Abstract
In modern manufacturing systems with computational complexities, decision-making with respect to dynamic rescheduling and reconfiguration in case of internal disturbances is an important issue. This paper introduces a multi-agent-based dynamic scheduling system for manufacturing flow lines (MFLs) using the Prometheus methodology (PM) considering the dynamic customer demands and internal disturbances. The PM is used for designing a decision-making system with the feature of simultaneous dynamic rescheduling. The developed system is implemented on a real MFL of a small- and medium-sized enterprise (unplasticized polyvinyl chloride (uPVC) door and window) where the dynamic customer demands and internal machine break downs are considered. The application has been completely modeled using a Prometheus design tool, which offers full support to the PM, and implemented in JACK agent-based systems. Each agent is autonomous and has an ability to cooperate and negotiate with other agents. The proposed decision-making system supports both static and dynamic scheduling. A simulation platform for testing the proposed multi-agent system (MAS) is developed, and two real scenarios are defined for evaluating the proposed system. The analysis takes into account the comparisons of the overall performances of the system models using the MAS scheduling and conventional scheduling approaches. The result of simulation indicates that the proposed MAS could increase the uptime productivity and the production rate of flexible flow-line manufacturing systems.
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References
Young RE, Vesterager J (1990) An approach to implementing CIM in small and medium size companies. International Journal of NIST Special Publication 785:63–79
Levy M, Powell P (2000) Information systems strategy for small and medium sized enterprises: an organisational perspective. J Strateg Inf Syst 9:63–84
Bai D, Zhang Z-H, Zhang Q (2016) Flexible open shop scheduling problem to minimize makespan. Comput Oper Res 67:207–215
Vatankhah Barenji R, Hashemipour M, Guerra-Zubiaga DA (2015) A framework for modelling enterprise competencies: from theory to practice in enterprise architecture. Int J Comput Integr Manuf 28:791–810
Monostori L, Váncza J, Kumara SR (2006) Agent-based systems for manufacturing. CIRP Ann Manuf Techn 55:697–720
M. Paolucci and R. Sacile (2016) Agent-based manufacturing and control systems: new agile manufacturing solutions for achieving peak performance: CRC Press
Lu SH, Kumar P (1991) Distributed scheduling based on due dates and buffer priorities. IEEE Trans Autom Control 36:1406–1416
A. V. Barenji, R. V. Barenji, and M. Hashemipour (2013) Structural modeling of a RFID-enabled reconfigurable architecture for a flexible manufacturing system, in Smart Objects, Systems and Technologies (SmartSysTech), Proceedings of 2013 European Conference on, pp. 1–10
Barenji AV, Barenji RV, Hashemipour M (2014) A frameworks for structural modelling of an RFID-enabled intelligent distributed manufacturing control system. S Afr J Ind Eng 25:48–66
Shen W, Hao Q, Yoon HJ, Norrie DH (2006) Applications of agent-based systems in intelligent manufacturing: an updated review. Adv Eng Inform 20:415–431
Zhong RY, Huang GQ, Lan S, Dai Q, Zhang T, Xu C (2015) A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing. Adv Eng Inform 29:799–812
Kerzner HR (2013) Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons, Hoboken
Yoon HJ, Shen W (2008) A multiagent-based decision-making system for semiconductor wafer fabrication with hard temporal constraints. IEEE Trans Semicond Manuf 21:83–91
M. Pinedo (2015) Scheduling: Springer. doi: 10.1007/978-3-319-26580-3
Ramamritham K, Stankovic JA (1994) Scheduling algorithms and operating systems support for real-time systems. Proc IEEE 82:55–67
H. J. Yoon and W. Shen 2005 Agent-based scheduling mechanism for semiconductor manufacturing systems with temporal constraints, in IEEE International Conference Mechatronics and Automation, pp. 1123–1128
Caridi M, Cavalieri S (2004) Multi-agent systems in production planning and control: an overview. Prod Plan Control 15:106–118
Gibson MR, Ohlmann JW, Fry MJ (2010) An agent-based stochastic ruler approach for a stochastic knapsack problem with sequential competition. Comput Oper Res 37:598–609
R. Smith, The contract net protocol: highlevel communication and control in a distributed problem solver, 1980, IEEE Trans. on Computers, C, 29, 12
Valckenaers P, Van Brussel H (2005) Holonic manufacturing execution systems. CIRP Ann Manuf Techn 54:427–432
Kaplanoğlu V (2014) Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance. Appl Soft Comput 23:165–179
Chen K-Y, Chen C-J (2010) Applying multi-agent technique in multi-section flexible manufacturing system. Expert Syst Appl 37:7310–7318
L. Padgham and M. Winikoff (2002) Prometheus: a methodology for developing intelligent agents, in International Workshop on Agent-Oriented Software Engineering, pp. 174–185
L. Padgham and M. Winikoff (2002) Prometheus: a pragmatic methodology for engineering intelligent agents, in Proceedings of the OOPSLA 2002 Workshop on Agent-Oriented Methodologies, pp. 97–108
Barenji RV, Barenji AV, Hashemipour M (2014) A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. Int J Adv Manuf Technol 71:1773–1791
Barenji AV, Barenji RV, Hashemipour M (2016) Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Adv Eng Softw 91:1–11
Baykasoglu A, Gorkemli L (2016) Dynamic virtual cellular manufacturing through agent-based modelling. Int J Comput Integr Manuf:1–16
Sahin C, Demirtas M, Erol R, Baykasoğlu A, Kaplanoğlu V (2015) A multi-agent based approach to dynamic scheduling with flexible processing capabilities. J Intell Manuf:1–19
Renna P (2010) Job shop scheduling by pheromone approach in a dynamic environment. Int J Comput Integr Manuf 23(5):412–424
Padgham L, Winikoff M (2005) Prometheus: a practical agent-oriented methodology. Agent-oriented methodologies:107–135
L. Padgham, J. Thangarajah, and M. Winikoff (2007) The Prometheus design tool—a conference management system case study, in International Workshop on Agent-Oriented Software Engineering, pp. 197–211
L. Padgham and M. Winikoff (2005) Developing intelligent agent systems: a practical guide vol. 13: John Wiley & Sons
R. H. Bordini, M. Dastani, and M. Winikoff (2006) Current issues in multi-agent systems development, in International Workshop on Engineering Societies in the Agents World, pp. 38–61
Gascueña JM, Fernández-Caballero A (2011) Agent-oriented modeling and development of a person-following mobile robot. Expert Syst Appl 38:4280–4290
M. Winikoff (2005) JACK™ intelligent agents: an industrial strength platform, in Multi-Agent Programming, ed: Springer, pp. 175–193
A. Shaygan and R. V. Barenji (2016) Simulation platform for multi agent based manufacturing control system based on the hybrid agent, arXiv preprint arXiv:1603.07766
Gurumurthy A, Kodali R (2011) Design of lean manufacturing systems using value stream mapping with simulation: a case study. J Manuf Technol Manag 22:444–473
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Barenji, A.V., Barenji, R.V., Roudi, D. et al. A dynamic multi-agent-based scheduling approach for SMEs. Int J Adv Manuf Technol 89, 3123–3137 (2017). https://doi.org/10.1007/s00170-016-9299-4
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DOI: https://doi.org/10.1007/s00170-016-9299-4