| Author: | Wen, Xin |
| Title: | Air logistics operations : cabin crew scheduling and risk analysis |
| Advisors: | Chung, S. H. Nick (ISE) Choi, Tsan-ming Jason (ITC) Ji, Ping (ISE) |
| Degree: | Ph.D. |
| Year: | 2019 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Aeronautics, Commercial -- Freight Airlines -- Management Flight crews Business logistics |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | 157 pages : illustrations |
| Language: | English |
| Abstract: | The air logistics industry is playing a crucial role in the modern world through facilitating both passenger and cargo movement nationally and internationally. However, this industry is characterized by fierce competition, high operating costs, and diverse uncertainties. Therefore, air logistics operators are committed to improving decision quality for both passenger and cargo logistics to maintain profitability in the risky and competitive market. Among the air logistics management issues, the operational scheduling problems for air passenger logistics and strategic pricing strategies for air cargo logistics are the critically important and challenging decisions. Therefore, focusing on these two areas, this thesis aims at enhancing the operational and strategic decision making for modern airlines in the current volatile business environment. As cabin crews are crucial resources for airlines which are relatively under-studied compared to cockpit crews, this research firstly concentrates on improving the operational cabin crew scheduling methodologies by proposing a new practical pairing approach from the perspective of air passenger logistics operations. Then, the strategic risk-averse pricing decisions are investigated where the mean-variance theory is utilized for risk analysis from the perspective of air cargo logistics operations. Regarding the air passenger logistics operations, cabin crew scheduling is one of the most important but challenging operational scheduling problems faced by airlines, which is decomposed into a cabin crew pairing problem and a cabin crew assignment problem. Due to the high complexity and large scale of the problem, cabin crews are usually scheduled on a team basis separated by aircraft types as cockpit crews for simplicity. However, the cross-qualification of cabin crews and the manpower configuration heterogeneity of various flights make the scheduling problem for cabin crews totally different from that for cockpit crews. Besides, some airlines are adopting the individual cabin crew pairing approach, and applying the strategy of controlled crew substitution to hedge against the manpower requirement variation caused by flight fluctuation in the uncertain market. Motivated by the emergence of individual cabin crew pairing practice as well as the shortcomings of the team-based cabin crew pairing scheme, this research conducts an analytical study which aims at improving manpower utilization while reducing costs by utilizing a new individual cabin crew pairing generation approach. The impacts of the relationship between manpower availability with requirement benchmarks on cabin crew scheduling strategies are investigated to derive deep insights regarding airline crew management in the volatile market. A customized column generation approach is developed to solve the problem. Computational experiments based on real-world collected flight schedules data demonstrate the advantages of the proposed approach over the existing team-based method, such as substantially improving manpower utilization by 199% and reducing cost by 61%. Furthermore, the proposed pairing approach shows great potential in alleviating the negative impact of flight fluctuation. On the other hand, the strategic pricing decisions for air cargo carriers are extremely challenging due to the intensive market competition and diverse uncertainties arising from both market demand and operating costs. However, this problem is rather under-explored in the literature. It is reasonable that many freight airlines are holding risk-averse attitudes in decision making in order to survive in the highly volatile and competitive market. Therefore, in this thesis, the mean-variance theory is applied to characterize the risk-averse behaviors of decision makers, and the equilibrium prices for two competing risk-averse air cargo carriers under demand and cost uncertainties are derived. Then, how the crucial factors like risk sensitivity coefficients, market competition, market share, demand uncertainty and cost uncertainty affect the airlines' optimal prices is studied. In addition, important cost thresholds and relative risk-averse attitude thresholds are identified for the impacts of these factors on the equilibrium prices. The analytical results derived from this research demonstrate the symmetry in the optimal prices and critical thresholds for the two carriers. Besides, the importance to consider both carrier's own and the competitor's risk attitudes and operating characteristics in decision making when market competition exists is highlighted. Moreover, the direct and indirect impacts of risk attitudes on the optimal prices are identified, thus highlighting the importance to integrate risk considerations into the optimal pricing decision framework. Finally, it is found that market situations play a critical role in characterizing the effects of diverse parameters on the equilibrium prices, which should be carefully evaluated by decision makers. To conclude, realizing the importance of improving the decision making for air logistics operations in the highly uncertain and competitive market, this research conducts the series of research described in this thesis. Specifically, a new individual cabin crew pairing generation approach which demonstrates superior performances in manpower utilization improvement and cost reduction for air passenger logistics operations is developed. Moreover, this study also conducts an analytical risk analysis for air cargo logistics operations, and explores the optimal pricing strategies for freight airlines facing diverse uncertainties through the application of the mean-variance theory. The insights derived from this thesis research not only contribute to the air logistics management literature, but they also provide valuable guidance to practitioners such as operations managers in airlines. |
| Rights: | All rights reserved |
| Access: | open access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022289513003411.pdf | For All Users | 2.24 MB | Adobe PDF | View/Open |
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