Disentangling the European airlines efficiency puzzle: a network data envelopment analysis approach

© 2015 Elsevier Ltd. In recent years the European airline industry has undergone critical restructuring. It has evolved from a highly regulated market predominantly operated by national airlines to a dynamic, liberalized industry where airline firms compete freely on prices, routes, and frequencies. Although several studies have analyzed performance issues for European airlines using a variety of efficiency measurement methods, virtually none of them has considered two-stage alternatives - not only in this particular European context but in the airline industry in general. We extend the aims of previous contributions by considering a network Data Envelopment Analysis (network DEA) approach which comprises two sub-technologies that can share part of the inputs. Results show that, in general, most of the inefficiencies are generated in the first stage of the analysis. However, when considering different types of carriers several differences emerge - most of the low-cost carriers' inefficiencies are confined to the first stage. Results also show a dynamic component, since performance differed across types of airlines during the decade 2000-2010

Mathematical models for group revenue management

Revenue Management is a technique focus to decision rules for maximizing profit from sale of perish-able inventory units. This paper deals with the special case of hotel revenue management, which can be solved using deterministic and stochastic mathematical programming techniques. We first describe the problem with a theoreti-cal framework that sets the revenue maximization criteria for a hotel. We consider the general case of the problem that accept independent and group guests, with a general mixed integer linear programming model that maximize the total forecasting. Finally, we made comparisons be-tween different proposed models and were found good-quality solutions in short running times

Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Improved genetic algorithms by means of fuzzy crossover operators for revenue management in airlines

Abstract: Revenue Management is an economic policy that increases the earned profit by adjusting the service demand and inventory. Revenue Management in airlines correlates with inventory control and price levels in different fare classes. We focus on pricing and seat allocation problems in airlines by introducing a constrained optimization problem in Binary Integer Programming (BIP) formulation. Two BIP problems are represented. Moreover, some improved Genetic Algorithms (GAs) approaches are used to solve these problems. We introduce new crossover operators that assign a Fuzzy Membership Function to each parent in GAs. We achieve better outputs with new methods that take lower calculation times and earn higher profits. Three different test problems in different scales are selected to evaluate the effectiveness of each algorithm. This paper defines new crossover operators that help to reach better solutions that take lower calculation times and more earned profits

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

Business to business online revenue management.

With the emergence of the Internet, electronic commerce (e-commerce), revenue management and especially applications that combine both are becoming increasingly an area of innovation for service industries. E-commerce has introduced efficiencies across the service chain and it has allowed improvements to take place within and across organizations. Revenue management when combined with ecommerce and done online not only improves resource management but it can be used as a strategic tool to gain competitive advantage. This chapter examines the current approaches and future trends in these very exciting and promising areas

Dynamic Pricing through Sampling Based Optimization

In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm off ers multiple products to be sold over a fixed discrete time horizon. Each product sold consumes one or more resources, possibly sharing the same resources among di fferent products. The firm is given a fixed initial inventory of these resources and cannot replenish this inventory during the selling season. We assume there is uncertainty about the demand seen by the fi rm for each product and seek to determine a robust and dynamic pricing strategy that maximizes revenue over the time horizon. While the traditional robust optimization models are tractable, they give rise to static policies and are often too conservative. The main contribution of this paper is the exploration of closed-loop pricing policies for di fferent robust objectives, such as MaxMin, MinMax Regret and MaxMin Ratio. We introduce a sampling based optimization approach that can solve this problem in a tractable way, with a con fidence level and a robustness level based on the number of samples used. We will show how this methodology can be used for data-driven pricing or adapted for a random sampling optimization approach when limited information is known about the demand uncertainty. Finally, we compare the revenue performance of the di fferent models using numerical simulations, exploring the behavior of each model under diff erent sample sizes and sampling distributions.National Science Foundation (U.S.) (Grant 0556106-CMII)National Science Foundation (U.S.) (Grant 0824674-CMII)Singapore-MIT Allianc

Carve-Outs under Airline Antitrust Immunity

This paper offers the first formal economic analysis of carve-outs under airline antitrust im- munity. Carve-outs are designed to limit the potential anticompetitive effects of cooperation by alliance partners in hub-to-hub markets, where they provide overlapping nonstop service. While the paper shows that carve-outs are beneficial when the alliance does not involve full integration of the partners’ operations on the hub-to-hub route, its key point is that a carve-out may be harmful when imposed on a joint-venture alliance. A JV alliance involves full exploitation of economies of traffic density on the hub-to-hub route, and a carve-out prevents the realization of these benefits. While a carve-out may limit anticompetitive incentives on the hub-to-hub route, welfare may be reduced if the resulting gains are overshadowed by the efficiency loss generated by the carve-out.carve-out, alliance, antitrust immunity, airlines