Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning

Computers and Operations Research

Volumn 39, Issue 7, 2012, Pages 1315-1324

  Zhang, Zhicong ^a   Zheng, Li ^b   Li, Na ^c   Wang, Weiping ^a   Zhong, Shouyan ^a   Hu, Kaishun ^a  

^a DONGGUAN UNIVERSITY OF TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Reinforcement learning; Scheduling; Tardiness; Unrelated parallel machines

Indexed keywords

ARRIVAL TIME; COMPUTATIONAL EXPERIMENT; DECISION TIME; DOMAIN KNOWLEDGE; DUE DATES; DYNAMIC ENVIRONMENTS; FUNCTION APPROXIMATION; NEAR-OPTIMAL POLICIES; POISSON PROCESS; PRIORITY RULES; REINFORCEMENT LEARNING METHOD; REWARD FUNCTION; SCHEDULING PROBLEM; STATE FEATURE; TARDINESS; TEST PROBLEM; UNRELATED PARALLEL MACHINES; WEIGHTED TARDINESS;

APPROXIMATION ALGORITHMS; LEARNING ALGORITHMS; MACHINERY; OPTIMIZATION; POISSON DISTRIBUTION; REINFORCEMENT; REINFORCEMENT LEARNING; SCHEDULING;

PROBLEM SOLVING;

EID: 81555208210     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2011.07.019     Document Type: Article

References (43)

