Data-based scheduling framework and adaptive dispatching rule of complex manufacturing systems

International Journal of Advanced Manufacturing Technology

Volumn 66, Issue 9-12, 2013, Pages 1891-1905

  Li, Li ^a   Zijin, Sun ^a   Jiacheng, Ni ^a   Fei, Qiao ^a  

^a TONGJI UNIVERSITY   (China)

Author keywords

Complex manufacturing system; Data mining; Data based; Scheduling

Indexed keywords

BACKWARD PROPAGATION; BINARY REGRESSION MODEL; COMPLEX MANUFACTURING SYSTEMS; DATA-BASED; OPERATIONAL PERFORMANCE; PARTICLE SWARM OPTIMIZATION ALGORITHM; SCHEDULING FRAMEWORKS; SEMICONDUCTOR MANUFACTURING SYSTEMS;

ALGORITHMS; DATA MINING; MANUFACTURE; PARTICLE SWARM OPTIMIZATION (PSO); REGRESSION ANALYSIS; SCHEDULING; SEMICONDUCTOR DEVICE MANUFACTURE;

COMPLEX NETWORKS;

EID: 84879070876     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-012-4468-6     Document Type: Article

References (57)

1. Kusiak A (2001) Rough set theory a data mining tool for semiconductor manufacturing. IEEE Trans Electron Packing Manuf 24(1):44-50
2. Kusiak A (2000) Decomposition in data mining an industrial case study. IEEE Trans Electron packing Manuf 23(4):345-353
3. Kusiak A (2001) Feature transformation methods in data mining. IEEE Trans Electron Packing Manuf 24(4):214-221
4. Chen YM, Miao DQ, Wang RZ (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recognit Lett 31(3):226-233
5. Shine YR, Su CT (2002) Attribute selection for neural network based adaptive scheduling systems in flexible manufacturing systems. Int J Adv Manuf Technol 20(7):532-544
6. Shiue YR, Guh RS (2006) The optimization of attribute selection in decision tree-based production control systems. Int J Adv Manuf Technol 28(7-8):737-746
7. Shiue YR, Guh RS (2006) Learning based multi pass adaptive scheduling for a dynamic manufacturing cell environment. Rob Comput Integr Manuf 22(3):203-216
8. Shiue YR (2009) Development of two-level decision tree-based real-time scheduling system under product mix variety environment. Robot Comput-Integr Manuf 25(4-5):709-720
9. Shiue YR, Guh RS, Tseng TY (2009) GA based learning bias selection mechanism for real time scheduling systems. Expert Syst Appl 36(9):11451-11460
10. Hu CH, Su SF (2004) Hierarchical clustering methods for semiconductor manufacturing data. Proceedings of the 2004 IEEE International Conference on Networking, Sensing Control, Taiwan, pp 1063-1068
11. Chen T (2007) Predicting wafer-lot output time with a hybrid FCM-FBPN approach. IEEE Trans Dystem Man Cybern B Cybern 37(4):784-793
12. Chen T (2007) An intelligent hybrid system for wafer lot output time prediction. Adv Eng Inform 21(1):55-65
13. Koonce DA, Tsai SC (2000) Using data mining to find patterns in genetic algorithm solutions to a job shop schedule. Comput Ind Eng 38(3):361-374
14. Li XN (2006) Application of data mining in scheduling of single machine system. Ph.D. dissertation, Iowa State University
15. Rafinejad SN, Ramtin F, Arabani AB (2009) A new approach to generate rules in genetic algorithm solution to a job shop schedule by fuzzy clustering. Proceedings of the World Congress on Engineering and Computer Science, USA
16. Gradisar D, Music G (2007) Automated Petri-net modeling based on production management data. Math Comput Model DynSyst 13(3):267-290
17. Gradisar D, Music G (2007) Production-process modeling based on production-management data: a Petri-Net approach. Int J Comput Integr Manuf 20(8):794-810
18. Mueller R, McGinnis LF (2007) Automatic generation of simulation models for semiconductor manufacturing. Proceedings of the 2007 Winter Simulation Conference. Washington, pp 648-657
19. Ye K, Qiao F, Ma YM (2010) General structure of the semiconductor production scheduling model. Appl Mech Mater 20-23:465-469
20. Bagchi S, Baseman RJ, Davenport A, Natarajan R, Slonim N, Weiss S (2010) Data analytics and stochastic modeling in a semiconductor fab. Appl Stoch Model Bus Ind 26(1):1-27
21. Hosoe H, Knanamori N, Yoshida K (2007) The methods of data collection and tool processing time estimation in lot processing. In: International Symposium on Semiconductor Manufacturing. Santa Clara, Institute of Electrical and Electronics Engineers Inc., Piscataway, pp 241-244
22. Chen T (2003) A fuzzy back propagation network for output time prediction in a wafer fab. Appl Soft Comput 2(3):211-222
23. Chen T (2010) Intelligent scheduling approaches for a wafer fabrication factory. J Intell Manuf 23(3):897-911
24. Chen T, Wang YC (2009) A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory - a simulation study. Int J Adv Manuf Technol 45(1-2):110-121
25. Chung SH, Lai CM (2006) Job releasing and throughput planning for wafer fabrication under demand fluctuating make-to stock environment. Int J Adv Manuf Technol 31(3-4):316-327
