Stochastic scheduling

Stochastic Scheduling

Volumn 9780521518512, Issue , 2012, Pages 1-191

  Sarin, Subhash C ^a   Nagarajan, Balaji ^b   Liao, Lingrui ^b  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ENGINEERING EDUCATION; PRODUCTION CONTROL; SCHEDULING; STUDENTS;

APPLIED MATHEMATICS; OBJECTIVE FUNCTIONS; OPERATIONS MANAGEMENT; PERFORMANCE MEASURE; PRODUCTION SCHEDULING; STOCHASTIC ENVIRONMENT; STOCHASTIC SCHEDULING; UNDERGRADUATE STUDENTS;

STOCHASTIC SYSTEMS;

EID: 84925616349     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511778032     Document Type: Book

References (68)

1. Agrawal, M. K., and Elmaghraby, S. E. 2001. “On computing the distribution function of the sum of independent random variables.” Computers and Operations Research 28(5):473-483.
2. Ayhan, H., and Olsen, T. L. 2000. “Scheduling of multiclass single server queues under nontraditional performance measures.” Operations Research 48(3):482-489.
3. Baker, K. R. 1974. Introduction to Sequencing and Scheduling. New York: Wiley.
4. Clark, C. E. 1961. “The greatest of a finite set of random variables.” Operations Research 9:145-162.
5. Daniels, R. L., and Chambers, R. J. 1990. “Multiobjective flow-shop scheduling.” Naval Research Logistics 37:981-995.
6. Daniels, R. L., and Kouvelis, P. 1995. “Robust scheduling to hedge against processing time uncertainty in single-stage production.” Management Science 41(2):363-376.
7. Daniels, R. L., and Carrillo, J. E. 1997. “β-Robust scheduling for single-machine systems with uncertain processing times.” IIE Transactions 29:977-985.
8. Dar-el, E. M., Herer, Y. T., and Masin, M. 1999. “CONWIP-based production lines with multiple bottlenecks: Performance and design implications.” IIE Transactions 31:99-111.
9. De, P., Ghosh, J. B., and Wells, C. E. 1992. “Expectation-variance analysis of job sequences under processing time uncertainty.” International Journal of Production Economics 28:289-297.
10. Dempster, A. P., Laird, N. M., and Rubin, D. B. 1977. “Maximum likelihood from incomplete data via the EMalgorithm.” Journal of the Royal Statistical Society, Series B (Methodological) 39(1):1-38.
11. de Kluyver, C. A. 1980. “Media selection by mean-variance analysis.” European Journal of Operational Research 5:112-117.
12. de Kluyver, C. A., and Baird, F. T. 1984. “Media selection by mean-variance analysis.” European Journal of Operational Research 16:152-156.
13. Dodin, B. 1985. “Approximating the distribution function in stochastic networks.” Computers and Operations Research 12(3):251-264.
14. Dodin, B. 1996. “Determining the optimal sequences and the distributional properties of the completion times in stochastic flow shops.” Computers and Operations Research 23:829-843.
15. Duenyas, I., Hopp, W. H., and Spearman, M. L. 1993. “Characterizing the output process of a CONWIP line with deterministic processing and random outages.” Management Science 39:975-988.
16. Elmaghraby, S. E. 1977. Activity Networks: Project Planning and Control by Network Models. New York: Wiley.
17. Forst, F. G. 1995. “Bicriteria stochastic scheduling on one or more machines.” European Journal of Operational Research 80:404-409.
18. Glover, F. 1975. “Surrogate constraint duality in mathematical programming.” Operations Research 23:434-453.
19. Held, M., and Karp, R. M. 1962. “A dynamic programming approach to sequencing problems.” The Japan Society for Industrial and Applied Mathematics (SIAM) 10:196-210.
20. Hendricks, K. B. 1992. “The output processes of serial production lines of exponential machines with finite buffers.” Operations Research 40(6):1139-1147.
21. Johnson, S. M. 1954. “Optimal two-and three-stage production schedules with setup times included.” Naval Research Logistics Quarterly 1:61-67.
