员工培训排课系统遗传算法英文文献翻译 第8页
4 Formulation of optimal course arrangement models
4.1 Objective function
The optimization model we proposed for adaptively arranging training courses to meet training purpose was formulated. The objective function is shown as. The objective is to maximize the scheduling utility, in terms of course attibutes and training demand.
4.2 Constraints
1. Available training time
2. Course importance requirement
3. Usage frequency requirement
4. Course level requirement
5. Course for machine requirement
6. Prerequisite course requirement
7. Required course (RC) requirement
4.3 Genetic algorithms for solution process
This research has considered the issue of the scheduling of the training course as an optimization problem. The objective function is set to meet demands from the trainee, such as training time and course level, etc. The model formulated in this study is a combinatorial model. The GA coding schema can be applied to the course scheduling. Utilizing GA enables solutions to be obtained promptly and easily because GA does not trap into the local optima and can reach a global solution. Moreover, GA employs multiple starting points to search for a solution simultaneously, speeding up the search process. The information represented by chromosome, in terms of GA, can be interchanged, decoded and computed to achieve better solution. According to the abovementioned reasons, GA was utilized as a solution tool in this research. 辣文论文网毕业论文
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http://www.751com.cn/When arranging a training course, one major indicator is whether or not a specific course unit been picked as training unit. To build a model for course scheduling, the decision variables can be designed as a binary variable with 1 indicating course picked and 0 otherwise. It shows the bit presentation of course unit. Each box presents a course unit. The value in the box, 1 or 0, indicates its arrangement for a training program.
Many settings should be ascertained before GA is executed. In our illustrative model, variables were encoded into a chromosome, and a fitness function was built. A two-point crossover mechanism was used. The mutation rate was set to 0.01, while the crossover rate 0.8, with elitism selection. The initial population of the chromosome pool reaches 100, which is the empirical value for efficiently pursuing optimal solution. Other settings include: (1) total available training time 240 min; (2) the demand levels for course categories are: 0.5 for machinery, 0.4 for electricity, and 0.1 for operation; (3) if the optimal solution did not change over 50 generations, it stopped running.
5 Computer assisted training system
5.1 Demand analysis
An optimization model was formulated to deal with training course scheduling for maintenance representatives in the machine tool sector. To build a computer assisted training system (CATS) by utilizing GA methodology, we first investigated the characteristics of training materials, and the practice of the training process. The demands from both trainees and course arrangers were then collected and analyzed. To analyze the functional demand of CATS, and to fully augment system usability, managers from six machine tool manufacturers were interviewed. All were managers working in the human resource departments of manufacturers ranked within the top 10 for sales figures from 1999 to 2004 in Taiwan. They were Yeong-Ching Machinery, Yang Iron Works Co, Union Optronics Crop, Leadwell Taiwan, OR Taichung Machinery, and Chevalier Groups.
5.1.1 The machine tool sector
The machine tool industry is one of the most important industrial activities in Taiwan. Machine tool enterprises must have reliable operational ability to attract international orders in the highly competitive global environment. Five major characteristics of the machine tool industry were isolated from the interview materials.
1. The machine tool industry has critically close relations with the aerospace industry, electronics, the automobile industry and defense. It plays an important supportive and cooperative role with other industries.
2. The machine tool industry, which produces high-precision machine tools, is technology-intensive.
3. Demand for machine tools in market is dramatically influenced by economical cycles.
4. The numbers of components and range of materials is large. Enterprises frequently encounter urgent requests for changes of design.
5. While enterprises need to continuously invest on technology and human resources, the returns on investment are relatively low.
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