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Complexity
Volume 2018, Article ID 6932985, 38 pages
https://doi.org/10.1155/2018/6932985
Research Article

A Proactive Robust Scheduling Method for Aircraft Carrier Flight Deck Operations with Stochastic Durations

1Naval Aviation University, Yantai 264001, China
2College of Aerospace Engineering, Chongqing University, Chongqing 400044, China

Correspondence should be addressed to Yu Wu; nc.ude.uqc@uyuwuqc

Received 4 April 2018; Revised 5 August 2018; Accepted 14 August 2018; Published 1 November 2018

Academic Editor: Hassan Zargarzadeh

Copyright © 2018 Xichao Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Liu and K. Liu, “Advances in carrier-based aircraft deck operation scheduling,” Systems Engineering - Theory & Practice, vol. 37, no. 1, pp. 49–60, 2017. View at Google Scholar
  2. US Naval Air Systems Command, CVN Flight-Hanger Deck NATOPS Manual, NACYAIR 00-80T-120, Washington, DC, USA, 2008.
  3. A. Jewell, M. A. Wigge, C. M. Gagnon, M. Colleen, L. A. Lynn, and K. M. Kirk, USS Nimitz and Carrier Airwing Nine Surge Demonstration, US Navy: Center for Naval Analyses, 1998.
  4. A. Jewell, Sortie generation capacity of embarked airwings, US Navy: Center for Naval Analyses, 1998.
  5. T. Jiang, X. Su, and W. Han, “Optimization of support scheduling on deck of carrier aircraft based on improved differential evolution algorithm,” in 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), pp. 136–140, Beijing, China, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Demeulemeester, “Robust project scheduling,” Foundations and Trends® in Technology, Information and Operations Management, vol. 3, no. 3-4, pp. 201–376, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. R. G. Dastidar and E. Frazzoli, “A queueing network based approach to distributed aircraft carrier deck scheduling,” in Infotech@Aerospace 2011, St. Louis, MO, USA, 2011. View at Publisher · View at Google Scholar
  8. L. Yu, C. Zhu, J. Shi, and W. Zhang, “An extended flexible job shop scheduling model for flight deck scheduling with priority, parallel operations, and sequence flexibility,” Scientific Programming, vol. 2017, Article ID 2463252, 15 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. W. W. Shi, W. Han, and W. C. Si, “Optimization of direct flight line maintenance process for multi-carrier planes,” Computer Engineering and Design, vol. 34, no. 12, pp. 4214–4219, 2013. View at Google Scholar
  10. J. C. Ryan, A. G. Banerjee, M. L. Cummings, and N. Roy, “Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations,” IEEE Transactions on Cybernetics, vol. 44, no. 6, pp. 761–773, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. X. C. Su, W. Han, W. Xiao, and T. T. Jiang, “Pit-stop support scheduling on deck of carrier plane based on memetic algorithm,” Systems Engineering and Electronics, vol. 38, no. 10, pp. 2303–2309, 2016. View at Google Scholar
  12. S. Tao, C. Wu, Z. Sheng, and X. Wang, “Space-time repetitive project scheduling considering location and congestion,” Journal of Computing in Civil Engineering, vol. 32, no. 3, 2018. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Wu, X. Pan, R. Kang, C. He, and L. Gong, “Multi-parameters uncertainty analysis of logistic support process based on GERT,” Journal of Systems Engineering and Electronics, vol. 25, no. 6, pp. 1011–1019, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. J. C. Ryan, M. L. Cummings, N. Roy, A. Banerjee, and A. Schulte, “Designing an interactive local and global decision support system for aircraft carrier deck scheduling,” in Infotech@Aerospace 2011, pp. 1–12, St. Louis, MO, USA, 2011. View at Publisher · View at Google Scholar
  15. Q. Feng, S. K. Zeng, and R. Kang, “A MAS-based model for dynamic scheduling of carrier aircraft,” Acta Aeronautica Et Astronautica Sinica, vol. 30, no. 11, pp. 2119–2125, 2009. View at Google Scholar
