17-04-2017, 12:32 PM
The generation of schedules is the most fundamental activity in any educational institution. It is also the most difficult and slow process. The generation of the time table can be compared with many classic problems in computer theory such as "N-Queen problem", "work schedule".
The basic objective of our project is to automate the scheduling process. Our goal is to design an interactive user program that generates the schedule according to the restrictions given. The program is designed with special emphasis on the requirements of engineering college. The program can simply be expanded to fit the requirements of other types of institutions as well.
The problem of scheduling can be compared to optimization problems such as "Traveling sales person problem". In the problem of traveling salespeople a set of feasible solutions is obtained, and one of them is chosen as the optimum, which satisfies the restrictions. Using the concept of 'Hop Field Memory' in Neural Networks, you can derive an 'Energy Function' for any optimization problem. The optimal solution is the one that minimizes the energy function. Similarly, it is possible to derive an energy function for the problem of generating schedules using hop field networks and solution, which minimizes this energy function, is the optimal solution.
The basic objective of our project is to automate the scheduling process. Our goal is to design an interactive user program that generates the schedule according to the restrictions given. The program is designed with special emphasis on the requirements of engineering college. The program can simply be expanded to fit the requirements of other types of institutions as well.
The problem of scheduling can be compared to optimization problems such as "Traveling sales person problem". In the problem of traveling salespeople a set of feasible solutions is obtained, and one of them is chosen as the optimum, which satisfies the restrictions. Using the concept of 'Hop Field Memory' in Neural Networks, you can derive an 'Energy Function' for any optimization problem. The optimal solution is the one that minimizes the energy function. Similarly, it is possible to derive an energy function for the problem of generating schedules using hop field networks and solution, which minimizes this energy function, is the optimal solution.