When a customer requests a discount fare, the airline must decide whether to sell the seat at the requested discount or to hold the seat in hope that a customer will arrive later who will pay more. I model this situation for a single leg flight with multiple fare classes and customers who arrive according to a semi-Markov process (possibly nonhomogeneous). These customers can request multiple seats (batch requests) and can be overbooked. Under certain conditions, I show that the value function decreases as departure approaches. If each customer only requests a single seat or if the requests can be partially satisfied, then I show that there are optimal booking curves which decrease as departure approaches. I provide counterexamples to show that this structural property of the optimal policy does not hold in general. When customers are allowed to cancel I show that booking curves exist and may be monotone in certain cases. I also consider the situation where the customer's request size and fare offered are not known, but their joint probability distribution is available, and show that under certain conditions existence of booking curves obtains, and that under further assumptions, they are monotone. Finally, the theoretical results are used in realistic numerical examples, which are compared to certain deterministic upper bounds and revenues obtained under heuristic policies. The airline yield management problem described above is an instance of a generic revenue management problem, which, in turn, can be cast into a finite horizon semi-Markov dynamic optimal control problem. I provide examples of other applications of revenue management.

This thesis is concerned with measuring and explaining the productive efficiency of firms or organizations. In particular, the study compares three alternative methods of measuring efficiency, namely, the deterministic frontier method, the stochastic frontier method, and the data envelopment analysis method (DEA). The dissertation consists of two parts. In Part I, the relative merits of the three methods are discussed and evaluated through a Monte Carlo study under certain known conditions. The study focuses on the effects of exogenous variables on efficiency estimates. The results show that the stochastic frontier method generally produces better efficiency estimates than the other two methods. The DEA, however, has a slight advantage in cases where there are weak input substitution and large variations in input variables. In Part II, the three methods are examined empirically through their applications to a panel of 19 railways in OECD countries and a panel of 36 international airlines. Comparison of the three sets of efficiency estimates confirms that on average the stochastic frontier method yields higher efficiency estimates than the other two methods, as indicated by the Monte Carlo results. The efficiency estimates by the two parametric methods are highly correlated, whereas there are considerable differences between the DEA estimates and those from the parametric methods. This is also consistent with the Monte Carlo results. By comparing the alternative efficiency estimates in the two applications, it is found that there is less discrepancy among the three sets of efficiency estimates in the airline case than in the railway case. This can be partly attributed to the fact that there are fewer variations in the operating environments in the airline case than in the railway case. The simulation results in Part I provide some general guidelines regarding the relative merits of the three alternative methods under certain known conditions. The two applications of the three methods in Part II serve as examples of how these three methods can be applied to practical problems where no a priori knowledge of either the production technology nor the efficiency profile exists. They illustrate some of the problems that may be encountered in empirical applications.

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When a customer requests a discount fare, the airline must decide whether to sell the seat at the requested discount or to hold the seat in hope that a customer will arrive later who will pay more. I model this situation for a single leg flight with multiple fare classes and customers who arrive according to a semi-Markov process (possibly nonhomogeneous). These customers can request multiple seats (batch requests) and can be overbooked. Under certain conditions, I show that the value function decreases as departure approaches. If each customer only requests a single seat or if the requests can be partially satisfied, then I show that there are optimal booking curves which decrease as departure approaches. I provide counterexamples to show that this structural property of the optimal policy does not hold in general. When customers are allowed to cancel I show that booking curves exist and may be monotone in certain cases. I also consider the situation where the customer's request size and fare offered are not known, but their joint probability distribution is available, and show that under certain conditions existence of booking curves obtains, and that under further assumptions, they are monotone. Finally, the theoretical results are used in realistic numerical examples, which are compared to certain deterministic upper bounds and revenues obtained under heuristic policies. The airline yield management problem described above is an instance of a generic revenue management problem, which, in turn, can be cast into a finite horizon semi-Markov dynamic optimal control problem. I provide examples of other applications of revenue management. / Business, Sauder School of / Graduate