  16. Q. Feng, S. Li, and B. Sun, “A multi-agent based intelligent configuration method for aircraft fleet maintenance personnel,” Chinese Journal of Aeronautics, vol. 27, no. 2, pp. 280–290, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. J. C. Ryan and M. L. Cummings, “A systems analysis of the introduction of unmanned aircraft into aircraft carrier operations,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 2, pp. 209–220, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Qi and D. Wang, “Dynamic aircraft carrier flight deck task planning based on HTN,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 1608–1613, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Wu and X. Qu, “Obstacle avoidance and path planning for carrier aircraft launching,” Chinese Journal of Aeronautics, vol. 28, no. 3, pp. 695–703, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Michini and J. P. How, “A human-interactive course of action planner for aircraft carrier deck operations,” in Infotech@Aerospace 2011, pp. 1–11, St. Louis, MO, USA, 2011. View at Publisher · View at Google Scholar
  21. Q. Feng, W. Bi, B. Sun, and Y. Ren, “Dynamic scheduling of carrier aircraft based on improved ant colony algorithm under disruption and strong constraint,” in 2017 Second International Conference on Reliability Systems Engineering (ICRSE), pp. 1–9, Beijing, China, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. O. Lambrechts, E. Demeulemeester, and W. Herroelen, “Time slack-based techniques for robust project scheduling subject to resource uncertainty,” Annals of Operations Research, vol. 186, no. 1, pp. 443–464, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. F. Ballestín, “When it is worthwhile to work with the stochastic RCPSP?” Journal of Scheduling, vol. 10, no. 3, pp. 153–166, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Masmoudi and A. Haït, “Project scheduling under uncertainty using fuzzy modelling and solving techniques,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 135–149, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Li, Z. Xu, L. Xiong, and Y. Liu, “Robust proactive project scheduling model for the stochastic discrete time/cost trade-off problem,” Discrete Dynamics in Nature and Society, vol. 2015, Article ID 586087, 10 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. W. X. Wang, X. Wang, X. L. Ge, and L. Deng, “Multi-objective optimization model for multi-project scheduling on critical chain,” Advances in Engineering Software, vol. 68, no. 1, pp. 33–39, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Van de Vonder, E. Demeulemeester, and W. Herroelen, “Proactive heuristic procedures for robust project scheduling: an experimental analysis,” European Journal of Operational Research, vol. 189, no. 3, pp. 723–733, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. F. Deblaere, E. Demeulemeester, W. Herroelen, and S. van de Vonder, “Robust resource allocation decisions in resource-constrained projects,” Decision Sciences, vol. 38, no. 1, pp. 5–37, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. N. Pang, H. Su, and Y. Shi, “Project robust scheduling based on the scattered buffer technology,” Applied Sciences, vol. 8, no. 4, p. 541, 2018. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Lamas and E. Demeulemeester, “A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations,” Journal of Scheduling, vol. 19, no. 4, pp. 409–428, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. M. E. Bruni, L. di Puglia Pugliese, P. Beraldi, and F. Guerriero, “An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations,” Omega, vol. 71, pp. 66–84, 2017. View at Publisher · View at Google Scholar · View at Scopus
  32. P. Ghoddousi, R. Ansari, and A. Makui, “An improved robust buffer allocation method for the project scheduling problem,” Engineering Optimization, vol. 49, no. 4, pp. 718–731, 2017. View at Publisher · View at Google Scholar · View at Scopus
  33. R. K. Chakrabortty, R. A. Sarker, and D. L. Essam, “Resource constrained project scheduling with uncertain activity durations,” Computers & Industrial Engineering, vol. 112, pp. 537–550, 2017. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Rostami, S. Creemers, and R. Leus, “New strategies for stochastic resource-constrained project scheduling,” Journal of Scheduling, vol. 21, no. 3, pp. 349–365, 2018. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Tao, C. Wu, Z. Sheng, and X. Wang, “Stochastic project scheduling with hierarchical alternatives,” Applied Mathematical Modelling, vol. 58, pp. 181–202, 2018. View at Publisher · View at Google Scholar · View at Scopus
