Attribute Selection for the Scheduling of Flexible Manufacturing Systems Based on Fuzzy Set-theoretic Approach and Genetic Algorithm

基於模糊理論與基因演算法的彈性製造系統排程屬性選擇

10.29977/JCIIE.200501.0006

劉益宏(Yi-Hung Liu)；黃漢邦(Han-Pang Huang)；林育生(Yu-Sheng Lin)

彈性製造系統 ； 動態排程 ； 屬性選擇 ； 模糊理論 ； 基因演算法 ； Flexible manufacturing system ； dynamic scheduling ； attribute selection ； fuzzy theory ； genetic algorithm

工業工程學刊

22卷1期（2005 / 01 / 01）

46 - 55

英文

動態地給予一個彈性製造系統適合的派工法則可以增加產出、降低平均流程時間、及減少延遲工件等。為了達到這個目的，即時的系統顯著資訊必須擷取，然後依據萃取出的資訊建立派工機制。兩個重要的議題主導著動態排程彈性製造系統的性能；其一是如何選取有用的系統屬性，另外就是派工機制的設計。本論文目標在於解決第一個議題。 一個好的屬性評估方法必須要提供哪些屬性該選擇，哪些該捨去的資訊。因此，在本文中提出了一個以模糊理論及基因演算法為基礎的監督式屬性探勘演算法(supervised attribute mining algorithm, SAMA)來處理上述的問題。此演算法可以依照各屬性彼此之間關聯程度的重要性來給予排序。本文實驗利用模擬軟體建立了一個彈性製造系統來驗證此演算法的有效性。實驗結果指出SAMA可以達到屬性評估跟最佳化的屬性子集合選取等目的。更進一步，利用所得出的最佳化屬性子集合與未經過SAMA處理的系統全部屬性來做比較。最後，結果顯示經由SAMA選擇的屬性當成排程器的輸入可以提升彈性製造系統的性能。

Assigning proper dispatching rules dynamically has been shown to enhance various performance measures for a flexible manufacturing system (FMS). To achieve this, real-time salient information of the system is extracted and then a rule's dispatching mechanism is built for the scheduling task. For a dynamic scheduled FMS, two critical issues dominate the performance; the first is the selection of system attributes and the second is the design of the dispatching mechanism. This paper aims to deal with the first issue. A good attribute evaluation method should provide the information from which attribute are selected or removed. In this paper, a supervised attribute mining algorithm (SAMA), which is based on the fuzzy set-theoretic approach and genetic algorithm (GA), is proposed to execute this function. SAMA is able to rank attributes according to their relative importance. In the experiment, a FMS is conducted to demonstrate the validity of the proposed SAMA. The experimental results indicate that the attribute evaluation task and optimal attribute subset selection can be achieved by using the SAMA. Moreover, compared with using all system attributes without selection, performance of the FMS can be improved by using the optimal attributes as input of the scheduler.

