臺灣博碩士論文加值系統

One of the tools to deal with the complexity in manufacturing planning problem is Advanced Planning and Scheduling (APS). Many implementations of APS in the real world are stopped due to the solution’s infeasibility. One of the causes is the inaccurate physical system model: the machine’s behavior. Most of the study tend to simplify it. No matter how good the solution is, it will not work if it is built on a wrong physical system model. Since modelling the machine detailedly takes effort and cost and the model that tends to be simplified leads to an infeasible solution. This study presents what shop floor’s condition it is mandatory to model the detailed machine’s behavior as the detailed one in generating the APS’s solution. To adapt to the industry 4.0 challenge, the case study on the industry 4.0 shop floor and the known machine’s error in real time are applied. This study shows that it is important to model the detailed machine detailedly. It is because there is a significant difference on the customer order’s makespan when the detailed machine is modeled as the detailed one rather than as the simplified one. This phenomenon is affected by the machine or process before-after and the characteristic of the modelled machine itself.

One of the tools to deal with the complexity in manufacturing planning problem is Advanced Planning and Scheduling (APS). Many implementations of APS in the real world are stopped due to the solution’s infeasibility. One of the causes is the inaccurate physical system model: the machine’s behavior. Most of the study tend to simplify it. No matter how good the solution is, it will not work if it is built on a wrong physical system model. Since modelling the machine detailedly takes effort and cost and the model that tends to be simplified leads to an infeasible solution. This study presents what shop floor’s condition it is mandatory to model the detailed machine’s behavior as the detailed one in generating the APS’s solution. To adapt to the industry 4.0 challenge, the case study on the industry 4.0 shop floor and the known machine’s error in real time are applied. This study shows that it is important to model the detailed machine detailedly. It is because there is a significant difference on the customer order’s makespan when the detailed machine is modeled as the detailed one rather than as the simplified one. This phenomenon is affected by the machine or process before-after and the characteristic of the modelled machine itself.

ABSTRACT i
ACKNOWLEDGEMENT ii
TABLE OF CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
LIST OF APPENDIXES vii
CHAPTER 1 1
INTRODUCTION 1
1.1. Background 1
1.2. Problem Definition 2
1.3. Research Objective 2
1.4. Research Scope and Limitation 2
1.5. Organization of Thesis 3
CHAPTER 2 4
LITERATURE REVIEW 4
2.1. Advanced Planning & Scheduling (APS) 4
2.1.1. History 4
2.1.2. Definition 5
2.1.3. Concept 6
2.1.4. Component 8
2.2. Industry 4.0 Environment 10
2.2.1. History 10
2.2.2. Concept 10
2.2.3. Industry 4.0-APS 11
2.3. Research Gap 12
CHAPTER 3 14
METHODOLOGY 14
3.1. Subject and Object 14
3.2. Assumption 15
3.3. General Study Framework 16
3.3.1. Initial Scheduling Generation Stage 17
3.3.2. Resource Modelling Examination Stage 17
3.3.3. Simulation Model Verification and Validation 20
CHAPTER 4 21
RESULT AND DISCUSSION 21
4.1. Initial Scheduling Generation 21
4.2. Simulation-case Generation 22
4.3. Examination Results: Discrete Batch 23
4.3.1. Working capacity 23
4.3.2. Processing time 23
4.3.3. Setup time 25
4.3.4. Transfer time 26
4.4. Examination Results: Continuous 27
4.4.1. Working capacity 27
4.4.2. Processing time 28
4.4.3. Setup time 29
4.4.4. Transfer time 30
4.5. Examination Results: Unpauseable with Full Break Time (UFBT) 31
4.5.1. Cycle time 31
4.5.2. Setup time 32
4.5.3. Transfer time 33
4.6. Examination Results: Unpauseable with Partial Break Time (UPBT) 34
4.6.1. Cycle time 34
4.6.2. Setup time 35
4.6.3. Transfer time 36
4.7. The Conditions 37
4.8. The Conditions Validity towards Uncertainty 38
CHAPTER 5 39
CONCLUSION 39
5.1. Conclusion 39
5.2. Future Research 39
REFERENCES 40
APPENDIXES 42

