Research on Multi-leg Inventory Control Based on Passenger Choice

To remedy the lack of mathematical programming and the Expected Marginal Seat Revenue (EMSR) model for multi-leg seat inventory control, this paper proposes a method based on passenger choice. Except for data about which seats passengers decide to opt for, there is no need to obtain distributions of passengers’ demands or other “a priori” information. The proposed method can discover the real factors that affect passengers’ choices, and then estimate the probabilities of seat choices and the revenue according to the weights of the factors. Simulated experiments and comparison with the shadow price method and the virtual “bucket” method confirm the feasibility and effectiveness of the proposed method in seat inventory control for multi-leg flights

An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer set, and the firm decides the best subset to offer at any given moment to maximize expected revenue. The resulting dynamic program for the firm is intractable and approximated by a deterministic linear program called the CDLP which has an exponential number of columns. However, under the choice-set paradigm when the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has been proposed but finding an entering column has been shown to be NP-hard. In this paper, starting with a concave program formulation called SDCP that is based on segment-level consideration sets, we add a class of constraints called product constraints (σPC), that project onto subsets of intersections. In addition we propose a natural direct tightening of the SDCP called ESDCPκ, and compare the performance of both methods on the benchmark data sets in the literature. In our computational testing on the benchmark data sets in the literature, 2PC achieves the CDLP value at a fraction of the CPU time taken by column generation. For a large network our 2PC procedure runs under 70 seconds to come within 0.02% of the CDLP value, while column generation takes around 1 hour; for an even larger network with 68 legs, column generation does not converge even in 10 hours for most of the scenarios while 2PC runs under 9 minutes. Thus we believe our approach is very promising for quickly approximating CDLP when segment consideration sets overlap and the consideration sets themselves are relatively small

An Optimization Technique of Airlines\u27 Seat Inventory Management

This study consisted of a simulation to maximize an airline\u27s Origin- Destination revenues. It has been hypothesized that Linear Programming is capable of maximizing airlines\u27 networks revenues. Simulation was performed with Linear Interactive Discrete Optimizer (LINDO). This project was designed to consider different fares and its classes along with multi flight connections. The seats\u27 allocation process came out with the maximum possible revenues, and enough flexibility in terms of changing such allocation to work around competition and trends. Results came supportive to the hypothesis. Sensitivity analysis provided our model with a tool to modify and change different variables, like fares, without affecting the reached goal (maximized network revenues). Although, the utilized network is a portion of a real world one, this study should inspire revenue management departments build their own simulation based on this model. Conclusion and recommendations are submitted

Dynamic pricing under customer choice behavior for revenue management in passenger railway networks

Revenue management (RM) for passenger railway is a small but active research field with an increasing attention during the past years. However, a detailed look into existing research shows that most of the current models in theory rely on traditional RM techniques and that advanced models are rare. This thesis aims to close the gap by proposing a state-of-the-art passenger railway pricing model that covers the most important properties from practice, with a special focus on the German railway network and long-distance rail company Deutsche Bahn Fernverkehr (DB). The new model has multiple advantages over DB’s current RM system. Particularly, it uses a choice-based demand function rather than a traditional independent demand model, is formulated as a network model instead of the current leg-based approach and finally optimizes prices on a continuous level instead of controlling booking classes. Since each itinerary in the network is considered by multiple heterogeneous customer segments (e.g., differentiated by travel purpose, desired departure time) a discrete mixed multinomial logit model (MMNL) is applied to represent demand. Compared to alternative choice models such as the multinomial logit model (MNL) or the nested logit model (NL), the MMNL is significantly less considered in pricing research. Furthermore, since the resulting deterministic multi-product multi-resource dynamic pricing model under the MMNL turns out to be non- linear non-convex, an open question is still how to obtain a globally optimal solution. To narrow this gap, this thesis provides multiple approaches that make it able to derive a solution close to the global optimum. For medium-sized networks, a mixed-integer programming approach is proposed that determines an upper bound close to the global optimum of the original model (gap < 1.5%). For large-scale networks, a heuristic approach is presented that significantly decreases the solution time (by factor up to 56) and derives a good solution for an application in practice. Based on these findings, the model and heuristic are extended to fit further price constraints from railway practice and are tested in an extensive simulation study. The results show that the new pricing approach outperforms both benchmark RM policies (i.e., DB’s existing model and EMSR-b) with a revenue improvement of approx. +13-15% over DB’s existing approach under a realistic demand scenario. Finally, to prepare data for large-scale railway networks, an algorithm is presented that automatically derives a large proportion of necessary data to solve choice-based network RM models. This includes, e.g., the set of all meaningful itineraries (incl. transfers) and resources in a network, the corresponding resource consumption and product attribute values such as travel time or number of transfers. All taken together, the goal of this thesis is to give a broad picture about choice-based dynamic pricing for passenger railway networks