This thesis is concerned with measuring and explaining the productive efficiency of firms or organizations. In particular, the study compares three alternative methods of measuring efficiency, namely, the deterministic frontier method, the stochastic frontier method, and the data envelopment analysis method (DEA). The dissertation consists of two parts. In Part I, the relative merits of the three methods are discussed and evaluated through a Monte Carlo study under certain known conditions. The study focuses on the effects of exogenous variables on efficiency estimates. The results show that the stochastic frontier method generally produces better efficiency estimates than the other two methods. The DEA, however, has a slight advantage in cases where there are weak input substitution and large variations in input variables. In Part II, the three methods are examined empirically through their applications to a panel of 19 railways in OECD countries and a panel of 36 international airlines. Comparison of the three sets of efficiency estimates confirms that on average the stochastic frontier method yields higher efficiency estimates than the other two methods, as indicated by the Monte Carlo results. The efficiency estimates by the two parametric methods are highly correlated, whereas there are considerable differences between the DEA estimates and those from the parametric methods. This is also consistent with the Monte Carlo results. By comparing the alternative efficiency estimates in the two applications, it is found that there is less discrepancy among the three sets of efficiency estimates in the airline case than in the railway case. This can be partly attributed to the fact that there are fewer variations in the operating environments in the airline case than in the railway case. The simulation results in Part I provide some general guidelines regarding the relative merits of the three alternative methods under certain known conditions. The two applications of the three methods in Part II serve as examples of how these three methods can be applied to practical problems where no a priori knowledge of either the production technology nor the efficiency profile exists. They illustrate some of the problems that may be encountered in empirical applications. / Business, Sauder School of / Graduate

published_or_final_version / Business Administration / Master / Master of Business Administration

