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研究生： Astrid Lelitya Rahma
Astrid Lelitya Rahma
論文名稱： 運用階層式時間彩色派翠網路與基因演算法於設計流程人力指派最佳化之研究
Modeling and Optimizing Human Resource Assignment of Design Process Using Hierarchical Timed Colored Petri Net and Genetic Algorithm
指導教授： 歐陽超
Chao Ou-Yang
口試委員: 郭人介
Ren-Jieh Kuo
楊朝龍
Chao-Lung Yang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 104
中文關鍵詞： Design Process 、Genetic Algorithm 、Hierarchical Timed Colored Petri Net 、Human Resource Assignment 、Project Evaluation and Review Technique 、Project Management
外文關鍵詞： Design Process, Genetic Algorithm, Hierarchical Timed Colored Petri Net, Human Resource Assignment, Project Evaluation and Review Technique, Project Management
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    • This thesis presents a Hierarchical Timed Colored Petri Net-based modeling and Genetic Algorithm optimization approach for human resource assignment in a process design. The objective of the genetic algorithm is to find the human resource assignment that has the most minimal most likely total duration of the design project and the under time of resources. The simulating of the design process in Hierarchical Timed Colored Petri Net is based on the human resource assignment result of the genetic algorithm. By running the simulation, it can get the optimistic time, most likely time, and pessimistic time of a design project. Using project evaluation and review technique it can calculate the expected time and the standard deviation of a design project based on the simulation results. From simulation results, can be reviewed which job of a design process that has the most critical human resource assignment.
    A Case study in this thesis based on the design process of one of
    the companies in Taipei, Taiwan. The genetic algorithm results give the best 5 solutions of resource allocation with the best fitness value is 318.8. These 5 best solutions have the same most likely duration of design project which is 204 days. After running the simulation using HTCPN model, solution 2 has the most minimal expected time of a design project which is 208.5 days. Solution 2, 4, and 5 have the same most critical jobs which are job 20, job 21, job 24, job 27, and job 34.

    This thesis presents a Hierarchical Timed Colored Petri Net-based modeling and Genetic Algorithm optimization approach for human resource assignment in a process design. The objective of the genetic algorithm is to find the human resource assignment that has the most minimal most likely total duration of the design project and the under time of resources. The simulating of the design process in Hierarchical Timed Colored Petri Net is based on the human resource assignment result of the genetic algorithm. By running the simulation, it can get the optimistic time, most likely time, and pessimistic time of a design project. Using project evaluation and review technique it can calculate the expected time and the standard deviation of a design project based on the simulation results. From simulation results, can be reviewed which job of a design process that has the most critical human resource assignment.
    A Case study in this thesis based on the design process of one of
    the companies in Taipei, Taiwan. The genetic algorithm results give the best 5 solutions of resource allocation with the best fitness value is 318.8. These 5 best solutions have the same most likely duration of design project which is 204 days. After running the simulation using HTCPN model, solution 2 has the most minimal expected time of a design project which is 208.5 days. Solution 2, 4, and 5 have the same most critical jobs which are job 20, job 21, job 24, job 27, and job 34.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vii CHAPTER 1 INTRODUCTION 1 1.1. Background 1 1.2. Purpose 4 1.3. Research Structure 4 CHAPTER 2 LITERATURE REVIEW 5 2.1. Petri Net 5 2.1.1. Colored Petri Net 6 2.1.2. Hierarchical Timed Colored Petri Net 7 2.2. CPN Tools 8 2.2.1 Simulation using CPN Tools 9 2.3. Genetic Algorithm 10 2.3.1 Procedure of Genetic Algorithm 11 2.3.2 Genetic Representation 11 2.4. Engineering Design Process 12 2.5. Resource Assignment 13 CHAPTER 3 RESEARCH METHODOLOGY 15 3.1. Research Methods 15 3.2. Research Framework 15 ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vii CHAPTER 1 INTRODUCTION 1 1.1. Background 1 1.2. Purpose 4 1.3. Research Structure 4 CHAPTER 2 LITERATURE REVIEW 5 2.1. Petri Net 5 2.1.1. Colored Petri Net 6 2.1.2. Hierarchical Timed Colored Petri Net 7 2.2. CPN Tools 8 2.2.1 Simulation using CPN Tools 9 2.3. Genetic Algorithm 10 2.3.1 Procedure of Genetic Algorithm 11 2.3.2 Genetic Representation 11 2.4. Engineering Design Process 12 2.5. Resource Assignment 13 CHAPTER 3 RESEARCH METHODOLOGY 15 3.1. Research Methods 15 3.2. Research Framework 15 3.3. Design Process 21 3.4. Resource Allocation using Genetic Algorithm 22 3.4.1. Assumptions and Limitations 23 3.4.2. The Mathematical Model 24 3.4.3. Steps of Genetic Algorithm 30 3.5. Modeling Design Process using Hierarchical Timed Colored Petri Net 38 3.6. Simulation of HTCPN model of Design Process 45 CHAPTER 4 RESULTS AND DISCUSSION 49 4.1. Human Resource Assignment Case 49 4.2. Human Resource Assignment using Genetic Algorithm 61 4.3. Simulation 73 4.4. Simulation Results and Analysis 79 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH 88 5.1. Conclusion 88 5.2. Research Contribution 89 5.3. Future Research 90 REFERENCE 91

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    Aalst, W. M. P. v. d., Stahl, C. & Westergaard, M., 2013. Strategies for Modeling Complex Processes using Colored Petri Nets. Transactions on Petri Nets and Other Models of Concurrency VII. Lecture Notes in Computer Science, , Volume vol 7480, pp. 6-55.
