Multi-objective buffer space allocation with the cross-entropy method

International Journal of Simulation Modelling

Volumn 12, Issue 1, 2013, Pages 50-61

  Bekker, J ^a  

^a STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

Buffer allocation; Cross Entropy; Multi Objective optimisation; Simulation

Indexed keywords

EID: 84875016549     PISSN: 17264529     EISSN: None     Source Type: Journal    
DOI: 10.2507/IJSIMM12(1)5.228     Document Type: Article

References (29)

1. Rubinstein, R. Y. (1997). Optimization of computer simulation models with rare events, European Journal of Operations Research, Vol. 99, No. 1, 89-112, doi:10.1016/S03772217(96)00385-2
2. Rubinstein, R. Y.; Kroese, D. P. (2004). The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning, Springer, New York
3. Bekker, J.; Aldrich, C. (2010). The cross-entropy method in multi-objective optimisation: An assessment, European Journal of Operational Research, Vol. 211, No. 1, 112-121, doi:10.1016/j.ejor.2010.10.028
4. nd ed., Springer, New York
5. Cruz, F. R. B.; Van Woensel, T.; MacGregor Smith, J. (2010). Buffer and throughput trade-offs in MG1K queuing networks: A bi-criteria approach, International Journal of Production Economics, Vol. 125, No. 2, 224-234, doi:10.1016/j.ijpe.2010.02.017
6. Cruz, F. R. B.; Duarte, A. R.; Van Woensel, T. (2008). Buffer allocation in general single-server queuing networks, Computers & Operations Research, Vol. 35, No. 11, 3581-3598, doi:10.1016/j.cor.2007.03.004
7. Papadopoulos, H. T.; Heavey, C. (1996). Queuing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines, European Journal of Operational Research, Vol. 92, No. 1, 1-27, doi:10.1016/0377-2217(95)00378-9
8. Vouros, G. A.; Papadopoulos, H. T. (1998). Buffer allocation in unreliable production lines using a knowledge-based system, Computers & Operations Research, Vol. 25, No. 12, 1055-1067, doi:10.1016/S0305-0548(98)00034-3
9. Yang, S.; Wu, C.; Hu, S. J. (2000). Modeling and analysis of multi-stage transfer lines with unreliable machines and finite buffers, Annals of Operations Research, Vol. 93, No. 1-4, 405-421, doi:10.1023/A:1018944411591
10. Hillier, F. S.; So, K. C. (1991). The effect of machine breakdowns and interstage storage on the performance of production line systems, International Journal of Production Research, Vol. 29, No. 10, 2043-2055, doi:10.1080/00207549108948066
11. Gershwin, S. B.; Schor, J. E. (2000). Efficient algorithms for buffer space allocation, Annals of Operations Research, Vol. 93, No. 1, 117-144, doi:10.1023/A:1018988226612
12. Heavey, C.; Papadopoulos, H. T.; Browne, J. (1993). The throughput rate of multi-station unreliable production lines, European Journal of Operational Research, Vol. 68, No. 1, 69-89, doi:10.1016/0377-2217(93)90077-Z
13. MacGregor Smith, J.; Cruz, F. R. B. (2005). The buffer allocation problem for general finite buffer queuing networks, IIE Transactions, Vol. 37, No. 4, 343-365, doi:10.1080/ 07408170590916986
14. Can, B.; Heavey, C. (2011). Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems, Computers & Industrial Engineering, Vol. 61, No. 3, 447-462, doi:10.1016/j.cie.2011.03.012
15. Cao, Y.; Subramaniam, V.; Chen, C. (2012). Performance evaluation and enhancement of multistage manufacturing systems with rework loops, Computers & Industrial Engineering, Vol. 62, No. 1, 161-176, doi:10.1016/j.cie.2011.09.004
16. Lutz, C. M.; Davis, K. R., Sun, M. (1998). Determining buffer location and size in production lines using tabu search, European Journal of Operational Research, Vol. 106, No. 2-3, 301-316, doi:10.1016/S0377-2217(97)00276-2
17. Dolgui, A.; Eremeev, A.; Kolokolov, A.; Sigaev, V. (2002). A genetic algorithm for the allocation of buffer storage capacities in a production line with unreliable machines, Journal of Mathematical Modelling and Algorithms, Vol. 1, No. 2, 89-104, doi:10.1023/A:1016560109076
18. Malakooti, B. (1991). A multiple criteria decision making approach for the assembly line balancing problem, International Journal of Production Research, Vol. 29, No. 10, 1979-2001, doi:10.1080/00207549108948063
19. Battini, D.; Persona, A.; Regattieri, A. (2009). Buffer size design linked to reliability performance: A simulative study, Computers & Industrial Engineering, Vol. 56, No. 4, 1633-1641, doi:10.1016/j.cie.2008.10.020
20. CEM Website, from http://www.cemethod.org, accessed on 02-04-2012
21. Kroese, D. P.; Rubinstein, R. Y. (Eds.), (2005). Special issue: The Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation, Annals of Operations Research, Vol. 134, No. 1
22. Kroese, D. P. (2010). Cross-entropy method, Cochran, J. J.; Cox, L. A.; Keskinocak, P.; Kharoufeh, J. P.; Cole Smith, J. (Eds.), Wiley Encyclopaedia of Operations Research and Management Science, John Wiley & Sons, Inc., New York
23. Alon, G.; Kroese, D. P.; Raviv, T.; Rubinstein, R. Y. (2005). Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment, Annals of Operations Research, Vol. 134, No. 1, 137-151, doi:10.1007/s10479-005-5728-8
24. Goldberg, D. E. (1989). Genetic Algorithms in search, optimization and machine learning, Addison-Wesley Publishing Company, Boston
25. rd ed., McGraw-Hill, New York
26. rd ed., McGraw-Hill, New York
27. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, 182-197, doi:10.1109/4235.996017
28. Gomes, J. H. F.; Salgado Jr., A. R.; De Paiva, A. P.; Ferreira, J. R.; Da Costa, S. C.; Balestrassi, P. P. (2012). Global Criterion Method Based on Principal Components to the Optimization of Manufacturing Processes with Multiple Responses, Strojniski vestnik - Journal of Mechanical Engineering, Vol. 58, No. 5, 345-353, doi:10.5545/sv-jme.2011.136
29. Galzina, V.; Lujic, R.; Saric, T. (2012). Adaptive fuzzy particle swarm optimization for flow shop scheduling problem, Technical Gazette, Vol. 19, No. 1, 151-157