Kwan, Cheuk Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 115-118). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.9 / Chapter 1.1 --- The Gate Assignment Problem --- p.9 / Chapter 1.2 --- Contributions --- p.10 / Chapter 1.3 --- Formulation of Gate Assignment Problem --- p.11 / Chapter 1.4 --- Organization of Thesis --- p.13 / Chapter 2 --- Literature Review --- p.15 / Chapter 2.1 --- Introduction --- p.15 / Chapter 2.2 --- Formulations of Gate Assignment Problems --- p.15 / Chapter 2.2.1 --- Static Gate Assignment Model --- p.16 / Chapter 2.2.1.1 --- Total Passenger Walking Distance --- p.17 / Chapter 2.2.1.2 --- Waiting Time --- p.20 / Chapter 2.2.1.3 --- Unassigned Flights --- p.21 / Chapter 2.2.2 --- Stochastic and Robust Gate Assignment Model --- p.22 / Chapter 2.2.2.1 --- Idle Time --- p.22 / Chapter 2.2.2.2 --- Buffer Time --- p.23 / Chapter 2.2.2.3 --- Flight Delays --- p.23 / Chapter 2.2.2.4 --- Gate Conflicts --- p.24 / Chapter 2.3 --- Solution Methodologies --- p.25 / Chapter 2.3.1 --- Expert System Approaches --- p.25 / Chapter 2.3.2 --- Optimization --- p.27 / Chapter 2.3.2.1 --- Exact Methods --- p.27 / Chapter 2.3.2.2 --- Heuristic Approaches --- p.28 / Chapter 2.3.2.3 --- Meta-Heuristics Approaches --- p.29 / Chapter 2.3.2.4 --- Tabu Search and Path Relinking --- p.31 / Chapter 2.4 --- Current Practice of Gate Assignment Problems --- p.32 / Chapter 2.5 --- Summary --- p.32 / Chapter 3 --- Tabu Search --- p.34 / Chapter 3.1 --- Introduction --- p.34 / Chapter 3.2 --- Mathematical Model --- p.34 / Chapter 3.3 --- Principles of Tabu Search --- p.36 / Chapter 3.4 --- Neighborhood Structures --- p.38 / Chapter 3.4.1 --- Insert Move --- p.38 / Chapter 3.4.2 --- Exchange Move --- p.39 / Chapter 3.5 --- Short Term Memory Structure --- p.41 / Chapter 3.6 --- Aspiration Criterion --- p.42 / Chapter 3.7 --- Intensification and Diversification Strategies --- p.43 / Chapter 3.8 --- Tabu Search Framework --- p.45 / Chapter 3.8.1 --- Initial Solution --- p.45 / Chapter 3.8.2 --- Tabu Search Algorithm --- p.46 / Chapter 3.9 --- Computational Studies --- p.52 / Chapter 3.9.1 --- Parameters Tuning --- p.52 / Chapter 3.9.1.1 --- Fine-tuning a Tabu Search Algorithm with Statistical Tests --- p.53 / Chapter 3.9.1.2 --- Tabu Tenure --- p.54 / Chapter 3.9.1.3 --- Move Selection Strategies --- p.56 / Chapter 3.9.1.4 --- Frequency of Exchange Moves --- p.59 / Chapter 3.9.2 --- Comparison the Fine-tuned TS with original TS --- p.62 / Chapter 3.10 --- Conclusions --- p.63 / Chapter 4 --- Path Relinking --- p.65 / Chapter 4.1 --- Introduction --- p.65 / Chapter 4.2 --- Principles of Path Relinking --- p.65 / Chapter 4.2.1 --- Example of Path Relinking --- p.66 / Chapter 4.3 --- Reference Set --- p.68 / Chapter 4.3.1 --- Two-Reference-Set Implementation --- p.71 / Chapter 4.3.1.1 --- Random Exchange Gate Move --- p.72 / Chapter 4.4 --- Initial and Guiding Solution --- p.73 / Chapter 4.5 --- Path-Building Process --- p.74 / Chapter 4.6 --- Tabu Search Framework with Path Relinking --- p.78 / Chapter 4.6.1 --- Computational Complexities --- p.82 / Chapter 4.7 --- Computational Studies --- p.82 / Chapter 4.7.1 --- Best Configuration for Path Relinking --- p.83 / Chapter 4.7.1.1 --- Reference Set Strategies and Initial and Guiding Criteria --- p.83 / Chapter 4.7.1.2 --- Frequency of Path Relinking --- p.86 / Chapter 4.7.1.3 --- Size of Volatile Reference Set --- p.87 / Chapter 4.7.1.4 --- Size of Non-volatile Reference Set --- p.89 / Chapter 4.7.2 --- Comparisons with Other Algorithms --- p.94 / Chapter 5 --- Case Study --- p.98 / Chapter 5.1 --- Introduction --- p.98 / Chapter 5.2 --- Airport Background --- p.98 / Chapter 5.2.1 --- Layout of ICN --- p.98 / Chapter 5.3 --- Data Preparation --- p.99 / Chapter 5.3.1 --- Passenger Data --- p.103 / Chapter 5.4 --- Computational Studies --- p.104 / Chapter 5.4.1 --- Experiments without Airline Preference --- p.104 / Chapter 5.4.2 --- Experiments with Airline Preference --- p.106 / Chapter 5.4.2.1 --- Formulation --- p.106 / Chapter 5.4.2.2 --- Results --- p.108 / Chapter 5.5 --- Conclusion --- p.111 / Chapter 6 --- Conclusion --- p.112 / Chapter 6.1 --- Summary of Achievement --- p.112 / Chapter 6.2 --- Future Developments --- p.113 / Bibliography --- p.115 / Appendix --- p.119 / Chapter 1. --- Friedman´ةs Test --- p.119 / Chapter 2. --- Wilcoxon's Signed Rank Test for Paired Observation --- p.120 / Chapter 3. --- Hybrid Simulated Annealing with Tabu Search Approach --- p.121 / Chapter 4. --- Arrival Flight Data of Incheon International Airport --- p.122 / Chapter 5. --- Departure Flight Data of Incheon International Airport --- p.139

This research is a comparative field study of the influence of societal culture on organization structure and process. Past empirical studies were used to dérive a framework incorporating four fundamental dimensions of cultural values: power, authority distance, group orientation, and cognitive orientation- From thèse dimensions, five configurations were identified which accounted for most of the organizations reviewed in previous field studies: the Autocracy, the Political Entourage, the Traditional Bureaucracy, the Modem Bureaucracy and the Consensus configuration.[...] / Cette recherche est une étude comparative de l’Influence de la culture sociale sur les structures et processus d’organisation. Un cadre Incluant quatre dimensions fondamentales des systèmes de valeurs culturels, soit le pouvoir, la distance d’autorité, l’orientation de groupe et l’orientation cognitive, fut dérivé d’études empiriques passées. Cinq configurations furent Identifiées à partir de ces dimensions et expliquèrent la plupart des organisations étudiées dans des ouvrages antérieurs: l’Autocratie. l’Entourage Politique, la Bureaucratie Traditionnelle, la Bureaucratie Moderne et la configuration du Consensus.[...]

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