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.


















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.
Baykasoğlu, A., & Özbakır, L. (2008). Analysing the effect of flexibility on manufacturing systems performance. Journal of Manufacturing Technology Management, 19(2), 172–193.
Baykasoğlu, A. (2009). Quantifying machine flexibility. International Journal of Production Research, 47(15), 4109–4123.
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.
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.
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.
Browne, J., Dubois, D., Rathmill, K., & Sethi, S. P. (1984). Classification of flexible manufacturing systems. The FMS Magazine, 2(2), 114–117.
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.
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.
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.
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.
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.
Farahvash, P., & Boucher, T. O. (2004). A multi-agent architecture for control of AGV systems. Robotics and Computer-Integrated Manufacturing, 20(6), 473–483.
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.
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.
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.
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.
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.
Padgham, L., & Winikoff, M. (2004). Developing intelligent agent systems a practical guide. Melbourne: Wiley.
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.
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.
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.
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.
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.
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.
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.
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.
Acknowledgments
The present study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (Grant No. 111M279).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-015-1069-x