Advertisement

Journal of Scheduling

, 12:417 | Cite as

A survey of dynamic scheduling in manufacturing systems

  • Djamila OuelhadjEmail author
  • Sanja Petrovic
Article

Abstract

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling.

The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

Keywords

Dynamic scheduling Robust scheduling Predictive–reactive scheduling Agent-based scheduling 

References

  1. Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065–2082. CrossRefGoogle Scholar
  2. Akturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81–97. CrossRefGoogle Scholar
  3. Aydin, M. E., & Öztemel, E. (2000). Job-shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems, 33(2–3), 169–178. CrossRefGoogle Scholar
  4. Aytug, H., Lawley, M. A., McKay, K., Mohan, S., & Uzsoy, R. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161(1), 86–110. CrossRefGoogle Scholar
  5. Bean, J. C., Birge, J. R., Mittenthal, J., & Noon, C. E. (1991). Match up scheduling with multiple resources release dates and disruptions. Journal of Operations Research, 39(3), 471–483. Google Scholar
  6. Belz, R., & Mertens, P. (1996). Combining knowledge-based systems and simulation to solve rescheduling problems. Decision Support Systems, 17(2), 141–157. CrossRefGoogle Scholar
  7. Bierwirth, C., & Mattfeld, D. C. (1999). Production scheduling and rescheduling with genetic algorithms. Evolutionary Computation, 7(1), 1–17. CrossRefGoogle Scholar
  8. Bongaerts, L., Monostori, L., McFarlane, D., & Kadar, B. (2000). Hierarchy in distributed shop floor control. Computers in Industry, 43(2), 123–137. CrossRefGoogle Scholar
  9. Brandimarte, P., & Villa, A. (1999). Modelling manufacturing systems: from aggregate planning to real-time control. Berlin: Springer. Google Scholar
  10. Brennan, R. W., & Norrie, D. H. (2001). Evaluating the performance of reactive control architectures for manufacturing production control. Computers in Industry, 46(3), 235–245. CrossRefGoogle Scholar
  11. Cavalieri, S., Garetti, M., Macchi, M., & Taisch, M. (2000). An experimental benchmarking of two multi-agent architectures for production scheduling and control. Computers in Industry, 43(2), 139–152. CrossRefGoogle Scholar
  12. Chryssolouris, G., & Subramaniam, V. (2001). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing, 12(3), 281–293. CrossRefGoogle Scholar
  13. Church, L. K., & Uzsoy, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153–163. CrossRefGoogle Scholar
  14. Cowling, P. I., & Johansson, M. (2002). Using real-time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244. CrossRefGoogle Scholar
  15. Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2000). Multi-agent systems for dynamic scheduling. In M. Garagnani, (Ed.), Proceedings of the nineteenth workshop of planning and scheduling of the UK (pp. 45–54). The Open University, UK. Google Scholar
  16. Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2001). A multi-agent architecture for dynamic scheduling of steel hot rolling. In Proceedings of the third international ICSC world manufacturing congress (pp. 104–111). Rochester, NY, USA. Google Scholar
  17. Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2003). A multi-agent architecture for dynamic scheduling of steel hot rolling. Journal of Intelligent Manufacturing, 14, 457–470. CrossRefGoogle Scholar
  18. Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2004). Dynamic scheduling of steel casting and milling using multi-agents. Journal of Production Planning and Control, 15, 1–11. CrossRefGoogle Scholar
  19. Daniels, R. L., & Kouvelis, P. (1995). Robust scheduling to hedge against processing time uncertainty in single-stage production. Management Science, 41(2), 363–737. CrossRefGoogle Scholar
  20. Dorn, J. (1995). Case-based reactive scheduling. In R. M. Kerr & E. Szelke (Eds.), Artificial intelligence in reactive scheduling (pp. 32–50). Dordrecht: Kluwer Academic. Google Scholar
  21. Dorn, J., Kerr, R. M., & Thalhammer, G. (1994). Reactive scheduling in a fuzzy temporal framework. In E. Szelke & R. M. Kerr (Eds.), Knowledge-based reactive scheduling (pp. 39–55). Amsterdam: North-Holland. Google Scholar
