Today, Professor Vazquez-Abad talked about a case study which was a project that she has contributed to for optimizing scheduling and operating of transportation at Melbourne Airport at Australia. The problem is to better analyze and operate the transportation system at Melbourne Airport. The risk constraints for this project are: financial investments, communication and network, chemical and environmental factors, economic modeling of strategic decisions and meet industry standards.
The setup is as following: buses are empty at departures, buses go to arrivals, and random numbers of passengers are waiting for the bus (numbers of passengers are not known), random loading time (the amount of time required to load the passengers are not known), buses go around the parking lots to pick up passengers, again there is random amount of passengers. Passengers queue at the stations.
One of the objective functions is to reduce the waiting time of the passengers and provide feasible service to the passengers so that everyone will be able to take the buses and go to the desired destination within the terminal. Another objective function is not letting passengers wait more than 10 minutes.
The problem and solutions provided uses Dynamic Programming and Greedy approaches. In the optimization solutions, there are objective functions, feasibility and principle of optimality, minimum and maximum constraints, greedy criterions. Given the objective functions, there are probabilistic mathematical models, queue’ing model and greedy approaches provided as feasible solutions to the problems.
My interest in this topic would be processing large amount of data using distributed systems principles, paradigms and applications. Professor mentioned that the data they are working on is very large and hard to process. I would be interested in working on such large data sets and processing them and making them meaningful. Moreover, I would be interested in analyzing the dynamic programming approach and greedy approach to the problem.
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