22. Jung, Y. S., Nagasawa, H., and Nishiyama, N. 1990. “Bicriteria single-stage scheduling to minimize both the expected value and the variance of the total flow time.” Journal of Japan Industrial Management Association 39:76-82 (in Japanese).
23. Kouvelis, P., Daniels, R. L., and Vairaktarakis, G. 2000. “Robust scheduling of a twomachine flow shop with uncertain processing times.” IIE Transactions 32:421-432.
24. Kumar, S., and Kumar, P. R. 1994. “Fluctuation smoothing policies are stable for stochastic reentrant lines.” In: 33rd IEEE Proceedings Conference on Decision and Control, December, pp. 1476-1480.
25. Lin, C., and Lee, C. 1995. “Single-machine stochastic scheduling with dual criteria.” IIE Transactions 27:244-249.
26. Lin, K. S. 1983. “Hybrid algorithm for sequencing with bicriteria.” Journal of Optimization Theory and Applications 39(1):105-124.
27. Liu, Q., Ohno, K., and Nakayama, H. 1992. “Multiobjective discounted Markov decision processes with expectation and variance criteria.” International Journal of Systems Science 23(6):903-914.
28. Lu, S. C. H., Ramaswamy, D., and Kumar, P. R. 1994. “Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants.” IEEE Transactions on Semiconductor Manufacturing 7(3):374-385.
29. Mazzola, J. B., and Neebe, A. W. 1986. “Resource-constrained assignment scheduling.” Operations Research 34:560-572.
30. McKay, K. N., Safayeni, F. R., and Buzacott, J. A. 1988. “Job-shop scheduling theory: What is relevant?” Interfaces 18:84-90.
31. McLachlan, G. J., and Peel, D. 2001. Finite Mixture Models. New York: Wiley.
32. Mclachlan, G. J., and Krishnan, T. 1997. TheEMAlgorithm and Extensions. New York: Wiley.
33. Morizawa, K., Ono, T., Nagasawa H., and Nishiyama, N. 1993. “Aninteractive approach for searching a preferred schedule.” Journal of Japan Industrial Management Association 39:76-82.
34. Murata, T., Ishibuchi, H., and Tanaka, H. 1996. “Multiobjective genetic algorithm and its applications to flowshop scheduling.” Computers and Industrial Engineering 30:957-968.
35. Nagasawa, H., and Shing, C. 1997. “Interactive decision system in parallel-machine stochastic multi-objective scheduling.” In: Proceedings of the 1st International Conference on Engineering Design and Automation, Bangkok, Thailand, pp. 421-424.
36. Nagasawa, H., and Shing, C. 1998. “Interactive decision system in stochastic multiobjective scheduling to minimize the expected value and variance of total flow time.” Journal of the Operations Research Society of Japan 41(2):261-278.
37. Nelson, R. T., Sarin, R. K., and Daniels, R. L. 1986. “Scheduling with multiple performance measures: The one-machine case.” Management Science 32(4):464-479.
38. Pinedo, M. 2002. Scheduling: Theory, Algorithms, and Systems, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall.
39. Portougal, V., and Trietsch, D. 1998. “Makespan related criteria for comparing schedules in stochastic environments.” The Journal of the Operational Research Society. 49(11):1188-1195.
40. Rajendran, C. 1995. “Heuristics for scheduling in flowshop with multiple objectives.” European Journal of Operational Research 82:540-555.
41. Runnalls, A. R. 2007. “Kullback-Leibler approach to Gaussian mixture reduction.” IEEE Transactions on Aerospace and Electronic Systems 43(3):989-999.
42. Salmond, D. J. 1988. “Mixture reduction algorithms for uncertainty tracking.” Technical Report 88004. Royal Aerospace Establishment, Farnborough, England.
43. Sarin, S. C., and Hariharan, R. 2000. “A two machine bicriteria scheduling problem.” International Journal of Production Economics 65:125-139.
44. Sarin, S. C., and Prakash, D. 2004. “Equal processing time bicriteria scheduling on parallel machines.” Journal of Combinatorial Optimization 8:227-240.
