【導(dǎo)讀】在現(xiàn)代企業(yè)制度，一個優(yōu)秀的人力資源系統(tǒng)是一個重要的競爭因素。一種常用的方法推動工作的能力，充沛的人力資源。雇員再培訓計劃，培養(yǎng)人才。訓練效果，可以大大提高課程，如果是精心設(shè)計和安排。我們改進了工藝，提出了優(yōu)化課程設(shè)置模式，在維修人員的培訓，利用遺傳。算法作為解決程序。自適應(yīng)計算機輔助訓練系統(tǒng)維修代表也進行了培訓，制定促。進人才培養(yǎng)機床業(yè)。為了經(jīng)營業(yè)務(wù)，以促進。可持續(xù)發(fā)展，員工培訓活動是至關(guān)重要的問題，員工培訓在企業(yè)中，資源，而且地提高組織效率，生產(chǎn)力和利潤。此外，員工培訓，可以激發(fā)人的能。力，增強工作人員的成績，提高個人和組織的作用。作出了有效的企業(yè)培訓計劃。學員，它并不提供診斷或建議。此外，課程調(diào)度系統(tǒng)也可以成為個性化和大眾。司在這一領(lǐng)域進行的常規(guī)手段。導(dǎo)致費時少和訓練成績。發(fā)式，提出解決辦法，初步滿足要求。基于必須對模型的調(diào)整，邱應(yīng)用遺傳搜索。年，柏拉圖系統(tǒng)中的伊利諾大學是一個CAI系統(tǒng)，對學生的教育。

　　

【正文】 ation algorithms for engineering problems and found that GA outperformed other optimization algorithms. 六維論文網(wǎng) Using GA to solve the optimization problem, the objective function must be formulated to be a fitness function, which represents the fitness of a system to its outside environment, as the performance index of system. If the value of this fitness function approaches the optimization goal, the system performance is better. GA has developed to find the optimization solution with fitness by some of artificial operational process, which simulated natural selection and geic, such as reproduction, crossover, and mutation. The basic principle of GA has developed from the simple geic algorithm, SGA (Goldberg, 1989). GA is applied in a wide variety of research fields and is shown capable of finding optimal solution rapidly (Arroyo and Armentano, 2020, Chang and Chung, 2020, Elegbede and Adjallah, 2020 and Kumral, 2020). 3 Course structure analysis For a trainee with limited training time, it is very important to receive the training as efficiently as possible. The available training time should be considered. The course bination is also configured according to each trainee’ s knowledge and abilities. Furthermore, some courses with multiple knowledge aspects do not belong to only one category. For instance, a course on machine fault diagnosis utilizes many skills or knowledge to acplish a diagnosis job, so the training material should be assigned specifically to reflect the characteristics. The courses should be procedural for effective employee training, so trainees should take the training courses one by one, from lower levels to higher levels. However, this may not actually be true in training if the courses are not well constructed or the trainees need only applied skills. In the general enterprise training practice, most of the trainees already have basic concepts of the training subject, and only need further applied knowledge. Therefore, the training courses could be implemented focusing on applied skills, and not according to a clear procedure. Weaver (1988) designed a training course, based on indicators of learning difficulty and importance, and found that more plex knowledge is, more difficult to learn, and this takes more time. Weaver defined the structure of course unit for knowledge analysis as four indicators: separability, plexity, importance and practicability. We modified Weaver’ s indicators due to industrial practice. Each training course was assigned attribute values according to course significance, frequency, level, and training time. The training courses in machine tool panies can be separated into three content categories: machinery, electricity and operation, based on designated maintenance tasks. Furthermore, two general course categories are aided for a plete course spectrum: programming and general. The manager expects the maintenance representatives to learn as many professionals as possible and is capable of fixing all models of machines. We considered course structure and the content of course to machine type. 員工培訓排課系統(tǒng)遺傳算法英文文獻翻譯 4 Formulation of optimal course arrangement models 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. 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 Geic 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 binatorial 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 puted to achieve better solution. According to the abovementioned reasons, GA was utilized as a solution tool in this research. 六維論文網(wǎng) 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 twopoint crossover mechanism was used. The mutation rate was set to , while the crossover rate , 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: for machinery, for electricity, and for operation。 (3) if the optimal solution did not change over 50 generations, it stopped running. 5 Computer assisted training system Demand analysis An optimization model was formulated to deal with training course scheduling for maintenance re