  22. Dorn, J., Kerr, R. M., & Thalhammer, G. (1995). Reactive scheduling: improving the robustness of schedules and restricting the effects of shop floor disturbances by fuzzy reasoning. International Journal of Human Computer Studies, 42, 687–704. CrossRefGoogle Scholar
  23. Duffie, N. A., & Piper, R. S. (1986). Non-hierarchical control of manufacturing systems. Journal of Manufacturing Systems, 5(2), 137–139. CrossRefGoogle Scholar
  24. Dutta, A. (1990). Reacting to scheduling exceptions in FMS environments. IIE Transactions, 22(4), 33–314. CrossRefGoogle Scholar
  25. Fox, M. S. (1994). ISIS: A retrospective. Intelligent scheduling. In M. Zweben & M. S. Fox (Eds.), Intelligent scheduling (pp. 1–28). San Mateo: Morgan Kaufmann. Google Scholar
  26. Garetti, M., & Taisch, M. (1995). Using neuronal networks for reactive scheduling. In R. M. Kerr & E. Szelke (Eds.), Artificial intelligence in reactive scheduling (pp. 146–147). Dordrecht: Kluwer Academic. Google Scholar
  27. Garner, B. J., & Ridley, G. J. (1994). Application of neuronal network process in reactive scheduling. In E. Szelke & R. M. Kerr (Eds.), Knowledge-based reactive scheduling (pp. 19–28). Amsterdam: North-Holland. Google Scholar
  28. Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic. Google Scholar
  29. Glover, F., Kelly, J. P., & Laguna, M. (1995). Genetic algorithms and tabu search: hybrids for optimisation. Computers of Operation Research, 22(1), 111–134. CrossRefGoogle Scholar
  30. Goldsmith, S. Y., & Interrante, L. D. (1998). An autonomous manufacturing collective for job shop scheduling. In The proceedings of AI & manufacturing research planning workshop (pp. 69–74). Albuquere. Menlo Park: AAAI Press. Google Scholar
  31. Gou, L., Luh, P. B., & Kyoya, Y. (1998). Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation. Computers in Industry, 37(3), 213–231. CrossRefGoogle Scholar
  32. Henning, G. P., & Cerda, J. (2000). Knowledge-based predictive and reactive scheduling in industrial environments. Computers and Chemical Engineering, 24(9), 2315–2338. CrossRefGoogle Scholar
  33. Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306. CrossRefGoogle Scholar
  34. Jahangirian, M., & Conroy, G. V. (2000). Intelligent dynamic scheduling system: the application of genetic algorithms. Integrated Manufacturing Systems, 11(4), 247–257. CrossRefGoogle Scholar
  35. Jain, A. K., & Elmaraghy, H. A. (1997). Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research, 35(1), 81–309. Google Scholar
  36. Jensen, M. T. (2001). Improving robustness and flexibility of tardiness and total flow-time job shops using robustness measures. Applied Soft Computing, 1(1), 35–52. CrossRefGoogle Scholar
  37. Jozefowska, J., Mika, M., Roycki, R., Waligora, G., & Wglarz, J. W. (1998). Local search meta-heuristics for discrete-continuous scheduling problems. European Journal of Operational Research, 107(2), 354–370. CrossRefGoogle Scholar
  38. Kerr, R. M., & Szelke, E. (1995). Artificial intelligence in reactive scheduling. Dordrecht: Kluwer Academic. Google Scholar
  39. Kutanoglu, E., & Sabuncuoglu, I. (2001). Routing-based reactive scheduling policies for machine failures in dynamic job shops. International Journal of Production Research, 39(14), 3141–3158. CrossRefGoogle Scholar
  40. Le Pape, C. (1994). Scheduling as intelligent control of decision-making and constraint propagation. In M. Zweben & M. S. Fox (Eds.), Intelligent scheduling (pp. 67–98). San Mateo: Morgan Kaufmann. Google Scholar
  41. Lee, C. Y., & Uzsoy, R. (1999). Minimizing makespan on a single batch processing machine with dynamic job arrivals. International Journal of Production Research, 37(1), 219–236. CrossRefGoogle Scholar
  42. Leon, V. J., Wu, S. D., & Storer, R. H. (1994). Robustness measures and robust scheduling for job shops. IIE Transactions, 26(5), 32–41. CrossRefGoogle Scholar
  43. Leus, R., & Herroelen, W. (2005). The complexity of machine scheduling for stability with a single disrupted job. Operations Research Letters, 33(2), 151–156. CrossRefGoogle Scholar
  44. Li, H., Li, Z., Li, L. X., & Hu, B. (2000). A production rescheduling expert simulation system. European Journal of Operational Research, 124(2), 283–293. CrossRefGoogle Scholar
