An Integrated Model for Analysis and Improvement of Scheduling “Flexible Manufacturing Systems (FMS)” and Dispatching “Automated Guided Vehicle (AGV)” Problems

Document Type : Original Article


1 M.A. Kar Higher Education Institute, Qazvin.

2 Assistant Professor, Shahid Beheshti University.

3 Professor, Amirkabir University of Technology.


Flexible manufacturing system scheduling is one of the most important and practical topics in manufacturing systems scheduling problems which could be affected by many features and subproblems. Considering them in FMS scheduling model in an integrated way leads to a feasible scheduling, and  the model will  not only be closer to the real settings in FMS environment but also its application in manufacturing systems will increase. This contribution takes into account manufacturing tasks and AGV dispatching scheduling problems simultaneously (in addition involving 2 subproblemsi machine loading, ii part routing problems implicitly). It provided a mathematical nonlinear mixed integer programming model. Having solved the model via Genetic Algorithm leaded to suboptimal solutions. Solving various examples, defining Lower and Upper Bounds and comparing them, demonstrate the quality of the solutions.


1. Atan, T. S., Pandit, R., (1996). Auxiliary tool allocation in flexible manufacturing systems. European Journal of Operational Research, 89(3), 642-659.
2.  Atmani, A., & Lashkari, R. S. A. (1998). Model of machine tool selection and operation allocation in FMS. International Journal of  Production Research, 36, 1339-1349.
3.  Bilge, Ü., & Ulusoy , G. (1995). A time window approach to simultaneous scheduling of machines and material handeling system in an FMS. Operations Research, 43, 1058-1070.
4. Blazewicz, J., Eiselt, H. A., Finke, G., Laporte, G., & Weglarz,J. (1991). Scheduling tasks and vehicles in flexible manufacturing systemInternational Journal of  Flexible Manufacturing Systems, 4, 5-16.
5. Caummond, A., Lacomme, P., Moukrim, A., & Tchernev, N. (2009). An MILP for scheduling problems in an FMS with one vehicle. European Journal of Operational Research, 199, 706-722.
6. Chan, F. T. S., & Swarnkar, R. (2006). Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and Computer-Intergrated Manufacturing, 22, 353-362.
7. Gamila, M. A., & Motavalli, S. A. (2003). Modeling technique for loading and scheduling problems in FMS. Robotics and Computer-Intergrated Manufacturing, 19, 45-54.
8. Ganesharjah, T., Hall, N. G., & Sriskandrajah, C. (1998). Design and operational issues in AGV-served manufacturing systems. Annals of operations research, 76, 109-154.
9. Gen. M, Cheng. R, Lin. L. (2008). Advanced Network Models: AGV Dispatching Model.  Network Models and Optimization: Multiobjective Genetic Algorithm Approach-(Decision Engineering). 651-666. London. Springer.
10. Grieco, A.,Semeraro, Q., & Tolio, T. (2001). A review of different approaches to the FMS Loading Problem. International Journal of  Flexible Manufacturing Systems, 13, 361- 384.
11. Jerald,J.,Asokan, P., Saravanan, R., & Rani, A.D. C. (2006). Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. The International Journal of  Advanced Manufacturing Technology, 29, 584-589.
12. Kelen C. T. Vivaldini, Luís F. Rocha, Marcelo Beker, António Paulo Moreira. (2015). Comprehensive Review of the Dispatching, Scheduling and Routing of AGVs. CONTROLO’2014 – Proceedings of the 11th Portuguese Conference on Automatic Control Lecture Notes in Electrical Engineering, 321, 505-514.
13. Liu, J., & MacCarthy, B. L. (1997). A global MILP model for FMS scheduling. European Journal of Operational Research, 100, 441-453.
14. Low. CH, Wu. T. (2001). Mathematical modeling and heuristic approaches to operation scheduling problems in an FMS environment. International Journal of Production Research, 39, 689-708.
15. Low. CH, Yip. Y, Wu. T. (2006). Modelling and heuristics of FMS scheduling with multiple objectives. Computers & Operations Research, 33, 674-694.
16. Mϋller, T., 1983. Automated Guided Vehicles. IFS (Publications) Ltd./Springer-Verlag, UK/Berlin.
17. Novas, J.M., & Henning, G. P. (2014). Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming. Expert Systems with Applications, 41, 2286-2299.
18. Rahimi, H., Azar, A., Rezaee Pandari, A. (2015). Multi_objective Mathematical model for jobshop production system and soling by using simulated annealing algorithm. Journal of Industrial Management Perspective, 5, 19.(In Persian)
19. Roh, H.K., Kim, Y.D., (1997). Due date based loading and scheduling methods for a flexible manufacturing system with an automatic tool transporter. International Journal of Production Research 35, 2989–3003.
20. Sarin, S. C., Chen, C.S. (1987). The machine loading and tool allocation problem in flexible manufacturing systems. International Journal of Production Research, 25(7), 1081-1094.
21. UdhayaKumar, P. & Kumanan, S. (2010).Task Scheduling of AGV in FMS Using Non-Traditional Optimization Techniques. International Journal Simulation Models, 1, 28-39.
22. Ulusoy, G., Sivrikaya-Serfioglu, F., & Bilge, Ü. (1997). A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Computers & Operations Research, 24, 335-351.
23. Vis, I. F. A. (2006). Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research, 170, 677-709.
24. Yang. J. (2001). GA-Based Discrete Dynamic Programming Approach for Scheduling in FMS Environments. Ieee transactions on systems, man, and cybernetics-part b: cybernetics. 3, 824-835.
25. Zandieh, M., Fotovvat, A. (2016). Jobshop Flow System Scheduling with Machine Access Constraint and Learning Effects based on a Hybrid Model. Journal of Industrial Management Perspective, 20, 41-58. (In Persian)
26. Zeballos, L. J. (2010). A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems. Robotics and Computer-Intergrated Manufacturing, 26, 725-743.
27. Zeballos, L. J., Quiroga, O. D., & Henning, G. P. (2010). A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations. Engineering Applications of Artificial Inteligence, 23, 229-248.