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研究生:ThiTamNguyen
研究生(外文):ThiTamNguyen 阮氏心
論文名稱:應用資料探勘在員工訓練的課程排程問題上
論文名稱(外文):Course Scheduling for Employee Training Using Data Mining
指導教授:吴建文
指導教授(外文):Chien-Wen Wu 吴建文
口試委員:杜壯盧宗成
口試委員(外文):杜壯Chung-Cheng Lu 盧宗成
口試日期:2012-06-21
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:管理國際學生碩士專班 (IMBA)
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:43
中文關鍵詞:排程問題員工訓練資料探勘
外文關鍵詞:Course SchedulingEmployee TrainingData Mining
相關次數:
  • 被引用被引用:0
  • 點閱點閱:164
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  • 下載下載:15
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In this research, we study the problem of scheduling a training course for an enterprise assuming that employees are busy and may not be available at all the time. We want to schedule several time slots for the course so that employee constraints, the instructor constraints and the room constraints can all be satisfied. A mathematical model is provided for the problem. Also, an algorithm based on Frequent Itemset Mining (FIM) is presented for the problem. The experiments were performed on a 1.2 GHz PC with 2 GB of memory running Windows XP. For our approach, we employed a version of the Mafia algorithm for Frequent Itemset Mining. For the exact approach, we used Visual C++ and the CPLEX callable library to solve our mathematical model for the comparison purpose. As a result, our approach performs faster than the CPLEX approach in overall. The maximum improved ratio is 2589.88%. However, when CF equals to 0.7, our approach shows poorer results. It may be explained by the influence of the availability of employees on these two approaches.

In this research, we study the problem of scheduling a training course for an enterprise assuming that employees are busy and may not be available at all the time. We want to schedule several time slots for the course so that employee constraints, the instructor constraints and the room constraints can all be satisfied. A mathematical model is provided for the problem. Also, an algorithm based on Frequent Itemset Mining (FIM) is presented for the problem. The experiments were performed on a 1.2 GHz PC with 2 GB of memory running Windows XP. For our approach, we employed a version of the Mafia algorithm for Frequent Itemset Mining. For the exact approach, we used Visual C++ and the CPLEX callable library to solve our mathematical model for the comparison purpose. As a result, our approach performs faster than the CPLEX approach in overall. The maximum improved ratio is 2589.88%. However, when CF equals to 0.7, our approach shows poorer results. It may be explained by the influence of the availability of employees on these two approaches.

ABSTRACT i
ACKNOWLEDGEMENTS ii
TABLE OF CONTENTS iii
LIST OF TABLES iv
LIST OF FIGURES v
ABBREVIATIONS vi
CHAPTER I: INTRODUCTION 1
1.1 Study Purpose 1
1.2 Research Procedure 3
1.3 Study Structure 4
CHAPTER II: LITERATURE REVIEW 5
2.1 Course Scheduling 5
2.2 Employee Education and Training 11
2.3 Employee Availability 13
CHAPTER III: MATHEMATICAL MODEL 16
3.1 Problem Model 16
3.2 Example 18
CHAPTER IV: A SOLUTION ALGORITHM 23
4.1 Frequent Itemset Mining 23
4.2 Our Approach 28
CHAPTER V: EXPERIMENTAL EVALUATION 32
5.1 Experimental Procedure 32
5.2 Experimental Results 33
CHAPTER VI: CONCLUSIONS AND SUGGESTIONS 36
6.1 Conclusions 36
6.2 Suggestions 37
REFERENCES 38


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