26. Pearn WL, Chung SH, Lai CM (2007) Due date assignment for wafer fabrication under demand variate environment. IEEE Trans Semicond Manuf 20(2):165-175
27. Chang PC, Wang YW, Liu CH (2006) Combining SOM and GA-CBR for flow time prediction in semiconductor manufacturing factory. Lect Notes Comput Sci 4259:767-775
28. Chen T (2006) A hybrid SOM-BPN approach to lot output time prediction in a wafer fab. Neural Process Lett 24(3):271-288
29. Chen T (2007) A hybrid look-ahead SOM-FBPN and FIR system for wafer-lot-output time prediction and achievability evaluation. Int J Adv Manuf Technol 35(5-6):575-586
30. Chang PC, Liao TW (2006) Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl Soft Comput 6(2):198-206
31. Wang WP, Ze C (2008) A neuro-fuzzy based forecasting approach for rush order control applications. Expert Syst Appl 35(1-2):223-234
32. Arredondo F, Martinez E (2010) Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing. Comput Ind Eng 58(1):70-83
33. Shukla KS, Tiwari MK, Son YJ (2008) Bidding-based multi-agent system for integrated process planning and scheduling a data-mining and hybrid tabu-SA algorithm-oriented approach. Int J Adv Manuf Technol 38(1-2):163-175
34. Li TS, Huang CL, Wu ZY (2006) Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system. J Intell Manuf 17(3):355-361
35. Hsu SC, Chien CF (2007) Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. Int J Prod Econ 107:88-103
36. Rokach L, Maimon O (2006) Data mining for improving the quality of manufacturing: a feature decomposition approach. J Intell Manuf 17(3):285-299
37. Shaw MJ, Park S, Raman N (1992) Intelligent scheduling with machine learning capabilities the induction of scheduling knowledge. IIE Trans 24(2):156-168
38. Park SC, Raman N, Shaw MJ (1997) Adaptive scheduling in dynamic flexible manufacturing systems. IEEE Trans Robot Autom 13(4):486-502
39. Lee I, Shaw MJ (2000) A neural-net approach to real time flow-shop sequencing. Comput Ind Eng 38(1):125-147
40. Lee KK (2008) Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing. Appl Soft Comput 8(4):1295-1304
41. Priore P, Fuente D, Pino R (2001) Learning-based scheduling of flexible manufacturing systems using case-based reasoning. Appl Artif Intell 15(10):949-963
42. Priore P, Fuente D, Puente J, Parreno J (2006) A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Eng Appl Artif Intel 19(3):247-255
43. Priore P, Fuente D, Gomez A, Puente J (2001) A review of machine learning in dynamic scheduling of flexible manufacturing systems. Artif Intell Eng Design Anal Manuf 15(3):251-263
44. Li DC, Wu CH, Chang FM (2005) Using data-fuzzification technology in small data set learning to improve FMS. Int J Adv Manuf Technol 27(3-4):321-328
45. Li DC, Chen LS, Lin YS (2003) Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments. Int J Prod Res 41(17):4011-4024
46. Li DC, Wu CH, Tsa TI, Fengming MC (2006) Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge. Comput Oper Res 33(6):1857-1869
47. Aydin ME, Öztemel E (2000) Dynamic job-shop scheduling using reinforcement learning agents. Robot Auton Syst 33(2-3):169-178
48. Yang HB, Yan HS (2009) An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell. Int J Adv Manuf Technol 42(3-4):312-320
49. Koonce DA, Gandhi SA (2004) Applying inductive logic programming for knowledge discovery in genetic algorithms solutions to a job shop schedule. Proceedings of the IIE 2004 Annual Conference, Houston
50. Weckman GR, Ganduri CV, Koonce DA (2008) A neural network job-shop scheduler. J Intell Manuf 19(2):191-201
51. Shahzad A, Mebarki N (2010) Discovering dispatching rules for job shop scheduling problem through data mining. The 8th International Conference of Modeling and Simulation, Hammamet
52. Chaudhuri A, De K (2010) Job scheduling problem using rough fuzzy multilayer perception neural networks. J Artif Intell Theory Appl 1(1):4-19
53. Kumar S, Rao CSP (2009) Application of ant colony, genetic algorithm and data mining-based techniques for scheduling. Robot Comput-Integr Manuf 25(6):901-908
54. Li XN, Olafsson S (2005) Discovering dispatching rules using data mining. J Sched 8(6):515-527
55. Olafsson S, Li XN (2010) Learning effective new single machine dispatching rules from optimal scheduling data. Int J Prod Econ 128:118-126
56. Choi HS, Kim JS, Lee DH (2011) Real-time scheduling for reentrant hybrid flow shops: a decision tree based mechanism and its application to a TFT-LCD line. Expert Syst Appl 38(4):3514-3521
57. Kwak C, Yih Y (2004) Data-mining approach to production control in the computer-integrated testing cell. IEEE Trans Robot Autom 20(1):107-116