  45. Lin, G. Y., & Solberg, J. J. (1992). Integrated shop floor control using autonomous agents. IIE Transactions, 24(3), 57–71. CrossRefGoogle Scholar
  46. Lin, G. Y., & Solberg, J. J. (1994). An agent based flexible routing manufacturing control simulation system. In Proceedings of the 1994 Winter simulation conference (pp. 970–977). Google Scholar
  47. MacCarthy, B. L., & Liu, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59–79. CrossRefGoogle Scholar
  48. Maturana, F., Shen, W., & Norrie, D. H. (1999). MetaMorph: an adaptive agent-based architecture for intelligent manufacturing. International Journal of Production Research, 37(10), 2159–2173. CrossRefGoogle Scholar
  49. Mehta, S. V., & Uzsoy, R. (1999). Predictable scheduling of a single machine subject to breakdowns. International Journal of Computer Integrated Manufacturing, 12(1), 15–38. CrossRefGoogle Scholar
  50. Meziane, F., Vadera, S., Kobbacy, K., & Proudlove, N. (2000). Intelligent systems in manufacturing: current developments and future prospects. Integrated Manufacturing Systems, 11(4), 218–238. CrossRefGoogle Scholar
  51. Miyashita, K., & Sycara, K. (1995). CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair. Artificial Intelligence, 76(1), 377–426. CrossRefGoogle Scholar
  52. Muhlemann, A. P., Lockett, G., & Farn, C. K. (1982). Job shop scheduling heuristics and frequency of scheduling. International Journal of Production Research, 20(2), 227–241. CrossRefGoogle Scholar
  53. Nof, S. Y., & Grant, F. H. (1991). Adaptive/predictive scheduling: review and a general framework. Production Planning & Control, 2(4), 298–312. CrossRefGoogle Scholar
  54. O’Donovan, R., Uzsoy, R., & McKay, K. N. (1999). Predictable scheduling of a single machine with breakdowns and sensitive jobs. International Journal of Production Research, 37(18), 4217–4233. CrossRefGoogle Scholar
  55. O’Hare, G., & Jennings, N. (1996). Foundations of distributed artificial intelligence. New York: Wiley. Google Scholar
  56. O’Kane, J. F. (2000). A knowledge-based system for reactive scheduling decision-making in FMS. Journal of Intelligent Manufacturing, 11(5), 461–474. CrossRefGoogle Scholar
  57. Ouelhadj, D., Hanachi, C., & Bouzouia, B. (1998). Multi-agent system for dynamic scheduling and control in manufacturing cells. In Proceedings of the IEEE international conference on robotics and automation (pp. 1256–1262). Belgium. Google Scholar
  58. Ouelhadj, D., Hanachi, C., Bouzouia, B., Farhi, A., & Moualek, A. (1999). A multi-contract net protocol for dynamic scheduling in flexible manufacturing systems. In Proceedings of the IEEE international conference on robotics and automation (pp. 1114–1120). USA. Google Scholar
  59. Ouelhadj, D., Hanachi, C., & Bouzouia, B. (2000). Multi-agent architecture for distributed monitoring in flexible manufacturing systems (FMS). In Proceedings of the IEEE international conference on robotics and automation (pp. 1120–1126). San Francisco, USA. Google Scholar
  60. Ouelhadj, D., Cowling, P. I., & Petrovic, S. (2003a). Contract net protocol for cooperative optimisation and dynamic scheduling of steel production. In A. Ibraham, K. Franke, & M. Koppen (Eds.), Intelligent systems design and applications (pp. 457–470). Berlin: Springer. Google Scholar
  61. Ouelhadj, D., Cowling, P. I., & Petrovic, S. (2003b). Utility and stability measures for agent-based dynamic scheduling of steel continuous casting. In Proceedings of the IEEE international conference on robotics and automation (pp. 175–180). Taipei, Taiwan. Selected in the finalist best student award. Google Scholar
  62. Ovacik, I. M., & Uzsoy, R. (1994). Rolling horizon algorithms for a single-machine dynamic scheduling problem with sequence-dependent set-up times. International Journal of Production Research, 32(6), 1243–1263. CrossRefGoogle Scholar
  63. Park, J., Kang, M., & Lee, K. (1996). Intelligent operations scheduling system in a job shop. International Journal of Advanced Manufacturing Technology, 11, 111–119. CrossRefGoogle Scholar
  64. Parunak, H. V. (1987). Manufacturing experience with the contract net. In M. Huhns (Ed.), Distributed artificial intelligence (pp. 285–310). London: Pitman. Google Scholar