1. B. Alidaee, and D. Rosa Scheduling parallel machines to minimize total weighted and unweighted tardiness Computers & Operations Research 24 8 1997 775 788
2. V.A. Armentano, and D.S. Yamashita Tabu search for scheduling on identical parallel machines to minimize mean tardiness Journal of Intelligent Manufacturing 11 5 2000 453 460 (Pubitemid 32067227)
3. M.E. Aydin, and E. Öztemel Dynamic job-shop scheduling using reinforcement learning agents Robotics and Autonomous Systems 33 2 2000 169 178
4. K.R. Baker, and J.W.M. Bertrand A dynamic priority rule for scheduling against due-dates Journal of Operations Management 3 1 1983 37 42
5. J. Bank, and F. Werner Heuristic algorithms for unrelated parallel machine scheduling with a common due date, release dates, and linear earliness and tardiness penalties Mathematical and Computer Modelling 33 4-5 2001 363 383 (Pubitemid 32122694)
6. Brauer W, Weiss G. Multi-machine scheduling: a multi-agent learning approach. In: Proceedings international conference on multi agent systems; 1998. p. 428.
7. Carroll DC. Heuristic sequencing of jobs with single and multiple components, PhD dissertation, Sloan School of Management, MIT, 1965.
8. R. Cheng, M. Gen, and T. Tozawa Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms Computers & Industrial Engineering 29 14 1995 513 517
9. Creighton DC, Nahavandi S. The application of a reinforcement learning agent to a multi-product manufacturing facility. In: Proceedings of IEEE international conference on industrial technology, Bangkok, Thailand; 2002. p. 122934.
10. B.C. Csáji, L. Monostori, and B. Kádár Reinforcement learning in a distributed market-based production control system Advanced Engineering Informatics 20 3 2006 279 288 (Pubitemid 44233382)
11. B.C. Csáji, B. Kádár, and L. Monostori Improving multiagent based scheduling by neurodynamic programming HoloMAS, LNAI 2744 2003 110 123 (Pubitemid 37095389)
12. T.K. Das, A. Gosavi, S. Mahadevan, and N. Marchalleck Solving semi-Markov decision problems using average reward reinforcement learning Management Science 45 4 1999 560 574
13. J.E. Diamond, and T.C.E. Cheng Error bound for common due date assignment and job scheduling on parallel machines IIE Transactions 32 5 2000 445 448 (Pubitemid 30540762)
14. R.O. Duda, and P.E. Hart Pattern classification and scene analysis 1973 Wiley New York
15. D.H. Eom, H.J. Shin, I.H. Kwun, J.K. Shim, and S.S. Kim Scheduling jobs on parallel machines with sequence-dependent family set-up times International Journal of Advanced Manufacturing Technology 19 12 2002 926 932
16. A.A. Gosavi Reinforcement learning algorithm based on policy iteration for average reward: empirical results with yield management and convergence analysis Machine Learning 55 1 2004 5 29
17. A. Gosavi Reinforcement learning for long-run average cost European Journal of Operational Research 155 3 2004 654 674
18. J. Hong, and V.V. Prabhu Distributed reinforcement learning control for batch sequencing and sizing in just-in-time manufacturing systems Applied Intelligence 20 2004 71 87
19. J.J. Kanet, and X. Li A weighted modified due date rule for sequencing to minimize weighted tardiness Journal of Scheduling 7 4 2004 261 276
20. D.W. Kim, D.G. Na, and F.F. Chen Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective Robotics and Computer-Integrated Manufacturing 19 1-2 2003 173 181
21. S.S. Kim, H.J. Shin, D.H. Eom, and C.O. Kim A due date density-based categorising heuristic for parallel machines scheduling The International Journal of Advanced Manufacturing Technology 22 9-10 2003 753 760
22. M.Y. Kovalyov, and F. Werner Approximation schemes for scheduling jobs with common due date on parallel machines to minimize total tardiness Journal of Heuristics 8 4 2002 415 428 (Pubitemid 34691604)
23. C.F. Liaw, Y.K. Lin, C.Y. Cheng, and M. Chen Scheduling unrelated parallel machines to minimize total weighted tardiness Computers and Operations Research 30 12 2003 1777 1789 (Pubitemid 36893352)
24. S. Mahadevan Average reward reinforcement learning method: foundations, algorithms, and empirical results Machine Learning 22 1 1996 159 196
25. J. Moddy, and C.J. Darken Fast learning in networks of locally-tuned processing units Neural Computation 1 1989 281 294
26. C.D. Paternina-Arboleda, and T.K. Das Intelligent dynamic control policies for serial production lines IIE Transactions 33 1 2001 65 77 (Pubitemid 32134357)
27. Rachamadugu RV, Morton TE. Myopic heuristics for the single machine weighted tardiness problem. Working paper #28-81-82, Graduate School of Industrial Administration, Garnegie-Mellon University; 1981.
28. R.S. Russell, E.M. Dar-El, and B.M. Taylor A comparative analysis of the COVERT job sequencing rule using various shop performance measures International Journal of Production Research 25 10 1987 1523 1540
29. Schwartz A. A reinforcement learning method for maximizing undiscounted rewards. In: Proceedings of the 10th international conference on machine learning, Morgan Kaufmann, San Mateo, CA; 1993. p. 298305.
30. S.S. Singh, V.B. Tadić, and A. Doucet A policy gradient method for semi-Markov decision processes with application to call admission control European Journal of Operational Research 178 3 2007 808 818 (Pubitemid 44765147)
31. F. Sivrikaya-Serifoglu, and G. Ulusoy Parallel machine scheduling with earliness and tardiness penalties Computers and Operations Research 26 8 1999 773 787 (Pubitemid 29351336)
32. H. Sun, and G. Wang Parallel machine earliness and tardiness scheduling with proportional weights Computers and Operations Research 30 5 2003 801 808 (Pubitemid 36175285)
33. R.S. Sutton, and A.G. Barto Reinforcement Learning: An introduction 1998 MIT Press Cambridge, Massachusetts
34. P. Tadepalli, and D. Ok Model-based average reward reinforcement learning Artificial Intelligence 100 12 1998 177 224 (Pubitemid 128403240)
35. Tamaki H, Murao H, Kitamura S. A heuristic-based hybrid solution for parallel machine scheduling problems with earliness and tardiness penalties. In: Proceedings of IEEE conference on emerging technologies and factory automation, vol. 2; 2003. p. 23944.
36. A. Vepsalainen, and T.E. Morton Priority rules for job shops with weighted tardiness costs Management Science 33 8 1987 1035 1047 (Pubitemid 17656065)
37. A. Volgenant, and E. Teerhuis Improved heuristics for the n-job single-machine weighted tardiness problem Computers & Operations Research 26 1 1999 35 44 (Pubitemid 29350684)
38. Y.C. Wang, and J.M. Usher Application of reinforcement learning for agent-based production scheduling Engineering Applications of Artificial Intelligence 18 1 2005 73 82
39. M.X. Weng, J. Lu, and H. Ren Unrelated parallel machine scheduling with setup time consideration and a total weighted completion time objective International Journal of Production Economics 70 3 2001 215 226 (Pubitemid 32272150)
40. W.Q. Xiao, and C.L. Li Approximation algorithms for common due date assignment and job scheduling on parallel machines IIE Transactions 34 5 2002 467 477
41. F. Yalaoui, and C. Chu An efficient heuristic approach for parallel machine scheduling with job splitting and sequence-dependent setup times IIE Transactions 35 2 2003 183 190
42. Y. Yi, and D.W. Wang Soft computing for scheduling with batch setup times and earliness-tardiness penalties on parallel machines Journal of Intelligent Manufacturing 14 3-4 2003 311 322
43. Zhang Z. Reinforcement learning based scheduling for final test station in semiconductor manufacturing factory, PhD dissertation, Tsinghua University, 2007.