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

Network Structures of Cargo Airlines - An Empirical and a Modelling Approach

The development of efficient air freight networks is an upcoming challenge. The present book approaches this problem for cargo airlines by characterising and classifying their network structures and by developing a model for an airline\u27s strategic network design. The book provides results which are of value for airline professionals (network efficiency analysis), policy makers (policy impact assessment) and researchers (cargo airline network design model)

Assessing the effect of proactive maintenance scheduling on maintenance costs and airline profitability: The case of Turkish Airlines Technic

This study mainly aims to examine the perception of Turkish Airlines Technic staff about the effect of proactive maintenance scheduling (PMS) on maintenance costs (MCs) and airline profitability(AP). Another purpose of the study is to reveal if perception of the staff changes according to demographic features and experience of the staff. As far as it is known, there is no direct study addressing the effects of PMS on MCs and airline profitability. The study is considered to be a first in this context. In the study, Turkish Airlines-THY was chosen as the company that was handled. The fact that the company has a separate special maintenance&repair group (Turkish Airlines Technic-THYT) was effective in the selection of this company. Some additional qualifications (very rooted, with corporate governance, Turkey's top largest&full service carrier, member of IATA and Star Alliance, and a listed company (in BIST) were also among other factors affecting the selection. Methodologically, it was used a quantitative survey with a sample of 133 THYT staff. Staff has been chosen among departments that takes place actively/directly in maintenance. Besides questions that aim to measure the perception of THYT staff on the effects of PMS to MCs and AP, some demographic and experience related questions were asked in order to find out if perception of the staff changes according to their qualifications. The data were collected by online survey method and analyzed with SPSS version 22. Necessary tests together with Chi-square and Fisher’s tests were carried out to analyze the results. It was concluded that there is a strong correlation between PMS and the MCs. The study proved that PMS has a great decreasing effect on MCs and a great increasing effect on AP. The study also revealed that THYT personnel is like-minded that PMS reduces MCs and increases AP as well

An Intelligent Decision Support System for the Empty Unit Load Device Repositioning Problem in Air Cargo Industry

Unit load devices (ULDs) are containers and pallets used in the air cargo industry to bundle freight for efficient loading and transportation. Mainly due to imbalances in global air transportation networks, deficits and surpluses of ULDs are the result and require stock balancing through the repositioning of (empty) ULDs. Following a design science research approach, we (1) elaborate the hitherto uninvestigated problem class of empty ULD repositioning (EUR) and (2) propose an intelligent decision support system (IDSS) that incorporates a heuristic for the given problem and combines artificial intelligence (i.e., rule-based expert system technology) with business analytics. We evaluate the IDSS with real-world data and demonstrate that the proposed solution is both effective and efficient. In addition, our results provide empirical evidence regarding the positive economic and ecological impact of leveraging the potential of ULD pooling in multi-carrier networks