  65. Parunak, H. V. (1996). Applications of distributed artificial Intelligence in industry. In G. M. P. O’Hare & N. R. Jennings (Eds.), Foundation of distributed artificial intelligence. New York: Wiley-Interscience. Chap. 4. Google Scholar
  66. Parunak, H. V. (2000). Agents in overalls: experiences and issues in the development and deployment of industrial agent-based systems. International Journal of Cooperative Information Systems, 9(3), 209–227. CrossRefGoogle Scholar
  67. Parunak, H. V., Baker, A. D., & Clark, S. J. (1997). The AARIA agent architecture: an example of requirements-driven agent based system design. In Proceedings of the 1st international conference on autonomous agents (pp. 482–483). California, USA. Google Scholar
  68. Pendharkar, P. C. (1999). A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments. Expert Systems with Applications, 16(2), 121–133. CrossRefGoogle Scholar
  69. Petrovic, D., & Duenas, A. (2006). A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets and Systems, 157(16), 2273–2285. CrossRefGoogle Scholar
  70. Pham, D. T., & Karaboga, D. (2000). Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks. London: Springer. Google Scholar
  71. Rajendran, C., & Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flow shops and job shops. European Journal of Operational Research, 116(1), 156–170. CrossRefGoogle Scholar
  72. Ramasesh, R. (1990). Dynamic job shop scheduling: a survey of simulation research. OMEGA International Journal of Management Science, 18(1), 43–57. CrossRefGoogle Scholar
  73. Ramos, C. (1994). An architecture and a negotiation protocol for the dynamic scheduling of manufacturing systems. In Proceedings of IEEE international conference on robotics and automation (pp. 8–13). Google Scholar
  74. Rossi, A., & Dini, G. (2000). Dynamic scheduling of FMS using a real-time genetic algorithm. International Journal of Production Research, 38(1), 1–20. CrossRefGoogle Scholar
  75. Ruiz, D., Canton, J., Mara, N. J., Espuna, A., & Puigjaner, L. (2001). On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants. Computers and Chemical Engineering, 25(4), 829–837. CrossRefGoogle Scholar
  76. Sabuncuoglu, I. (1998). A study of scheduling rules of flexible manufacturing systems: a simulation approach. International Journal of Operational Research, 36(2), 527–546. Google Scholar
  77. Sabuncuoglu, I., & Bayiz, M. (2000). Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research, 126(3), 567–586. CrossRefGoogle Scholar
  78. Sabuncuoglu, I., & Karabuk, S. (1999). Rescheduling frequency in an FMS with uncertain processing times and unreliable machines. Journal of Manufacturing Systems, 18(4), 268–283. CrossRefGoogle Scholar
  79. Sandholm, T. W. (2000). Automated contracting in distributed manufacturing among independent companies. Journal of Intelligent Manufacturing, 11(3), 271–283. CrossRefGoogle Scholar
  80. Sarin, S. C., & Salgame, R. R. (1990). Development of a knowledge-based system for dynamic scheduling. International Journal of Production Research, 28(8), 1499–1513. CrossRefGoogle Scholar
  81. Schmidt, G. (1994). How to apply fuzzy logic to reactive scheduling. In E. Szelke & R. M. Kerr (Eds.), Knowledge-based reactive scheduling (pp. 57–67). Amsterdam: North-Holland. Google Scholar
  82. Shafaei, R., & Brunn, P. (1999). Workshop scheduling using practical (inaccurate) data, Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach. International Journal of Production Research, 37(17), 3913–3925. CrossRefGoogle Scholar
  83. Shaw, J. M. (1988). Dynamic scheduling in cellular manufacturing systems: a framework for Network decision making. Journal of Manufacturing Systems, 7(2), 83–94. CrossRefGoogle Scholar
  84. Shen, W., & Norrie, D. H. (1999). Agent based systems for intelligent manufacturing: a state of the art survey. International Journal of Knowledge and Information Systems, 1(2), 129–156. Google Scholar
  85. Shen, W., Maturana, F., & Norrie, D. H. (2000). MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing. Journal of Intelligent Manufacturing, 11(3), 237–251. CrossRefGoogle Scholar
