a bid price control policy. Computational experiments indicate that our approach is quite fast, able to scale to industrial problems and can provide significant improvements over standard benchmarks.Network revenue management, linear programming, simulation, overbooking, no-shows.

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

Delayed purchase options in single-leg revenue management

Many airline reservation systems offer the commitment option to their potential passengers. This option allows passengers to reserve a seat for a fixed duration before making a final purchase decision. In this study, we develop single-leg revenue management models that consider such contingent commitment decisions. We start with a dynamic programming model of this problem. This model is computationally intractable as it requires storing a multidimensional state space because of bookkeeping of the committed seats. To alleviate this difficulty, we propose an alternate dynamic programming formulation that uses an approximate model of how the contingent commitments behave and we show how to extract a capacity allocation policy from the approximate dynamic programming formulation. In addition, we present a deterministic linear programming model that gives an upper bound on the optimal expected revenue from the intractable dynamic programming model. As the problem size becomes large in terms of flight capacity and the expected number of arrivals, we demonstrate an asymptotic lower bound for the deterministic linear programming model. Our extensive numerical study indicates that offering commitment options can noticeably increase potential revenue even though offering a contingent commitment option may not always be in the best interest of the airline. Also, our results show that the proposed approximate dynamic programming model coordinates capacity allocation and commitment decisions quite well

Dynamic Capacity Control in Air Cargo Revenue Management

This book studies air cargo capacity control problems. The focus is on analyzing decision models with intuitive optimal decisions as well as on developing efficient heuristics and bounds. Three different models are studied: First, a model for steering the availability of cargo space on single legs. Second, a model that simultaneously optimizes the availability of both seats and cargo capacity. Third, a decision model that controls the availability of cargo capacity on a network of flights

New capacity allocation policies in revenue management

In this dissertation, we study three emerging problems in revenue management. First problem is about optimal capacity allocation in single-leg airline revenue management with overbooking. We propose new static and dynamic models. The static problems are difficult to solve optimally. Therefore, we introduce approximate models, which provide upper and lower bounds on the optimal expected revenues. In the dynamic case, we propose a model based on two streams of events; the arrivals of booking requests and cancellations. Following the characterization of the optimal policy, we also present the nested structure of the optimal allocations. Second problem is about optimal capacity allocation in the presence of a contingent commitment option. This option has been recently offered by airline systems to provide purchase flexibility to the customers. The problem becomes finding the revenue maximizing policy for contingent commitments and advance bookings. We first propose a dynamic programming model. Since it is computationally intractable, we develop an alternate dynamic model based on geometric approximation. In our numerical study, we investigate the effect of the commitment option on various test instances. In the third problem, we investigate optimal room allocation policies in hotel revenue management. Long-term stays are very common in hotel industry. Therefore, it is crucial to consider allocation of multiple-day capacities when responding to a request. This requirement leads to solving large-scale network problems, which are computationally challenging. Therefore, we work on various decomposition methods to find reservation policies for walk-in and stay-over customers. We also devise solution algorithms to solve large problems efficiently

Dynamic Capacity Control in Air Cargo Revenue Management

This book studies air cargo capacity control problems. The focus is on analyzing decision models with intuitive optimal decisions as well as on developing efficient heuristics and bounds. Three different models are studied: First, a model for steering the availability of cargo space on single legs. Second, a model that simultaneously optimizes the availability of both seats and cargo capacity. Third, a decision model that controls the availability of cargo capacity on a network of flights

A review of revenue management : recent generalizations and advances in industry applications

Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research

A Dynamic Programming Model to Optimize the Capacity Control with the Priority of Air Cargo

This paper concentrates on the problem of the air cargo space management strategy with a comprehensive, abstract and simplified way, on the basis of the actual characteristics of transport demand in China's air cargo market. We focus on the urgent transportation of goods and general cargo transport whose time requirements are different. The paper first proposes a single-leg cargo space management dynamic programming model according to the different time limit of different kinds of goods, and then the two dimensional single-leg air cargo problem is transformed into one dimensional two-leg airline network problem. After that, we use the expanded method of dynamic programming decomposition to solve the model. A numerical example is solved and simulated to verify the effectiveness of the program

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

Overbooking Network Slices through Yield-driven End-to-End Orchestration

Proceeding of: 14th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '18)Network slicing allows mobile operators to offer, via proper abstractions, mobile infrastructure (radio, networking, computing) to vertical sectors traditionally alien to the telco industry (e.g., automotive, health, construction). Owning to similar business nature, in this paper we adopt yield management models successful in other sectors (e.g. airlines, hotels, etc.) and so we explore the concept of slice overbooking to maximize the revenue of mobile operators. The main contribution of this paper is threefold. First, we design a hierarchical control plane to manage the orchestration of slices end-to-end, including radio access, transport network, and distributed computing infrastructure. Second, we cast the orchestration problem as a stochastic yield management problem and propose two algorithms to solve it: an optimal Benders decomposition method and a suboptimal heuristic that expedites solutions. Third, we implement an experimental proof-of-concept and assess our approach both experimentally and via simulations with topologies from three real operators and a wide set of realistic scenarios. Our performance evaluation shows that slice overbooking can provide up to 3x revenue gains in realistic scenarios with minimal footprint on service-level agreements (SLAs).This work was supported in part by the H2020 5G-Transformer Project under Grant 761536 and in part by H2020-MSCA-ITN-2015 5G-Aura Project under Grant 675806