Skip to main content
SpringerLink
Log in

A survey of dynamic scheduling in manufacturing systems

  • Published:
Journal of Scheduling Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065–2082.

    Article  Google Scholar 

  • Akturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81–97.

    Article  Google Scholar 

  • Aydin, M. E., & Öztemel, E. (2000). Job-shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems, 33(2–3), 169–178.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Belz, R., & Mertens, P. (1996). Combining knowledge-based systems and simulation to solve rescheduling problems. Decision Support Systems, 17(2), 141–157.

    Article  Google Scholar 

  • Bierwirth, C., & Mattfeld, D. C. (1999). Production scheduling and rescheduling with genetic algorithms. Evolutionary Computation, 7(1), 1–17.

    Article  Google Scholar 

  • Bongaerts, L., Monostori, L., McFarlane, D., & Kadar, B. (2000). Hierarchy in distributed shop floor control. Computers in Industry, 43(2), 123–137.

    Article  Google Scholar 

  • Brandimarte, P., & Villa, A. (1999). Modelling manufacturing systems: from aggregate planning to real-time control. Berlin: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chryssolouris, G., & Subramaniam, V. (2001). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing, 12(3), 281–293.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cowling, P. I., & Johansson, M. (2002). Using real-time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Daniels, R. L., & Kouvelis, P. (1995). Robust scheduling to hedge against processing time uncertainty in single-stage production. Management Science, 41(2), 363–737.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Duffie, N. A., & Piper, R. S. (1986). Non-hierarchical control of manufacturing systems. Journal of Manufacturing Systems, 5(2), 137–139.

    Article  Google Scholar 

  • Dutta, A. (1990). Reacting to scheduling exceptions in FMS environments. IIE Transactions, 22(4), 33–314.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic.

    Google Scholar 

  • Glover, F., Kelly, J. P., & Laguna, M. (1995). Genetic algorithms and tabu search: hybrids for optimisation. Computers of Operation Research, 22(1), 111–134.

    Article  Google Scholar 

  • 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 

  • Gou, L., Luh, P. B., & Kyoya, Y. (1998). Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation. Computers in Industry, 37(3), 213–231.

    Article  Google Scholar 

  • Henning, G. P., & Cerda, J. (2000). Knowledge-based predictive and reactive scheduling in industrial environments. Computers and Chemical Engineering, 24(9), 2315–2338.

    Article  Google Scholar 

  • Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306.

    Article  Google Scholar 

  • Jahangirian, M., & Conroy, G. V. (2000). Intelligent dynamic scheduling system: the application of genetic algorithms. Integrated Manufacturing Systems, 11(4), 247–257.

    Article  Google Scholar 

  • Jain, A. K., & Elmaraghy, H. A. (1997). Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research, 35(1), 81–309.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kerr, R. M., & Szelke, E. (1995). Artificial intelligence in reactive scheduling. Dordrecht: Kluwer Academic.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Leon, V. J., Wu, S. D., & Storer, R. H. (1994). Robustness measures and robust scheduling for job shops. IIE Transactions, 26(5), 32–41.

    Article  Google Scholar 

  • Leus, R., & Herroelen, W. (2005). The complexity of machine scheduling for stability with a single disrupted job. Operations Research Letters, 33(2), 151–156.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lin, G. Y., & Solberg, J. J. (1992). Integrated shop floor control using autonomous agents. IIE Transactions, 24(3), 57–71.

    Article  Google Scholar 

  • 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).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nof, S. Y., & Grant, F. H. (1991). Adaptive/predictive scheduling: review and a general framework. Production Planning & Control, 2(4), 298–312.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • O’Hare, G., & Jennings, N. (1996). Foundations of distributed artificial intelligence. New York: Wiley.

    Google Scholar 

  • O’Kane, J. F. (2000). A knowledge-based system for reactive scheduling decision-making in FMS. Journal of Intelligent Manufacturing, 11(5), 461–474.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • Park, J., Kang, M., & Lee, K. (1996). Intelligent operations scheduling system in a job shop. International Journal of Advanced Manufacturing Technology, 11, 111–119.

    Article  Google Scholar 

  • Parunak, H. V. (1987). Manufacturing experience with the contract net. In M. Huhns (Ed.), Distributed artificial intelligence (pp. 285–310). London: Pitman.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pham, D. T., & Karaboga, D. (2000). Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks. London: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ramasesh, R. (1990). Dynamic job shop scheduling: a survey of simulation research. OMEGA International Journal of Management Science, 18(1), 43–57.

    Article  Google Scholar 

  • 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).

  • Rossi, A., & Dini, G. (2000). Dynamic scheduling of FMS using a real-time genetic algorithm. International Journal of Production Research, 38(1), 1–20.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Sabuncuoglu, I., & Bayiz, M. (2000). Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research, 126(3), 567–586.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sandholm, T. W. (2000). Automated contracting in distributed manufacturing among independent companies. Journal of Intelligent Manufacturing, 11(3), 271–283.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Shaw, J. M. (1988). Dynamic scheduling in cellular manufacturing systems: a framework for Network decision making. Journal of Manufacturing Systems, 7(2), 83–94.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Shen, W., Norrie, D. H., & Barthes, J. P. A. (2001). Multi-agent systems for concurrent intelligent design and manufacturing. London: Taylor & Francis.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Smith, R. (1980). The contract net protocol: high level communication and control in distributed problem solver. IEEE Transactions on Computers, 29(12), 1104–1113.

    Article  Google Scholar 

  • 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 

  • 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 

  • Sousa, P., & Ramos, C. (1999). A distributed architecture and negotiation protocol for scheduling in manufacturing systems. Computers in Industry, 38(2), 103–113.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Suresh, V., & Chaudhuri, D. (1993). Dynamic scheduling a survey of research. International Journal of Production Economics, 32(1), 53–63.

    Article  Google Scholar 

  • Szelke, E., & Kerr, R. M. (1994). Knowledge-based reactive scheduling. Amsterdam: North-Holland.

    Google Scholar 

  • Tharumarajah, A., & Bemelman, R. (1997). Approaches and issues in scheduling a distributed shop-floor environment. Computers in Industry, 34(1), 95–109.

    Article  Google Scholar 

  • Tharumarajah, A. (2001). Survey of resource allocation methods for distributed manufacturing systems. Production Planning & Control, 12(1), 58–68.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • 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.

    Article  Google Scholar 

  • Yamamoto, M., & Nof, S. Y. (1985). Scheduling/rescheduling in the manufacturing operating system environment. International Journal of Production Research, 23(4), 705–722.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhou, H., Feng, Y., & Han, L. (2001). The hybrid heuristic genetic algorithm for job shop scheduling. Computers and Industrial Engineering, 40(3), 191–200.

    Article  Google Scholar 

  • Zweben, M., & Fox, M. S. (1994). Intelligent scheduling. San Mateo: Morgan Kaufmann.

    Google Scholar 

  • 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 

Download references

Author information

Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK

    Djamila Ouelhadj & Sanja Petrovic

Authors
  1. Djamila Ouelhadj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanja Petrovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Djamila Ouelhadj.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ouelhadj, D., Petrovic, S. A survey of dynamic scheduling in manufacturing systems. J Sched 12, 417–431 (2009). https://doi.org/10.1007/s10951-008-0090-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10951-008-0090-8

Keywords

Search

Navigation