1. Arzi, Y.,L. Iaroslavitz(2000).Operating an FMC by a decision-tree-based adaptive production control system.International Journal of Production Research,38,675-697.
2. Bezdek, J. C.(1981).Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum Press.
3. Bezdek, J. C.,P. F. Castelaz(1977).Prototype classification and feature selection with fuzzy sets.IEEE Transaction on Systems, man, and Cybernetics,7,87-92.
4. Chan, F. T. S.,H. K. Chan,H. C. W. Lau,R. W. L. Ip(2003).Analysis of Dynamic Dispatching Rules for a Flexible Manufacturing System.Journal of Materials Processing Technology,138,325-331.
5. Chase, R. B.,N. J. Aquilano(1995).Production and Operations Management-Manufacturing and Services.IRWIN.
6. Chen, C. C.,Y. Yih(1993).Identifying attributes for knowledge-based development in dynamic scheduling environments.International Journal of Production Research,34,1739-1755.
7. Cho, H.,R. A. Wysk(1993).A robust adaptive scheduler for an intelligent workstation controller.International Journal of Production Research,31,771-789.
8. De, R. K.,N. R. Pal,S. K. Pal(1997).Feature analysis: Neural network and fuzzy set theoretic approaches.Pattern Recognition,30,1579-1590.
9. Devijver, P. A.,J. Kittler(1982).Pattern Recognition: A Statistical Approach.London:Prentice-Hall.
10. Gen, Mitsuo,Runwei Cheng(1997).Genetic Algorithms & Engineering Design.John Wiley & Sons, Inc.
11. Goyal, S. K.,K. Mehta,R. Kodali,S. G. Deshmukh(1995).Simulation for Analysis of Scheduling Rules for a Flexible Manufacturing System.Integrated Manufacturing Systems,6(5),21-26.
12. Himmelblau, D. M.(1976).Applied Nonlinear Programming.New York:McGrawHill.
13. Huang, H. P.,Y. H. Liu(2001).A GA-based fuzzy feature evaluation algorithm for pattern recognition.Proceedings of the 10th IEEE International Conference on Fuzzy Systems,2,833-836.
14. Ip, W. H.,K. L. Yung,H. Min,D. Wang(2002).A CONWIP model for FMS control.Journal of Intelligent Manufacturing,13(2),109-117.
15. Jahangirian, M.,G. V. Conroy(2000).Intelligent Dynamic Scheduling System: the Application of Genetic Algorithms.Integrated Manufacturing Systems,11(4),247-257.
16. Jeng, M. D.,C. Shilin,Y. S. Huang(1999).Petri Net Dynamics-Based Scheduling of Flexible Manufacturing Systems with Assembly.Journal of Intelligent Manufacturing,10(6),514-555.
17. Jeong, K. C.,Y. D. Kim(1998).A Real-Time Scheduling Mechanism for a Flexible Manufacturing System: Using Simulation and Dispatching Rules.International Journal of Production Research,36(9),527-546.
18. Kaufmann, A.,M. Gupta(1985).Introduction to fuzzy arithmetic: theory and applications.New York:Van Nostrand Reinhold Co..
19. Kim, C. O.,H. S. Min,Y. Yih(1998).Integration of Inductive Learning and Neural Networks for Multi-Objective FMS Scheduling.International Journal of Production Research,36(9),2497-2509.
20. Kraaijveld, M. A.,J. Mao,A. K. Jain(1995).A non-linear projection method based on Kohonen`s topology preserving maps.IEEE Transactions on Neural Networks,6,548-559.
21. Laarhoven, P. J. M. van,E. H. L. Aarts(1987).Simulated Annealing: Theory and Applications.Published by D. Reidel Publishing Company.
22. Lampinen, J.,E. Oja(1995).Distortion tolerant pattern recognition based on self-organizing feature extraction.IEEE Transactions on Neural Networks,6,539-547.
23. Langevin, A.,D. Lauzon,D. Riopel(1996).Dispatching, Routing, and Scheduling of Two Automated Guided Vehicles in a FMS.International Journal of Flexible Manufacturing Systems,8,247-262.
24. Liu, J.,B. L. MacCarthy(1999).General Heuristic Procedures and Solution Strategies for FMS Scheduling.International Journal of Production Research,37(14),3305-3333.
25. Luca, A. D.,S Termini(1972).A definition of non probabilistic entropy in the setting of fuzzy set theory.Information and Control,20,301-312.
26. Min, H. S.(2002).Purdue University.
27. Montazeri, M.,L. N. Van Wassenhove(1990).Analysis of scheduling rules for an FMS.International Journal of Production Research,28(4),785-802.
28. Pal, S. K.,Basabi Chakraborty(1986).Fuzzy set theoretic measure for automatic feature evaluation.IEEE Transactions on Systems, Man, and Cybernetics,16,754-760.
29. Pal, S. K.,Basabi Chakraborty(1984).Intraclass and interclass ambiguities (fuzziness) in feature evaluation.Pattern Recognition Latter,2,275-279.
30. Pal, S. K.,R. K. De,J. Basak(2000).Unsupervised feature evaluation: A neuro-fuzzy approach.IEEE Transactions on Neural Networks,11(2),366-376.
31. Park, S. C.,N. Raman,M. J. Shaw(1997).Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach.IEEE Transactions on Robotics and Automation,13,486-502.
32. Priddy, K. L.,S. K. Rogers,D. W. Ruck,G. L. Tarr,M. Kabrisky(1993).Bayesian selection of important features for feedforward neural network.Neurocomputing,5,91-103.
33. Innovation in Manufacturing Systems and Technology (IMST)
34. Rau, K. R.,O. V. K. Chetty(1996).Production Planning of FMS under Tool Magazine Constraints: a Dynamic Programming Approach.International Journal of Advanced Manufacturing Technology,11,366-371.
35. Reyes, A.,H. Yu,G. Kelleher,S. Lloyd(2002).Integrating Petri Nets and Hybrid Heuristic Search for the Scheduling of FMS.Computers in Industry,47,123-138.
36. Ruck, D. W.,S. K. Rogers,M. Kabrisky(1990).Feature selection using a multilayer perception.Neural Network Computing,20,40-48.
37. Sabuncuoglu I.(1998).A Study of Scheduling Rules of Flexible Manufacturing Systems: a Simulation Approach.International Journal of Production Research,36(2),527-546.

1. Tsai, Chiao-Ju,Huang, Han-Pang(2007).A REAL-TIME SCHEDULING AND RESCHEDULING SYSTEM BASED ON RFID FOR SEMICONDUCTOR FOUNDRY FABS.工業工程學刊,24(6),437-445.