A study on the dynamic refund fee model of air tickets based on win-to-win mechanism

Purpose: To achieve the balancing of interests between airlines and passengers, the dynamic refund fee model is designed, which meets the interests of both airlines and passengers. Design/methodology/approach: In the study, the expectation utility function is constructed respectively for passengers and airlines, and the equilibrium point of the two is chosen to solve the passenger refund fee. Then, a dynamic refund fee model based on refund time and real-time sales revenue is proposed according to the actual operating conditions of the flight. Findings:The results show that refund fee is negatively related to three variables, the ticket price, the probability of ticket sales and the probability of refund. Besides, The maximum payment limit is the airline single seat cost or the actual fare paid below it, and the lowest can be exempt. Originality/value: A better standard of the rufund is proposed. The refund mode breaks the traditional static charging mode, and charges different refund fees to passengers for different refund time and the real time income of flights. Comparing with the current refund policy, the new charge of refund fee could best meets the interests of both parties.a refund fee standard.Peer Reviewe

Technology revenue management system for customer groups in hotels

This paper discusses revenue management; a technique that focuses on decision making that will maximize profit from the sale of perishable inventory units. New technologies management plays an important role in the development of revenue management techniques. Each new advance in technology management leads to more sophisticated revenue business capabilities. Today decision support revenue management systems and technologies management are crucial factors for the success of businesses in service industries. This paper addresses the specific case of customer groups in hotels.The paper introduces a new decision support system that sets the revenue maximization criteria for a hotel. The system includes a set of forecasting demand methods for customers. It addresses a general case considering individual guests and customer groups. The system also incorporates deterministic and stochastic mathematical programming models that help to make the best decisions. The actual revenue depends upon which reservation system the hotel uses. A simulation engine makes a comparison between different heuristics of room inventory control: the results include performance indexes such as occupancy rate, efficiency rate, and yield; it compares results and chooses one of them. The system proves its suitability for actual cases by testing against actual data and thus becomes an innovative and efficient tool in the management of hotels’ reservation systems

An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer-set, and the firm decides the best subset to offer at any given moment to maximize expected revenue. The resulting dynamic program for the firm is intractable and approximated by a deterministic linear program called the CDLP which has an exponential number of columns. However, under the choice-set paradigm when the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has been proposed but finding an entering column has been shown to be NP-hard. In this paper, starting with a concave program formulation based on segment-level consideration sets called SDCP, we add a class of constraints called product constraints, that project onto subsets of intersections. In addition we propose a natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methods on the benchmark data sets in the literature. Both the product constraints and the ?SDCP method are very simple and easy to implement and are applicable to the case of overlapping segment consideration sets. In our computational testing on the benchmark data sets in the literature, SDCP with product constraints achieves the CDLP value at a fraction of the CPU time taken by column generation and we believe is a very promising approach for quickly approximating CDLP when segment consideration sets overlap and the consideration sets themselves are relatively small.discrete-choice models, network revenue management, optimization

The Forty-year History of Revenue Management: Bibliometric Analysis

This paper presents research trends, leading publishers, influential articles, and shifting concerns in the field of Revenue Management for over forty years based on bibliometric analysis. Bibliometric data was retrieved from Web of Science core collection with a well-defined strategy. The data was processed using Network Analysis Interface for Literature Studies Project scripts. Subject-wise and year-wise research trends were presented. The shifting concerns in RM in terms of topic, method, and domain were highlighted using keyword analysis. In general, RM showed an increasing number of published papers with exponential manner every year. The research core in RM covered the three major decisions in RM including pricing, quantity control, and structural decision. It was highlighted that RM’s concern has shifted from single-firm decision to be more consumer- and competition-centric. The data showed that the needs of empirical study and more advanced quantitative methods for complex and real-time problems were urged. In addition, the adoption of RM was extended for industries with semi-flexible capacity. The top influential publishers were Decision Sciences, Operations Research, Management Science, and Management Science Manufacturing & Service Operations Management

