An effective heuristic for adaptive control of job sequences subject to variation in processing times

References

• Adams, J., E. Balas, and D. Zawack. 1988. “The Shifting Bottleneck Procedure for Job Shop Scheduling.” Management Science 34 (3): 391–401.10.1287/mnsc.34.3.391
   Web of Science ®Google Scholar
• Allahverdi, A., and H. Aydilek. 2013. “Algorithms for No-wait Flowshops with Total Completion Time Subject to Makespan.” The International Journal of Advanced Manufacturing Technology 68 (9–12): 2237–2251.10.1007/s00170-013-4836-x
   Web of Science ®Google Scholar
• Botta-Genoulaz, V. 2000. “Hybrid Flow Shop Scheduling with Precedence Constraints and Time Lags to Minimize Maximum Lateness.” International Journal of Production Economics 64 (1): 101–111.10.1016/S0925-5273(99)00048-1
   Web of Science ®Google Scholar
• Campbell, H. G., R. A. Dudek, and M. L. Smith. 1970. “A Heuristic Algorithm for the n Job, m Machine Sequencing Problem.” Management Science 16 (10): 630–637.10.1287/mnsc.16.10.B630
   Web of Science ®Google Scholar
• Cao, Q., J. W. Patterson, and X. Bai. 2005. “Reexamination of Processing Time Uncertainty.” European Journal of Operational Research 164 (1): 185–194.10.1016/j.ejor.2003.11.014
   Web of Science ®Google Scholar
• Conway, R. W., W. L. Maxwell, and L. W. Miller. 1967. Theory of Scheduling. Reading, MA: Addison-Wesley.
   Google Scholar
• Conway, R., W. Maxwell, J. O. McClain, and L. J. Thomas. 1988. “The Role of Work-in-Process Inventory in Serial Production Lines.” Operations Research 36 (2): 229–241.10.1287/opre.36.2.229
   Web of Science ®Google Scholar
• Garey, M. R., and D. S. Johnson. 1979. Computers and Intractability. San Francisco: Freeman.
   Google Scholar
• Garey, M. R., D. S. Johnson, and R. Sethi. 1976. “The Complexity of Flowshop and Jobshop Scheduling.” Mathematics of Operations Research 1 (2): 117–129.10.1287/moor.1.2.117
   Google Scholar
• Gupta, J. N. D., and E. F. Stafford Jr. 2006. “Flowshop Scheduling Research after Five Decades.” European Journal of Operational Research 169 (3): 699–711.10.1016/j.ejor.2005.02.001
   Web of Science ®Google Scholar
• Hoogenveen, J. A., and T. Kawaguchi. 1999. “Minimizing Total Completion Time in a Two-Machine Flowshop: Analysis of Special Cases.” Mathematics of Operations Research 24 (4): 887–910.10.1287/moor.24.4.887
   Web of Science ®Google Scholar
• Johnson, S. M. 1954. “Optimal Two- and Three-stage Production Schedules with Setup Times Included.” Naval Research Logistics Quarterly 1 (1): 61–68.10.1002/(ISSN)1931-9193
   Google Scholar
• Lawrence, S. R., and E. C. Sewell. 1997. “Heuristic, Optimal, Static, and Dynamic Schedules When Processing Times are Uncertain.” Journal of Operations Management 15 (1): 71–82.10.1016/S0272-6963(96)00090-3
   Google Scholar
• Li, W., X. G. Luo, D. Xue, and Y. L. Tu. 2011a. “A Heuristic for Adaptive Production Scheduling and Control in Flow Shop Production.” International Journal of Production Research 49 (11): 3151–3170.10.1080/00207540903575385
   Web of Science ®Google Scholar
• Li, W., B. R. Nault, D. Xue, and Y. L. Tu. 2011b. “An Efficient Heuristic for Adaptive Production Scheduling and Control in One-of-a-kind Production.” Computers & Operations Research 38 (1): 267–276.
   Web of Science ®Google Scholar
• Li, W., V. L. Mitchell and B. R. Nault. 2014. Inconsistent Objectives in Operating Room Scheduling. Proceedings of the 2014 Industrial and System Engineering Research Conference.
   Google Scholar
• Mahmoodi, F., C. T. Mosier, and R. E. Guerin. 1996. “The Effect of Combining Simple Priority Heuristics in Flow-dominant Shops.” International Journal of Production Research 34 (3): 819–839.10.1080/00207549608904936
   Web of Science ®Google Scholar
• Mousavi, S. M., M. Zandieh, and M. Yazdani. 2013. “A Simulated Annealing/local Search to Minimize the Makespan and Total Tardiness on a Hybrid Flowshop.” The International Journal of Advanced Manufacturing Technology 64 (1–4): 369–388.10.1007/s00170-012-4031-5
   Web of Science ®Google Scholar
• Nawaz, M., E. E. Enscore Jr., and I. Ham. 1983. “A Heuristic Algorithm for the m-Machine, n-Job Flow-shop Sequencing Problem.” OMEGA 11 (1): 91–95.10.1016/0305-0483(83)90088-9
   Web of Science ®Google Scholar
• Park, S. C., N. Raman, and M. J. Shaw. 1997. “Adaptive Scheduling in Dynamic Flexible Manufacturing Systems: A Dynamic Rule Selection Approach.” IEEE Transactions on Robotics and Automation 13 (4): 486–502.10.1109/70.611301
   Google Scholar
• Pinedo, M. L. 2012. Scheduling: Theory, Algorithms, and Systems. New York, NY: Springer.10.1007/978-1-4614-2361-4
   Google Scholar
• Pinedo, M., C. Zacharias, and N. Zhu. 2015. “Scheduling in the Service Industries: An Overview.” Journal of Systems Science and Systems Engineering 24 (1): 1–48.10.1007/s11518-015-5266-0
   Web of Science ®Google Scholar
• Ruiz, R., and C. Maroto. 2005. “A Comprehensive Review and Evaluation of Permutation Flowshop Heuristics.” European Journal of Operational Research 165 (2): 479–494.10.1016/j.ejor.2004.04.017
   Web of Science ®Google Scholar
• Taillard, E. 1993. “Benchmarks for Basic Scheduling Problems.” European Journal of Operational Research 64 (2): 278–285.10.1016/0377-2217(93)90182-M
   Web of Science ®Google Scholar
• Wan, L., R. Ma, and J. J. Yuan. 2014. “Primary-secondary Bicriteria Scheduling on Identical Machines to Minimize the Total Completion Time of All Jobs and the Maximum T-time of All Machines.” Theoretical Computer Science 518: 117–123.10.1016/j.tcs.2013.05.034
   Web of Science ®Google Scholar
• Yenisey, M. M., and B. Yagmahan. 2014. “Multi-objective Permutation Flow Shop Scheduling Problem: Literature Review, Classification and Current Trends.” OMEGA 45: 119–135.10.1016/j.omega.2013.07.004
   Web of Science ®Google Scholar