Dispatching rule selection with Gaussian processes

Central European Journal of Operations Research

Volumn 23, Issue 1, 2015, Pages 235-249

  Heger, Jens ^a   Hildebrandt, Torsten ^a   Scholz Reiter, Bernd ^a  

^a UNIVERSITY OF BREMEN   (Germany)

Author keywords

Dispatching rules; Gaussian processes; Machine learning; Planning and scheduling; Production management and logistics

Indexed keywords

EID: 84881500606     PISSN: 1435246X     EISSN: 16139178     Source Type: Journal    
DOI: 10.1007/s10100-013-0322-7     Document Type: Article

References (35)

1. Alpaydin E (2004) Introduction to machine learning (adaptive computation and machine learning series), vol 14. The MIT Press, Cambridge
2. Becker T, Windt K (2011) A comparative view on existing autonomous control approaches: observations from a simulation study. In: Hülsmann M, Scholz-Reiter B, Windt K (eds) Autonomous cooperation and control in logistics. Springer Berlin Heidelberg, pp 275–289
3. Blackstone JH, Phillips DT, Hogg GL (1982) A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res 20(1):27–45
4. Conway RW (1965) Priority dispatching and job lateness in a job shop. J Ind Eng 16:228–237
5. Dashkovskiy S, Karimi HR, Kosmykov M (2012) A Lyapunov-Razumikhin approach for stability analysis of logistics networks with time-delays. Int J Syst Sci 43(5):845–853
6. El-Bouri A, Shah P (2006) A neural network for dispatching rule selection in a job shop. Int J Adv Manuf Technol 31(3–4):342–349
7. Guneri AF, Gumus AT (2008) Artificial neural networks for finite capacity scheduling: a comparative study. Int J Ind Eng 15(4):349–359
8. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten H (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18
9. Hvalica D (2012) Solving job shop problems in the context of hypergraphs. Central Eur J Oper Res 21(1): 1–14. doi:10.1007/s10100-012-0268-1
10. Haupt R (1989) A survey of priority rule-based scheduling. Oper Res Spektrum 11(1):3–16
11. Heger J, Hildebrandt T, Scholz-Reiter B (2013) Switching dispatching rules with Gaussian processes. In: Windt K (ed) Lecture notes in production engineering: Robust manufacturing control. Springer, Berlin
12. Hentsch K, Köchel P (2011) Job scheduling with forbidden setups and two objectives using genetic algorithms and penalties. Central Eur J Oper Res 19:285–298
13. Hildebrandt T, Heger J, Scholz-Reiter B (2010) Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of the 12th annual conference on genetic and evolutionary computation, Portland, USA, pp 257–264
14. Holthaus O, Rajendran C (1997) Efficient dispatching rules for scheduling in a job shop. Int J Prod Econ 48(1):87–105
15. Holthaus O, Rajendran C (2000) Efficient jobshop dispatching rules: further developments. Prod Plan Control 11(2):171–178
16. Huffman BJ (2001) An object-oriented version of SIMLIB (a simple simulation package). INFORMS Trans Educ 2(1):1–15
17. Karimi HR, Duffie NA, Dashkovskiy S (2010) Local capacity H\_infinity control for production networks of autonomous work systems with time-varying delays. IEEE Trans Autom Sci Eng 7(4):849–857
18. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268
19. Law AM (2007) Simulation modeling and analysis, 4th edn. McGraw-Hill, Boston
20. MacKay DJC (1998) Introduction to Gaussian processes. In: Bishop CM (ed) Neural networks and machine learning, NATO ASI series, vol 168. Springer, Berlin, pp 133–165
21. Mouelhi-Chibani W, Pierreval H (2010) Training a neural network to select dispatching rules in real time. Comput Ind Eng 58(2):249–256. Scheduling in Healthcare and Industrial Systems
22. Neal RM (1996) Bayesian learning for neural networks (Lecture Notes in Statistics), 1st edn. Springer, Berlin
23. O’Hagan A (1978) Curve fitting and optimal design. J R Stat Soc 40(1):1–42
24. Paciorek CJ, Schervish MJ (2004) Nonstationary covariance functions for gaussian process regression. In: Proceedings of the conference on neural information processing systems (NIPS). MIT Press
25. Panwalkar SS, Iskander W (1977) A survey of scheduling rules. Oper Res 25(1):45–61
26. Plagemann C, Kersting K, Burgard W (2008) Nonstationary Gaussian process regression using point estimates of local smoothness. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases, vol 5212. Springer Berlin Heidelberg, pp 204–219
27. Priore P, de la Fuente D, Gomez A, Puente J (2001) A review of machine learning in dynamic scheduling of flexible manufacturing systems. AI EDAM 15(03):251–263
28. Rajendran C, Holthaus O (1999) A comparative study of dispatching rules in dynamic flowshops and jobshops. Eur J Oper Res 116(1):156–170
29. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning (adaptive computation and machine learning). The MIT Press, Cambridge
30. Rasmussen CE (1996) Evaluation of Gaussian processes and other methods for non-linear regression. PhD thesis, Department of Computer Science, University of Toronto
31. Sun YL, Yih Y (1996) An intelligent controller for manufacturing cells. Int J Prod Res 34(8):2353–2373
32. Wilbrecht JK, Prescott WB (1969) The influence of setup time on job shop performance. Manag Sci 16(4):274–280
33. Williams C (2006) Gaussian processes for machine learning—software examples. http://www.gaussianprocess.org/gpml/code/matlab/doc/
34. Williams CKI, Rasmussen CE (1996) Gaussian processes for regression. Adv Neural Inf Process Syst 8:514–520
35. Wu S-YD, Wysk RA (1989) An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing. Int J Prod Res 27(9):1603–1623