Skip to main content
SpringerLink
Log in

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of \(\hbox {Prometheus}^{\mathrm{TM}}\) methodology and programmed in \(\hbox {JACK}^{\mathrm{TM}}\) agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Baykasoğlu, A., Özbakır, L., & Sönmez, A. I. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 15(6), 777–785.

    Article  Google Scholar 

  • Baykasoğlu, A., & Özbakır, L. (2008). Analysing the effect of flexibility on manufacturing systems performance. Journal of Manufacturing Technology Management, 19(2), 172–193.

    Article  Google Scholar 

  • Baykasoğlu, A. (2009). Quantifying machine flexibility. International Journal of Production Research, 47(15), 4109–4123.

    Article  Google Scholar 

  • Baykasoğlu, A., & Özbakır, L. (2010). Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system. International Journal of Production Economics, 124(2), 369–381.

    Article  Google Scholar 

  • Baykasoğlu, A., & Durmuşoğlu, Z. D. U. (2012). Flow time analyses of a simulated flexible job shop by considering jockeying. The International Journal of Advanced Manufacturing Technology, 58(5), 693–707.

    Article  Google Scholar 

  • Bilge, Ü., & Ulusoy, G. (1995). A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research, 43(6), 1058–1070.

    Article  Google Scholar 

  • Browne, J., Dubois, D., Rathmill, K., & Sethi, S. P. (1984). Classification of flexible manufacturing systems. The FMS Magazine, 2(2), 114–117.

    Google Scholar 

  • Chang, A. Y., Whitehouse, D. J., & Chang, S. L. (2001). An approach to the measurement of single-machine flexibility. International Journal of Production Research, 39(8), 1589–1601.

    Article  Google Scholar 

  • Chao, D. Y., & Pan, Y. L. (2015). Uniform formulas for compound siphons, complementary siphons and characteristic vectors in deadlock prevention of flexible manufacturing systems. Journal of Intelligent Manufacturing, 26(1), 13–23.

    Article  Google Scholar 

  • Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418–429.

    Article  Google Scholar 

  • Egbelu, P. J., & Tanchoco, J. M. A. (1984). Characterization of automatic guided vehicle dispatching rules. The International Journal of Production Research, 22(3), 359–374.

    Article  Google Scholar 

  • Erol, R., Şahin, C., Baykasoğlu, A., & Kaplanoğlu, V. (2012). A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Applied Soft Computing, 12(6), 1720–1732.

    Article  Google Scholar 

  • Farahvash, P., & Boucher, T. O. (2004). A multi-agent architecture for control of AGV systems. Robotics and Computer-Integrated Manufacturing, 20(6), 473–483.

    Article  Google Scholar 

  • Gao, J., Gen, M., Sun, L., & Zhao, X. (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53(1), 149–162.

    Article  Google Scholar 

  • Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866.

    Article  Google Scholar 

  • Komma, V. R., Jain, P. K., & Mehta, N. (2011). An approach for agent modeling in manufacturing on \(\text{ JADE }^{{\rm TM}}\) reactive architecture. The International Journal of Advanced Manufacturing Technology, 52(9), 1079–1090.

    Article  Google Scholar 

  • Kumar, M. V. S., Janardhana, R., & Rao, J. S. P. (2011). Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing. The International Journal of Advanced Manufacturing Technology, 53(1–4), 339–351.

    Article  Google Scholar 

  • Lun, M., & Chen, F. (2000). Holonic concept based methodology for part routeing on flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology, 16(7), 483–490.

  • Mohamed, Z. M., Youssef, M. A., & Haq, F. (2001). The impact of machine flexibility on the performance of flexible manufacturing systems. International Journal of Operations & Production Management, 21(5/6), 707–742.

    Article  Google Scholar 

  • Padgham, L., & Winikoff, M. (2004). Developing intelligent agent systems a practical guide. Melbourne: Wiley.

    Book  Google Scholar 

  • Padgham, L., Thangarajah, J., Paul, P. (2009). Prometheus Design Tool Version 2.5 User Manual. Melbourne: RMIT University, Computer Science Department, Agent Research Group.

  • Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. Springer Science & Business Media.

  • Sabuncuoğlu, I., & Hommertzheim, D. (1989). An investigation of machine and AGV scheduling rules in an FMS. In Proceedings of the third ORSA/TIMS conference on flexible manufacturing Systems: Operations research models and applications.

  • Sabuncuoğlu, I., & Hommertzheim, D. (1992). Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system. The International Journal of Production Research, 30(5), 1059–1079.

    Article  Google Scholar 

  • Sabuncuoğlu, I., & Hommertzheim, D. (1992). Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion. The International Journal of Production Research, 30(7), 1617–1635.

  • Sabuncuoğlu, I., & Hommertzheim, D. (1993). Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules. International Journal of Flexible Manufacturing Systems, 5(4), 301–323.

  • Sethi, A. K., & Sethi, S. P. (1990). Flexibility in manufacturing: A survey. International Journal of Flexible Manufacturing Systems, 2(4), 289–328.

    Article  Google Scholar 

  • Srivastava, S. C., Choudhary, A. K., Kumar, S., & Tiwari, M. K. (2008). Development of an intelligent agent-based AGV controller for a flexible manufacturing system. The International Journal of Advanced Manufacturing Technology, 36(7), 780–797.

    Article  Google Scholar 

  • Subramaniam, V., Lee, G. K., Ramesh, T., & Hong, G. S. (2000). Machine selection rules in a dynamic job shop. The International Journal of Advanced Manufacturing Technology, 16(12), 902–908.

    Article  Google Scholar 

  • Şahin, C. (2010). Multi-agent approach for the scheduling of manufacturing systems. Industrial Engineering, Adana, Çukurova Üniversitesi. Ph.D.: 116.

  • Vinod, V., & Sridharan, R. (2011). Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system. International Journal of Production Economics, 129(1), 127–146.

    Article  Google Scholar 

  • Wahab, M. I. M., Wu, D., & Lee, C. G. (2008). A generic approach to measuring the machine flexibility of manufacturing systems. European Journal of Operational Research, 186(1), 137–149.

  • Wallace, A. (2007). Multi-agent negotiation strategies utilizing heuristics for the flow of AGVs. International Journal of Production Research, 45(2), 309–322.

    Article  Google Scholar 

  • Wang, L., Zhou, G., Xu, Y., & Liu, M. (2012). An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. The International Journal of Advanced Manufacturing Technology, 60(9–12), 1111–1123.

    Article  Google Scholar 

  • Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409–425.

    Article  Google Scholar 

Download references

Acknowledgments

The present study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (Grant No. 111M279).

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Cukurova University, Adana, Turkey

    Cenk Sahin, Melek Demirtas & Rizvan Erol

  2. Department of Industrial Engineering, Dokuz Eylul University, Izmir, Turkey

    Adil Baykasoğlu

  3. Department of Industrial Engineering, Gaziantep University, Gaziantep, Turkey

    Vahit Kaplanoğlu

Authors
  1. Cenk Sahin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melek Demirtas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rizvan Erol
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adil Baykasoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vahit Kaplanoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adil Baykasoğlu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahin, C., Demirtas, M., Erol, R. et al. A multi-agent based approach to dynamic scheduling with flexible processing capabilities. J Intell Manuf 28, 1827–1845 (2017). https://doi.org/10.1007/s10845-015-1069-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1069-x

Keywords

Search

Navigation