  36. H. Li and N. K. Womer, “Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming,” European Journal of Operational Research, vol. 246, no. 1, pp. 20–33, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. D. Morillo Torres, L. F. Moreno Velasquez, and F. J. Díaz Serna, “A branch and bound hybrid algorithm with four deterministic heuristics for the resource constrained project scheduling problem (RCPSP),” DYNA, vol. 82, no. 190, pp. 198–207, 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. S. Chand, Q. Huynh, H. Singh, T. Ray, and M. Wagner, “On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems,” Information Sciences, vol. 432, pp. 146–163, 2018. View at Publisher · View at Google Scholar · View at Scopus
  39. J. F. Gonçalves, J. J. M. Mendes, and M. G. C. Resende, “A genetic algorithm for the resource constrained multi-project scheduling problem,” European Journal of Operational Research, vol. 189, no. 3, pp. 1171–1190, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. E. Pérez, M. Posada, and A. Lorenzana, “Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms,” Soft Computing, vol. 20, no. 5, pp. 1879–1896, 2016. View at Publisher · View at Google Scholar · View at Scopus
  41. R. Yan, W. Li, P. Jiang, Y. Zhou, and G. Wu, “A modified differential evolution algorithm for resource constrained multi-project scheduling problem,” Journal of Computers, vol. 9, no. 8, p. 1922, 2014. View at Publisher · View at Google Scholar
  42. L. Wang and C. Fang, “A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem,” Expert Systems with Applications, vol. 39, no. 3, pp. 2451–2460, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Tian, X. Hao, and T. Murata, “Robust optimization method based on hybridization of GA and MMEDA for resource constraint project scheduling with uncertainty,” IEEJ Transactions on Electronics, Information and Systems, vol. 137, no. 7, pp. 957–966, 2017. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Elsayed, R. Sarker, T. Ray, and C. C. Coello, “Consolidated optimization algorithm for resource-constrained project scheduling problems,” Information Sciences, vol. 418-419, pp. 346–362, 2017. View at Publisher · View at Google Scholar · View at Scopus
  45. S. Asta, D. Karapetyan, A. Kheiri, E. Özcan, and A. J. Parkes, “Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem,” Information Sciences, vol. 373, pp. 476–498, 2016. View at Publisher · View at Google Scholar · View at Scopus
  46. R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303–315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. W. Shao, D. Pi, and Z. Shao, “An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem,” Applied Soft Computing, vol. 61, pp. 193–210, 2017. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Xu, L. Wang, S. Y. Wang, and M. Liu, “An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time,” Neurocomputing, vol. 148, pp. 260–268, 2015. View at Publisher · View at Google Scholar · View at Scopus
  49. P. Kumar Roy, A. Sur, and D. K. Pradhan, “Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization,” Engineering Applications of Artificial Intelligence, vol. 26, no. 10, pp. 2516–2524, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. W. Q. Ma, C. Y. Zhang, Q. H. Tang, and Y. Jia, “Steelmaking and continuous casting scheduling based on hybrid teaching-learning-based optimization algorithm,” Computer Integrated Manufacturing Systems, vol. 21, no. 5, pp. 1271–1278, 2015. View at Google Scholar
  51. H. Zheng and L. Wang, “An effective teaching–learning-based optimisation algorithm for RCPSP with ordinal interval numbers,” International Journal of Production Research, vol. 53, no. 6, pp. 1777–1790, 2015. View at Publisher · View at Google Scholar · View at Scopus
  52. H.-y. Zheng, L. Wang, and S.-y. Wang, “A co-evolutionary teaching-learning-based optimization algorithm for stochastic RCPSP,” in 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 587–594, Beijing, China, 2014. View at Publisher · View at Google Scholar · View at Scopus
  53. H. Y. Zheng, L. Wang, and X. L. Zheng, “Teaching–learning-based optimization algorithm for multi-skill resource constrained project scheduling problem,” Soft Computing, vol. 21, no. 6, pp. 1537–1548, 2017. View at Publisher · View at Google Scholar · View at Scopus
  54. Z. Zhang, S. Lin, R. Dong, and Q. Zhu, “Designing a human-computer cooperation decision planning system for aircraft carrier deck scheduling,” in AIAA Infotech @ Aerospace, pp. 1–9, Kissimmee, FL, USA, 2015. View at Publisher · View at Google Scholar