APICS, APICS Operations Management Body of Knowledge Framework, APICS The Association for Operations Management, Chicago, 2011.
BDC, Industry 4.0: The New Industrial Revolution Are Canadian manufacturers ready?, Business Development Bank of Canada, 2017.
Bhawakar, N.B., Jadhao, C.M., and Mhaske, S.S., “Multiobjective Advanced Planning and Scheduling Using Iterative Genetic Algorithm”, in the International Conference on Computing Communication Control and automation (ICCUBEA), 2016, 1-6.
Chen, J.C., Huang, P.T.B, Wu, J.G., Lai, K.Y., Chen, C.C., and Peng, T.W., “Advanced planning and scheduling for TFT-LCD color filter fab with multiple lines”, International Journal Advanced Manufacturing Technology, 2013, 67: 101-110.
Chen, K. and Ji, P., “A mixed integer programming model for advanced planning and scheduling (APS)”, European Journal of Operational Research, vol.181, 2007, 515–522.
Chen, K.J. and Ji, P., “A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval”, Expert Systems with Applications, vol. 33, 2007, 1004-1010.
Choi, S.H., Park, M.W., Lee, D.H., Jeong, K.C., Lim, S.K., Kim, J.G., and Lee, G.C., “A Conceptual Framework of an Advanced Planning and Scheduling System for Printed Circuit Board Manufacturing Lines”, Asia Pacific Management Review, vol. 11, 2006, 3: 133-139.
Chua, T.J., Wang, F.Y., Cai, T.X., and Yin, X.F., “A Heuristics-based Advanced Planning and Scheduling System with Bottleneck Scheduling Algorithm”, in the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2006, 240-247.
Entrup, M.L., Advanced Planning in Fresh Food Industries: Integrating Shelf Life into Production Planning, Physica-Verlag, Heidelberg, 2005.
Eulalia, L.A., D’Amours, S., Frayret, J., Menegusso, C.C., and Azevedo, R.C., “Advanced Supply Chain Planning Systems (APS) Today and Tomorrow”, Supply Chain Management - Pathways for Research and Practice, ISBN: 978-953-307-294-4, InTech, 2011.
Fachini, R.F. and Esposto, K.F., “A framework for development of advanced planning and scheduling (APS) systems in glass container industry”, Journal of Manufacturing Technology Management, vol. 29, 2018, 3: 570-587.
Guo, J.H., “Applications of the Internet of Things Technology in Advanced Planning Systems”, Sensors and Materials, vol. 30, 2018, 8: 1723–1734.
Hsu, T.H., Wang, L.C., and Chu, P.C., “Development of a Cloud-based Advanced Planning and Scheduling System”, Procedia Manufacturing, vol. 17, 2018, 427-434.
Hvolby,H-H. and Steger-Jensen, K.. “Technical and industrial issues of advanced planning and scheduling (APS) systems”. Computers in Industry, vol. 61, 2010, 9: 845–851.
Jardzioch, A and Skobiej, B., “Simulated Hardening Algorithm Application in Advanced Planning and Scheduling”, Entrepreneurship and Management, vol. 17, 2016, 105-119.
Kung, L.C. and Chern, C.C., “Heuristic factory planning algorithm for advanced planning and scheduling”, Computers & Operations Research, vol. 36, 2009, 2513-2530.
Kuroda, M., Shin, H., and Zinnohara, A., “Robust scheduling in an advanced planning and scheduling environment”, International Journal of Production Research, vol. 40, 2002, 15: 3655-3668
Lee, Y.H., Jeong, C.S., and Moon, C., “Advanced planning and scheduling with outsourcing in manufacturing supply chain”, Computers & Industrial Engineering, vol. 43, 2002, 351-374.
Montesco, R.A.E., Pessoa, M.A.O., and Blos, M.F., “Scheduling heuristic resourced-based on task time windows for APS (Advanced planning and scheduling) Systems”, IFAC-PapersOnLine, vol. 48, 2015, 3: 2273-2280.
Neumann, K., Schwindt, C., and Trautmann, N., “Advanced production scheduling for batch plants in process industries”, OR Spectrum, 2002, 24: 251–279.
Ozturk, C. and Ornek, A.M., “Operational extended model formulations for Advanced Planning and Scheduling systems”, Applied Mathematical Modelling, vol. 38, 2014, 181-195.
Ozturk, C. and Ornek, M.A., “Optimisation and Constraint Based Heuristic Methods for Advanced Planning and Scheduling Systems”, International Journal of Industrial Engineering, vol. 23, 2016, 1: 26-48.
Peng, Y., Lu, D., and Chen, Y., “A Constraint Programming Method for Advanced Planning and Scheduling System with Multilevel Structured Products”, Discrete Dynamics in Nature and Society, vol. 2014, 2014, 917685.
Pessoa, M.A.O., Montesco, R.A.E., Junqueira, F., Filho, D.J.S., Miyagi, P.E., “Advanced Planning and Scheduling Systems based on Time Windows and Constraint Programming”, in the 11th IFAC Workshop on Intelligent Manufacturing Systems, 2013, 192-197.
PWC, Industry 4.0: Building the digital enterprise, 2016, taken from: www.pwc.com/industry40 (accessed on May 23, 2019).
Shin, K., Kuroda, M., and Fukusako, S., “A Practical Scheduling Framework fbr Component Manufacturers in an APS Environment”, Japan Industrial Management Association, vol. 55, 2004, 59-68.
SIROPACK, “Industry 4.0: un nuovo nome per l’innovazione Siropack”, 2017, taken from: http://www.siropack.it/industry-4-0-un-nuovo-nome-per-linnovazione-siropack(accessed on May 23, 2019)
Stadtler, H., Kilger, C., and Meyr, H., Supply Chain Management and Advanced Planning Concepts: Models, Software, and Case Studies, Springer, Heidelberg, 5th Edition, 2015.
Vidoni, M.C. and Vecchietti, A.R., “A systemic approach to define and characterize Advanced Planning Systems (APS)”, Computers & Industrial Engineering, vol. 90, 2015, 326–338.
Wiers, V.C.S. and de Kok, A.G., Designing, Selecting, Implementing and Using APS Systems, Springer, Switzerland, 2018.
Zhong, R.Y., Li, Z., Pang, L.Y., Pan, Y., Qu, T., and Huang, G.Q., “RFID-enabled real-time advanced planning and scheduling shell for production decision making”, International Journal of Computer Integrated Manufacturing, vol. 26, 2013, 7: 649-662.