  86. Shen, W., Norrie, D. H., & Barthes, J. P. A. (2001). Multi-agent systems for concurrent intelligent design and manufacturing. London: Taylor & Francis. Google Scholar
  87. Shukla, C. S., & Chen, F. F. (1996). The state of the art in intelligent real-time FMS control: a comprehensive survey. Journal of Intelligent Manufacturing, 7, 441–455. CrossRefGoogle Scholar
  88. Smith, R. (1980). The contract net protocol: high level communication and control in distributed problem solver. IEEE Transactions on Computers, 29(12), 1104–1113. CrossRefGoogle Scholar
  89. Smith, F. S. (1994). OPIS: A methodology and architecture for reactive scheduling. In M. Zweben & M. S. Fox (Eds.), Intelligent scheduling (pp. 29–66). San Mateo: Morgan Kaufmann. Google Scholar
  90. Smith, F. S. (1995). Reactive scheduling systems. In D. Brown & W. T. Scherer (Eds.), Intelligent scheduling systems (pp. 155–192). Dordrecht: Kluwer Academic. Google Scholar
  91. Sousa, P., & Ramos, C. (1999). A distributed architecture and negotiation protocol for scheduling in manufacturing systems. Computers in Industry, 38(2), 103–113. CrossRefGoogle Scholar
  92. Stoop, P. P. M., & Weirs, V. C. S. (1996). The complexity of scheduling in practice. International Journal of Operations and Production management, 16(10), 37–53. CrossRefGoogle Scholar
  93. Sun, J., & Xue, D. (2001). A dynamic reactive scheduling mechanism for responding to changes of production orders and manufacturing resources. Computers in Industry, 46(2), 189–207. CrossRefGoogle Scholar
  94. Suresh, V., & Chaudhuri, D. (1993). Dynamic scheduling a survey of research. International Journal of Production Economics, 32(1), 53–63. CrossRefGoogle Scholar
  95. Szelke, E., & Kerr, R. M. (1994). Knowledge-based reactive scheduling. Amsterdam: North-Holland. Google Scholar
  96. Tharumarajah, A., & Bemelman, R. (1997). Approaches and issues in scheduling a distributed shop-floor environment. Computers in Industry, 34(1), 95–109. CrossRefGoogle Scholar
  97. Tharumarajah, A. (2001). Survey of resource allocation methods for distributed manufacturing systems. Production Planning & Control, 12(1), 58–68. CrossRefGoogle Scholar
  98. Vieira, G. E., Herrmann, J. W., & Lin, E. (2000a). Analytical models to predict the performance of a single machine system under periodic and event-driven rescheduling strategies. International Journal of Production Research, 38(8), 1899–1915. CrossRefGoogle Scholar
  99. Vieira, G. E., Hermann, J. W., & Lin, E. (2000b). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256–266. CrossRefGoogle Scholar
  100. Vieira, G. E., Hermann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: a framework of strategies, policies and methods. Journal of Scheduling, 6(1), 36–92. CrossRefGoogle Scholar
  101. Wu, S. D., Storer, R. H., & Chang, P. C. (1991). A rescheduling procedure for manufacturing systems under random disruptions. In Proceedings joint USA/German conference on new directions for operations research in manufacturing (pp. 292–306). Google Scholar
  102. Wu, S. D., Storer, R. H., & Chang, P. C. (1993). One machine rescheduling heuristics with efficiency and stability as criteria. Computers Operations Research, 20(1), 1–14. CrossRefGoogle Scholar
  103. Yamamoto, M., & Nof, S. Y. (1985). Scheduling/rescheduling in the manufacturing operating system environment. International Journal of Production Research, 23(4), 705–722. CrossRefGoogle Scholar
  104. Youssef, H., Sait, S. M., & Adiche, H. (2001). Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Engineering Applications of Artificial Intelligence, 14(2), 167–181. CrossRefGoogle Scholar
  105. Zhou, H., Feng, Y., & Han, L. (2001). The hybrid heuristic genetic algorithm for job shop scheduling. Computers and Industrial Engineering, 40(3), 191–200. CrossRefGoogle Scholar
  106. Zweben, M., & Fox, M. S. (1994). Intelligent scheduling. San Mateo: Morgan Kaufmann. Google Scholar
  107. Zweben, M., Daun, B., & Deale, M. (1994). Scheduling and rescheduling with iterative repair. In M. Zweben & M. S. Fox (Eds.), Intelligent scheduling (pp. 241–254). San Mateo: Morgan Kaufmann. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Automated Scheduling, Optimisation and Planning Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations