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Linked References

1. L. Mönch, J. W. Fowler, S. Dauzère-Pérès, S. J. Mason, and O. Rose, “A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations,” Journal of Scheduling, vol. 14, no. 6, pp. 583–599, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
2. R. Gokhale and M. Mathirajan, “Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 60, no. 9–12, pp. 1099–1110, 2012. View at Publisher · View at Google Scholar
3. S. Q. Liu and E. Kozan, “Scheduling trains with priorities: a no-wait blocking parallel-machine job-shop scheduling model,” Transportation Science, vol. 45, no. 2, pp. 175–198, 2011. View at Publisher · View at Google Scholar · View at Scopus
4. C.-J. Liao, C.-M. Chen, and C.-H. Lin, “Minimizing makespan for two parallel machines with job limit on each availability interval,” Journal of the Operational Research Society, vol. 58, no. 7, pp. 938–947, 2007. View at Publisher · View at Google Scholar · View at Scopus
5. C. N. Potts and V. A. Strusevich, “Fifty years of scheduling: a survey of milestones,” Journal of the Operational Research Society, vol. 60, no. 1, pp. S41–S68, 2009. View at Publisher · View at Google Scholar · View at Scopus
6. D. Biskup, J. Herrmann, and J. N. D. Gupta, “Scheduling identical parallel machines to minimize total tardiness,” International Journal of Production Economics, vol. 115, no. 1, pp. 134–142, 2008. View at Publisher · View at Google Scholar · View at Scopus
7. A. Jouglet and D. Savourey, “Dominance rules for the parallel machine total weighted tardiness scheduling problem with release dates,” Computers & Operations Research, vol. 38, no. 9, pp. 1259–1266, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
8. S.-W. Lin, Z.-J. Lee, K.-C. Ying, and C.-C. Lu, “Minimization of maximum lateness on parallel machines with sequence-dependent setup times and job release dates,” Computers & Operations Research, vol. 38, no. 5, pp. 809–815, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
9. A. J. Ruiz-Torres, F. J. López, and J. C. Ho, “Scheduling uniform parallel machines subject to a secondary resource to minimize the number of tardy jobs,” European Journal of Operational Research, vol. 179, no. 2, pp. 302–315, 2007. View at Publisher · View at Google Scholar · View at Scopus
10. J. Banks, J. S. Carson, B. L. Nelson, and D. M. Nicol, Discrete-Event System Simulation, Prentice Hall, Upper Saddle River, NJ, USA, 5th edition, 2009.
11. P. Brucker, Scheduling Algorithms, Springer, Berlin, Germany, 5th edition, 2007.
12. E. Kozan and B. Casey, “Alternative algorithms for the optimization of a simulation model of a multimodal container terminal,” Journal of the Operational Research Society, vol. 58, no. 9, pp. 1203–1213, 2007. View at Publisher · View at Google Scholar · View at Scopus
13. K. Muthuraman and H. Zha, “Simulation-based portfolio optimization for large portfolios with transaction costs,” Mathematical Finance, vol. 18, no. 1, pp. 115–134, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
14. G. Van Ryzin and G. Vulcano, “Simulation-based optimization of virtual nesting controls for network revenue management,” Operations Research, vol. 56, no. 4, pp. 865–880, 2008. View at Publisher · View at Google Scholar · View at Scopus
15. R. Pasupathy, “On choosing parameters in retrospective-approximation algorithms for stochastic root finding and simulation optimization,” Operations Research, vol. 58, no. 4, part 1, pp. 889–901, 2010. View at Publisher · View at Google Scholar · View at Scopus
16. D. Bertsimas, O. Nohadani, and K. M. Teo, “Robust optimization for unconstrained simulation-based problems,” Operations Research, vol. 58, no. 1, pp. 161–178, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
17. A. K. Miranda and E. Del Castillo, “Robust parameter design optimization of simulation experiments using stochastic perturbation methods,” Journal of the Operational Research Society, vol. 62, no. 1, pp. 198–205, 2011. View at Publisher · View at Google Scholar · View at Scopus
18. S. H. Melouk, B. B. Keskin, C. Armbrester, and M. Anderson, “A simulation optimization-based decision support tool for mitigating traffic congestion,” Journal of the Operational Research Society, vol. 62, no. 11, pp. 1971–1982, 2011. View at Publisher · View at Google Scholar
19. L. Napalkova and G. Merkuryeva, “Multi-objective stochastic simulation-based optimisation applied to supply chain planning,” Technological and Economic Development of Economy, vol. 18, no. 1, pp. 132–148, 2012. View at Publisher · View at Google Scholar
20. Y. Zhang, M. L. Puterman, M. Nelson, and D. Atkins, “A simulation optimization approach to long-term care capacity planning,” Operations Research, vol. 60, no. 2, pp. 249–261, 2012. View at Publisher · View at Google Scholar
21. Y. C. Ho, R. S. Sreenivas, and P. Vakili, “Ordinal optimization of DEDS,” Discrete Event Dynamic Systems, vol. 2, no. 1, pp. 61–88, 1992. View at Publisher · View at Google Scholar · View at Scopus
22. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
23. G. Onwubolu and D. Davendra, “Scheduling flow shops using differential evolution algorithm,” European Journal of Operational Research, vol. 171, no. 2, pp. 674–692, 2006. View at Publisher · View at Google Scholar · View at Scopus
24. L. Wang, Q.-K. Pan, P. N. Suganthan, W.-H. Wang, and Y.-M. Wang, “A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems,” Computers & Operations Research, vol. 37, no. 3, pp. 509–520, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
25. Y.-C. Ho, Q.-C. Zhao, and Q.-S. Jia, Ordinal Optimization: Soft Optimization for Hard Problems, Springer, New York, NY, USA, 2007.
26. S.-C. Horng and S.-S. Lin, “An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application,” Expert Systems with Applications, vol. 36, no. 5, pp. 9340–9349, 2009. View at Publisher · View at Google Scholar · View at Scopus
27. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. View at Scopus
28. C.-H. Chen, E. Yücesan, L. Dai, and H.-C. Chen, “Optimal budget allocation for discrete-event simulation experiments,” IIE Transactions, vol. 42, no. 1, pp. 60–70, 2010. View at Publisher · View at Google Scholar · View at Scopus
29. R. Haupt, “A survey of priority rule-based scheduling,” Operations-Research-Spektrum, vol. 11, no. 1, pp. 3–16, 1989. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
30. W. Y. Fowlkes, C. M. Creveling, and J. Derimiggio, Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development, Addison-Wesley, Reading, Mass, USA, 1995.
31. B. Liu, L. Wang, and Y.-H. Jin, “Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time,” in Computational Intelligence and Security, vol. 3801 of Lecture Notes in Computer Science, pp. 630–637, 2005. View at Publisher · View at Google Scholar