On-demand air transportation is a recent trend in the airline industry. It allows the customer to call in days or even hours before to book a ight. Therefore, the scheduling and planning of this type of airline needs to be done daily. Hence, a successful on-demand air transportation requires an e cient ight scheduling system to construct the optimal daily ight schedules. An on-demand air transportation ight scheduling problem that arose in a Southern African industry has been studied. A new solution methodology is proposed. A number of new heuristics are used to combine ight legs for a robust solution. A time-space multi-commodity network is introduced to derive the mathematical model which is then solved using CPLEX. The results obtained are then compared with known results showing much more e cient performances and saving for the industry.

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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 thesis reports on research into the applicability of intelligent agents in the airline scheduling environment. The methodology employed was to look at intelligent agent research and then, based on this, to build models that can be used to solve some of the airline scheduling problems. The following was done: · An agent-based model was developed that can assist airline schedulers in the maintenance of a disrupted schedule. The agent model consists of a hybrid approach combining elements of machine learning and expert systems. · A multiagent model was developed that can generate a profitable and flyable schedule. The multiagent model developed extends the traditional control structures of the hierarchical agent organisation to a matrix structure. This new model can be extended to any problem domain that deals with resource allocation and capacity management. To guide the thinking behind this research, a few questions were posed regarding the problem to be solved: Q1. Can intelligent agents play a role in the airline industry, with specific focus on the scheduling creation and maintenance process? Q2. What will the design of the agent models be if the scheduling needs of an airline have to be addressed? Q3. If the models as envisioned in question 2 can be created, what will the practical implications be? At a conceptual level the research produced three results: R1. No references were found to multiagent technology in the production or maintenance of airline schedules. This theoretical research into agent systems shows that there is applicability in the scheduling environment, with specific reference to schedule maintenance and generation. R2. An agent model was created that combines declarative knowledge with empirical learning to assist human schedulers in the day-to-day maintenanceof the schedule. Multiple solutions to a scheduling problem are generated by the agent using embedded scheduling rules. The agent then uses the Qlearning algorithm to learn the preferences of the human scheduler. This approach combines the best of expert systems and machine learning. To solve the problem of schedule generation, a multiagent system with a matrix governance model was introduced. Aircraft and airports were modelled as buying and selling agents. The business manager agent that assigns individual aircrafts to specific routes was defined. This was accomplished by matching individual aircraft capacity to origin-destination demand. The agent model was then expanded to show how the inclusion of a resource manager agent can handle system capacity management. This is a matrix governance model, as an aircraft agent is managed by a business manager agent, as well as by a resource manager agent. The initial results from the prototype show that this model can generate profitable and flyable schedules. The multiagent model developed extends the traditional hierarchical agent organisation to that of a matrix structure. The contract net protocol used for typical multiagent coordination was adapted to work in this new control structure. This new model can be extended to any problem domain that deals with resource allocation and capacity management. R3. A few airlines use expert systems to handle schedule disruptions. By introducing machine learning, a flexibility is achieved that is currently not available. The approach proposed for schedule generation is not guaranteed to provide optimal results like traditional operations research techniques, but it is useful for high-level analysis, long-term planning, new hub or alliance planning and research. It also has potential as a catalyst for integrated planning. Keywords: Multiagent systems, airline scheduling / Ehlers, E.M., Prof.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Mordechai Porath. / M.S.

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering. University of the Witwatersrand, Johannesburg, 2016 / Given a flight schedule with fixed departure times and cost, solving the fleet assignment problem assists airlines to find the minimum cost or maximum revenue assignment of aircraft types to flights. The result is that each flight is covered exactly once by an aircraft and the assignment can be flown using the available number of aircraft of each fleet type. This research proposes a novel, non-linear integer programming fleet assignment model which differs from the linear time-space multi-commodity network fleet assignment model which is commonly used in industry. The performance of the proposed model with respect to the amount of time it takes to create a flight schedule is measured. Similarly, the performance of the time-space multicommodity fleet assignment model is also measured. The objective function from both mathematical models is then compared and results reported. Due to the non-linearity of the proposed model, a genetic algorithm (GA) is used to find a solution. The time taken by the GA is slow. The objective function value, however, is the same as that obtained using the time-space multi-commodity network flow model. The proposed mathematical model has advantages in that the solution is easier to interpret. It also simultaneously solves fleet assignment as well as individual aircraft routing. The result may therefore aid in integrating more airline planning decisions such as maintenance routing. / MT2017