Application of Hybrid Simulation in production scheduling in job shop systems

This work seeks to study one of the most complex and important issues in production scheduling research: flexible job shop systems. These systems are extremely important for industry, which uses the make-to-order strategy and seeks mix and volume flexibility. The model system will use agents within discrete-event simulation models, generating a Hybrid Simulation model. The agent will sequence the production orders at the beginning of the process and re-sequence them, when necessary, in order to achieve a multi-objective optimization. For this, the agent will bring together two different logics that have opposing goals. This work consists of the comparison of the results of three scheduling methods: firstly, with the sequence of arrival; secondly, with the agent using one sequencing logic; and, finally, using the same logic, but with adjustments in the sequence during the batch production, seeking to improve the negative points generated by the logic. It also stresses that this schedule ensures that the Manager Agent reduces makespan and increases machine utilization while increasing its interference in the model. This is a quantitative study, using the modeling and simulation method and following an empirical model.

[1]  Celso Leandro Palma,et al.  Handbook of simulation: principles, methodology, advances, applications, and practice , 2016 .

[2]  Nigel Gilbert,et al.  Multi-Agent Systems and Agent-Based Simulation , 1998, Lecture Notes in Computer Science.

[3]  Alfredo Garro,et al.  Agent-based simulation for the evaluation of a new dispatching model for the straddle carrier pooling problem , 2015, Simul..

[4]  Huaiqing Wang,et al.  Multi-agent-based proactive–reactive scheduling for a job shop , 2012 .

[5]  Charles M. Macal,et al.  Everything you need to know about agent-based modelling and simulation , 2016, J. Simulation.

[6]  Jirachai Buddhakulsomsiri,et al.  Simulation modeling and analysis for production scheduling using real-time dispatching rules: A case study in canned fruit industry , 2010 .

[7]  José Arnaldo Barra Montevechi,et al.  Analysis of the applicability of the IDEF-SIM modeling technique to the stages of a discrete event simulation project , 2014, Proceedings of the Winter Simulation Conference 2014.

[8]  Adelinde M. Uhrmacher,et al.  Proceedings of the Winter Simulation Conference , 2012, WSC 2012.

[9]  Bouziane Beldjilali,et al.  A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products , 2015, Service Orientation in Holonic and Multi-agent Manufacturing.

[10]  L. Hendry,et al.  The SHEN model for MTO SMEs: A performance improvement tool , 2003 .

[11]  G. Huse.  Individual‐based Modeling and Ecology , 2008 .

[12]  Saeed Mansour,et al.  Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach , 2011 .

[13]  Parham Azimi,et al.  Designing of an intelligent self-adaptive model for supply chain ordering management system , 2015, Eng. Appl. Artif. Intell..

[14]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[15]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[16]  Ali S. Kiran,et al.  Simulation and Scheduling , 2007 .

[17]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[18]  Yasuhiro Sudo,et al.  Agent based Manufacturing Simulation for Efficient Assembly Operations , 2013 .

[19]  Wai Kin Chan,et al.  Agent-based simulation tutorial - simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation , 2010, Proceedings of the 2010 Winter Simulation Conference.

[20]  A. Y. C. Nee,et al.  Agent-based distributed scheduling for virtual job shops , 2010 .

[21]  M. X. Weng,et al.  Multi-agent-based workload control for make-to-order manufacturing , 2008 .

[22]  Jayendran Venkateswaran,et al.  Simulation and optimisation based approach for job shop scheduling problems , 2016, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[23]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[24]  Weiming Shen,et al.  Applications of agent-based systems in intelligent manufacturing: An updated review , 2006, Adv. Eng. Informatics.

[25]  Gongfa Li,et al.  A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints , 2017, Eur. J. Oper. Res..

[26]  V. Vinod,et al.  Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system , 2011 .

[27]  B. Williams,et al.  Operations management. , 2001, Optometry.

[28]  Jerry Banks,et al.  Handbook of simulation - principles, methodology, advances, applications, and practice , 1998, A Wiley-Interscience publication.

[29]  Mohammad Fathian,et al.  A new critical path method and a memetic algorithm for flexible job shop scheduling with overlapping operations , 2013, Simul..

[30]  Fabiano Leal,et al.  Using Discrete-Event Simulation in Urban Solid Waste Selection , 2015 .

[31]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[32]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[33]  Peer-Olaf Siebers,et al.  Discrete-event simulation is dead, long live agent-based simulation! , 2010, J. Simulation.

[34]  R Taylor,et al.  Simulation as an essential tool for advanced manufacturing technology problems , 2000 .

[35]  Amos H. C. Ng,et al.  A simulation-based scheduling system for real-time optimization and decision making support , 2011 .