45. Sculli, D. 1983. “The completion time of PERT networks.” Journal of the Operational Research Society 34(2):155-158.
46. Shayman, M. A., and Gaucherand, E. F. 2001. “Risk-sensitive decision theoretic diagnosis.” IEEE Transactions on Automatic Control 46(7):1167-1171.
47. Shing, C., and Nagasawa, H. 1997. “Interactive decision support system in stochastic multi-objective scheduling.” Bulletin of Osaka Prefecture University, Series A, 45(2):133-142.
48. Shing, C., and Nagasawa, H. 1999. “Interactive decision support system in stochastic multi-objective portfolio selection.” International Journal of Production Economics 60-61:187-193.
49. Shogan, A. 1977. “Bounding distributions for a stochastic PERT network.” Networks 7(4):359-381.
50. Spearman, M. L., and Hopp, W. H. 1991. “Throughput of a constant work in process manufacturing line subject to failures.” International Journal of Production Research 29:635-655.
51. Spearman, M. L., Woodruff, D. L., and Hopp, W.H. 1980. “CONWIP:Apull alternative to kanban.” International Journal of Production Research 28(5):879-894.
52. Soroush, H. M. 1994. “The most critical path in aPERTnetwork:Aheuristic approach.” European Journal of Operational Research 78(1):93-105.
53. Soroush, H. M., and Fredenhall, L. D. 1984. “The stochastic single machine scheduling problem with earliness and tardiness costs.” European Journal of Operational Research 77:187-302.
54. Srivastava, R. K., and Sarin, S. C. 1993. “Determination of part-delivery dates in a small lot stochastic assembly system.” Opsearch 30(4):281-312.
55. Tan, B. 1997. “Variance of the throughput of an n-station production line with no intermediate buffers and time dependent failures.” European Journal of Operational Research 101:560-576.
56. Tan, B. 2000. “Asymptotic variance rate of the output in production lines with finite buffers.” Annals of Operations Research 93:385-403.
57. T’kindt, V., and Billaut, J.-C. 2005. “Special issue on multi-criteria scheduling.” European Journal of Operations Research 167:796-809.
58. van de Geer, S. A. 1996. “Rates of convergence for the maximum likelihood estimator in mixture models.” Journal Nonparametric Statistics 6:293-310.
59. van de Geer, S. A. 2004. “Asymptotic theory for maximum likelihood in nonparametric mixture models.” Computational Statistics & Data Analysis 41:453-464.
60. Wang, B., and Mazumder, P. 2005. “Multivariate normal distribution based statistical timing analysis using global projection and local expansion.” In: Proceedings of the 18th International Conference on VLSI Design, pp. 380-385.
61. Wassenhove, V., and Gelders, L. F. 1980. “Solving a bi-criteria scheduling problem.” European Journal of Operational Research 4(1):42-48.
62. Wilhelm, W. E. 1986. “A model to approximate transient performance of the flowshop.” International Journal of Production Research 24(1):33-50.
63. Wilhelm, W. E. 1986. “The application of lognormal models of transient operations in the flexible manufacturing environment.” Journal of Manufacturing Systems 5(4): 253-266.
64. Wilhelm, W. E., and Ahmadi-Marandi, S. 1982. “A methodology to describe operating characteristics of assembly systems.” IIE Transactions 14(3):204-214.
65. Williams, J. L. 2005. “Gaussian mixture reduction for tracking multiple maneuvering targets in clutter.” Ph.D. thesis, Air Force Institute of Technology, Ohio.
66. Wu, C. F. J. 1983. “On the convergence properties of the EM algorithm.” Annals of Statistics 11:55-103.
67. Yang, J., and Yu, G. 2002. “On the robust single machine scheduling problem.” Journal of Combinatorial Optimization 6:17-33.
68. Yao, M., and Chu, W. 2007. “A new approximation algorithm for obtaining the probability distribution function for project completion time.” Computers and Mathematics with Applications 54(2):282-295.