Dynamic Capacity Allocation for Airlines with Multi-Channel Distribution

In 2017, China's online e-commerce sales has already reached 29.16 trillion Yuan. While gaining huge benefits, it also poses great challenges for each industry. One of the biggest challenge is the change of sales channels. Of course, there are also huge opportunities between them. Among them, it is a huge impact on the perishable products industry such as the airline industry and the hotel industry. Because of the perishable of the product, both hotels and airlines want to be able to sell the product for a limited period of time and gain considerable benefits. Therefore, at the beginning of the industry, airlines and hotels hoped to sell their products through more channels and attracted channels to sell products by paying their commission fee. With the rapid development of e-commerce, more and more online channels are replacing traditional offline channels. The change of channels has brought great challenges to airline management and costs. For example, although online channels absorb more customer demand, the commission costs of airlines have increased significantly. In addition to the cost pressures imposed on airlines, the increase in channels has brought conflicts between channels and between channels and airlines. Some of the channels' behaviour has caused great losses to the airlines. For example: change the condition of retreat fee, increase ticket or room price, maliciously reduce the price to compete with the airline and so on. These behaviours have affected the airline's reputation and have also brought losses to the airlines. In order to deal with the challenges of online agents, the airline has also taken some corresponding measures, such as the opening of online direct marketing websites, direct sales APP and so on. However, the effect has not been very good and it is difficult to compete with online agents who have customer volume. At the same time, we also see that the airline industry and hotels are also facing great competitive pressure. For example, the high-speed rail increases the competitive of civil transport markets. High-speed trains generally have higher on-time rates than aircraft and also high-speed rail stations are generally more convenient for customers in the city. Therefore, for passengers, high-speed rail has advantages in short trips. In addition, the emergence of low-cost airlines has also intensified competition in the civil aviation industry such as China's Spring Airlines, Europe's Easyjet and Ryanair. Therefore, recently reducing channel distribution costs has been concerned for many airlines which are facing fierce competition in airline markets. In a long period since the 1970s, capacity control has always played a pivotal role in defining airlines market strategy. However, when airlines select distribution channels and make capacity allocation decisions, they still separately make different decisions. Hence, when a customer purchases a ticket from a channel with an appropriate fare class, the channel might not be an optimal channel from the airlines' perspective. When the airline sells a ticket in a right channel, the ticket price is probably not a right fare. Therefore, how to establish a better channel and fare class capacity control model has become the key for airlines to increase revenue. This thesis is a study based on the above issues. The main work includes the following aspects: At first, we studied the single-leg capacity allocation problem that considers the channel factor. Although the network revenue management has a lot of academic research and has been applied in international routes, for many domestic routes airlines still basically use single-leg revenue management system. In addition, from the historical development of revenue management, the single-leg revenue management model is the basic model of all revenue management models. Therefore, it is important to first establish a single-leg revenue management model that considers the channel issues. In this study, we will integrate channel distribution into dynamic capacity control model. The model can make channel decisions in conjunction with inventory and this is similar to the procedure shown in pure capacity allocation. The study has proposed an optimal policy basing on bid price that incorporates commission fee, price, and capacity. The numerical experiment results illustrate that introducing the channel distribution into airline revenue system can significantly improve the revenues and efficiently reduce the channel distribution cost for airlines. The numerical experiments demonstrate that airline revenues will increase more than 3% in a simple integrated system with two channels compared to the independent model. This study also analyses the reasons for improvements in different situations (such as multi-channels have better improvements than a single-channel and the model has a better match of channels and fare classes) so that management insights are obtained for airlines. Secondly, we analyse customer demand behaviours and we find that customers will experience demand transfer behaviours when facing channels. In the Internet age, due to more transparent information, the customer's transfer behaviour has been continuously expanded. For customers, the transfer of channels is more likely to occur than the transfer of fare classes because they do not need to pay for it. Therefore, it is necessary to establish a better revenue management model to consider the customer's channel transfer behaviour.In this part, we added customer channel transfer behaviour based on the original single-leg dynamic capacity allocation model that considers channel issues. We also developed the optimal policy for this model and made some numerical experiments. The numerical experiments demonstrate that the customer shift behaviour can influence the results of the model and subsequently the decisions of airlines. In the general numerical result, the new model can increase 1.23% than the above channel model. At the same time, through the analysis of the results, the airlines are provided with corresponding suggestions to face the customer's choice behaviour. For example, the airline needs to increase the customer's transfer rate through some methods, such as joining a price comparison network and increasing policy incentives. Thirdly, on the base of single-leg model, we propose a new network dynamic model to integrate network revenue management and channel distribution. To take a network structure airline, the airlines can make more revenue benefits comparing the single-leg method. Although the network dynamic model can make more improvements, the exact optimization is impossible for practical purposes because of the curse of dimensionality. Therefore, we use determined linear programming method for approximating to dynamic model. The numerical experiments demonstrate that the airline revenues can increase more than 3% in a simple network when the commission rate is 15% compared to the traditional network model. In addition to the above studies, the paper also summarizes the original literature on revenue management and channel issues and proposes future research directions