Method for high-efficiency class grouping and course scheduling in multi-restraint condition

Abstract

The invention relates to a method for class grouping and course scheduling by using a computer in schools, and specifically relates to a method for high-efficiency class grouping and course scheduling in a multi-restraint condition. The invention aims to overcome the technical shortcomings in the prior art so as to provide a space-saving and time-saving method to purposely, positively, promptly and reasonably complete the class grouping and course scheduling in a multi-restraint condition. For achieving the target, the technical scheme provided by the invention comprises such four steps as assignment, class grouping, conversion and course scheduling. The method has the advantage of largely reducing the time needed by class grouping and course scheduling, thus having a good time flexibility. In particular, when handling multiple students and classes, the method has more advantages of greatly improving the work efficiency, reducing the manpower, facilitating the workload statistics on teachers and students and curricula-variable trend of students and facilitating check on timetables of teacher and student, thus brining convenience for interim class adjustments. In addition, the method can be used to schedule examinations.

Description

Technical field [0001] The invention relates to a method for schools to classify and arrange classes by using computers, in particular to a method for efficiently classifying and arranging classes under multiple constraints. Background technique [0002] In 1975, S. Even et al. demonstrated that the curriculum problem is NP-complete. Since the timetable problem involves more information, and the time complexity of solving the optimal solution of the timetable problem is exponential in the scale of the timetable, the algorithm for finding the approximate optimal solution is generally used. In real life, people generally only want an approximate optimal solution that satisfies various conditions, or a satisfactory solution, not necessarily the optimal solution. Therefore, for the curriculum problem, the key is not how to find the optimal solution, but how to improve the satisfaction of the solution. [0003] The most worrying problem of traditional manual scheduling is the occurren...

AI Technical Summary

Problems solved by technology

The genetic algorithm is applied to the problem of scheduling courses, which is to gradually approach the set goal. First, the courses are arranged in the schedule according to the initial conditions. Class space consumption is large, especially after randomly generating a lot of timetables and checking according to the conditions, those that do not meet the conditions are discarded, and the remaining ones are randomly modified by imitating genetics to form many new sub-timetables, and those that do not meet the conditions are abandoned until the task is completed; other Simulating natural selection and genetics, that is, survival of the fittest and survival of the fittest, does not actively generate curriculum according to conditions, and there is a certain degree of blindness, which will inevitably lead to a lot of waste of time

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