  55. L. S. Cardona-Meza and G. Olivar-Tost, “Modeling and simulation of project management through the PMBOK® standard using complex networks,” Complexity, vol. 2017, Article ID 4791635, 12 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  56. D. Debels and M. Vanhoucke, “A decomposition-based genetic algorithm for the resource-constrained project-scheduling problem,” Operations Research, vol. 55, no. 3, pp. 457–469, 2007. View at Publisher · View at Google Scholar · View at Scopus
  57. R. Kolisch, “Serial and parallel resource-constrained project scheduling methods revisited: theory and computation,” European Journal of Operational Research, vol. 90, no. 2, pp. 320–333, 1996. View at Publisher · View at Google Scholar · View at Scopus
  58. T. A. M. Toffolo, H. G. Santos, M. A. M. Carvalho, and J. A. Soares, “An integer programming approach to the multimode resource-constrained multiproject scheduling problem,” Journal of Scheduling, vol. 19, no. 3, pp. 295–307, 2016. View at Publisher · View at Google Scholar · View at Scopus
  59. V. Valls, F. Ballestı́N, and S. Quintanilla, “Justification and RCPSP: a technique that pays,” European Journal of Operational Research, vol. 165, no. 2, pp. 375–386, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. O. Lambrechts, E. Demeulemeester, and W. Herroelen, “A tabu search procedure for developing robust predictive project schedules,” International Journal of Production Economics, vol. 111, no. 2, pp. 493–508, 2008. View at Publisher · View at Google Scholar · View at Scopus
  61. O. I. Tukel, W. O. Rom, and S. D. Eksioglu, “An investigation of buffer sizing techniques in critical chain scheduling,” European Journal of Operational Research, vol. 172, no. 2, pp. 401–416, 2006. View at Publisher · View at Google Scholar · View at Scopus
  62. W. Tian and E. Demeulemeester, “On the interaction between railway scheduling and resource flow networks,” SSRN Electronic Journal, vol. 25, no. 1-2, pp. 1–25, 2010. View at Publisher · View at Google Scholar
  63. B. Ashtiani, R. Leus, and M. B. Aryanezhad, “New competitive results for the stochastic resource-constrained project scheduling problem: exploring the benefits of pre-processing,” Journal of Scheduling, vol. 14, no. 2, pp. 157–171, 2011. View at Publisher · View at Google Scholar · View at Scopus
  64. S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  65. S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, “An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 482–500, 2012. View at Publisher · View at Google Scholar · View at Scopus
  66. V. Valls, F. Ballestín, and S. Quintanilla, “A hybrid genetic algorithm for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 185, no. 2, pp. 495–508, 2008. View at Publisher · View at Google Scholar · View at Scopus
  67. M. J. Geiger, “A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem,” European Journal of Operational Research, vol. 256, no. 3, pp. 729–741, 2017. View at Publisher · View at Google Scholar · View at Scopus
  68. Y. Niu, Z. Yang, P. Chen, and J. Xiao, “A hybrid tabu search algorithm for a real-world open vehicle routing problem involving fuel consumption constraints,” Complexity, vol. 2018, Article ID 5754908, 12 pages, 2018. View at Publisher · View at Google Scholar · View at Scopus
  69. T. Wauters, K. Verbeeck, P. de Causmaecker, and G. vanden Berghe, “A learning-based optimization approach to multi-project scheduling,” Journal of Scheduling, vol. 18, no. 1, pp. 61–74, 2015. View at Publisher · View at Google Scholar · View at Scopus
  70. T. Wauters, K. Verbeeck, G. V. Berghe, and P. de Causmaecker, “Learning agents for the multi-mode project scheduling problem,” Journal of the Operational Research Society, vol. 62, no. 2, pp. 281–290, 2011. View at Publisher · View at Google Scholar · View at Scopus
  71. Q. Jia and Y. Seo, “An improved particle swarm optimization for the resource-constrained project scheduling problem,” The International Journal of Advanced Manufacturing Technology, vol. 67, no. 9–12, pp. 2627–2638, 2013. View at Publisher · View at Google Scholar · View at Scopus
  72. F. Ballestin and R. Leus, “Resource-constrained project scheduling for timely project completion with stochastic activity durations,” Production and Operations Management, vol. 18, no. 4, pp. 459–474, 2009. View at Publisher · View at Google Scholar · View at Scopus
  73. C. Fang, R. Kolisch, L. Wang, and C. Mu, “An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem,” Flexible Services and Manufacturing Journal, vol. 27, no. 4, pp. 585–605, 2015. View at Publisher · View at Google Scholar · View at Scopus