    Ağralı, S., Taşkın, Z. C. & Ünal, A. T., 2016. Employee scheduling in service industries with flexible employee availability and demand. Omega 66, pp. 159-169.
    Alcaraz, J. & Maroto, C., 2001. A Robust Genetic Algorithm for Resource Allocation in Project Scheduling. Annals of Operations Research 102, p. 83–109.
    Anon., n.d.
    Bapjai, P. & Kumar, M., 2010. GeneticAlgorithm An Approach to Solve Global Optimization Problems. Indian Journal of Computer Science and Engineering Vol 1 No 3, pp. 199-206.
    Bouajaja, S. & Dridi, N., 2016. A Survey on Human Resource Allocation Problem and Its Application. Operational Research An International Journal, Volume 16, pp. 1-31.
    Chen, X., 2012. An Algorithm Development Environment for Problem-Solving: Software Review. Memetic Com 4, pp. 149-161.
    Chen, Y.-L., Hsu, P.-Y. & Chang, Y.-B., 2008. A Petri Net Approach to Support Resource Assignment in Project Management. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, 38(No. 3), pp. 564-574.
    Dey, D. K., 2014. Mathematical Study of Adaptive Genetic Algorithm (AGA) with Mutation and Crossover Probabilities. COMPUSOFT, An International Journal of Advanced Computer Technology, 3(5), pp. 765-768.
    Guan, S., Nakamura, M., Shikanai, T. & Okazaki, T., 2009. Resource assignment and scheduling based on a two-phase metaheuristic for cropping system. Computers and Electronics in Agriculture 66, pp. 181-190.
    Gunasekaran, L. & Subramaniam, S., 2016. FAGA: Hybridization of Fractional Order ABC and GA for Optimization. The International Arab Journal of Information Technology Vol. 13, No. 6B, pp. 1045-1053.
    Jensen, K., Christensen, S., Kristensen, L. M. & Westergaard, M., 2010. CPN Tools 4.0. [Online]
    Available at: http://cpntools.org/documentation/tasks/performance/start
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    Jones, K. O., 2005. Comparison of Genetic Algorithm and Particle Swarm Optimisation. International Conference on Computer Systems and Technologies.
    Kumar, A. & Ganesh, L. S., 1998. Use of Petri Nets for Resource Allocation in Projects. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 45 (No.1), pp. 49-56.
    Lowen, R. & Verschoren, A., 2008. Foundations of Generic Optimization Volume 2: Applications of Fuzzy Control, Genetic Algorithm, and Neural Networks. s.l.:Springer.
    Mejia, G. & Montoya, C., 2010. Applications of Resource Assignment and Scheduling with Petri Nets and Heuristic Search. Annals of Operations Research 181(1), pp. 795-812 .
    Mejia, G., Montoya, C., Cardona, J. & Castro, A. L., 2012. Petri nets and genetic algorithm for complex manufacturing systems scheduling. International Journal of Production Research, 50(No. 3), pp. 791-803.
    Netjes, M., Aalst, W. M. v. d. & Reijers, H. A., 2009. Analysis of resource-constrained processes with Colored Petri Nets. Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, pp. 251-265.
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