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The Vocational Colleges Timetable Problem Solving Model And Its Algorithm

Posted on:2013-03-19Degree:MasterType:Thesis
Country:ChinaCandidate:L Y TaoFull Text:PDF
GTID:2248330395473343Subject:Computer technology
Abstract/Summary:PDF Full Text Request
The curriculum is the command and schedule of the school’s day-to-day teaching activities, choreography curriculum is one of the most important teaching management. In recent years, with the rapid expansion of the High Vocational School, the number of students in a significant growth, increases the difficulty of arranging. Especially vocational colleges "combining learning with" training mode corresponding period management of teaching space, administrative classes proposed new requirements.A detailed analysis of the vocational colleges Timetable, ZheJiang Economic&Trade Polytechnic summarizes the problems existing in the the current vocational colleges Timetable process. According to the needs of vocational colleges teaching management features as well as the actual work, the traditional genetic algorithm based vocational colleges Timetable Problem Solving Model. Oriented constraint satisfaction based on the introduction of the automatic generation of individual priority, ranging from long class periods management software and hardware constraints grading training venue management, curriculum individual repair algorithm, and the initial population and selection crossover and mutation operator to optimize the design of a Course Scheduling Algorithm for vocational colleges. School this semester teaching tasks at the same time, for example, from the vertical, horizontal angle of Course Scheduling Algorithm simulation test, analysis of soft constraints, crossover probability, mutation probability parameters Timetable effect. Finally, a design based on the the JSP Language and SQL Server2000the vocational colleges FTCN Course Scheduling System, to verify the practicality of arranging model.
Keywords/Search Tags:Vocational institutions, Timetable, Constraint Satisfaction, Genetic algorithm
PDF Full Text Request
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