Main Article Content

Pawel Pawlewski
Poznan University of Technology (Poland)
Kamila Kluska
Poznan University of Technology (Poland)
Vol. 6 No. 1 (2017), Articles, pages 59-72
Accepted: Feb 16, 2017


This paper presents the results of the project, which goal is to analyze the production process capability after reengineering the assembly process due to expansion of a bus production plant. The verification of the designed work organization for the new configuration of workstations on new production hall is necessary. To solve these  problems authors propose a method based on mixing DES (Discrete Event Simulation) and ABS (Agent Based Simulation) approach. DES is using to model the main process – material flow (buses), ABS is using to model assembling operations of teams of  workers.One of obtained goal is to build a simulation model, which presents the new assembly line in the factory, taking into ac-count the arrangement of workstations and work teams in the new production hall as well as the transport between workstations. Second goal is to present work organization of work teams and division of individual workers’ labor (who belongs to a particular work team and performs operations on buses in a particular workstation) in order to determine the best allocation of tasks and the optimum size of individual work teams. Proposed solution enables to determine the effect of assembly interferences on the work of particular work teams and the efficiency of the whole production system, to define the efficiency of the designed assembly lines and proposing changes aimed at the quality improvement of the created conception. 


Download data is not yet available.

Article Details


Adham, A. A. J., Kamar, A. N. N., 2014. A novel method to develop an automobile assembly line system. In International Journal of Physical sciences, 9(19):430–437.

Angelidis, E., Bohn, D., Rose, O., 2012. A Simulation-Based Optimization Heuristic Using Self-Organization For Complex Assembly Lines. In Proceedings of the 2012 Winter Simulation Conference (WSC).

Bartkowiak, T., Gessner, A., 2014. Modeling Performance of a Production Line and Optimizing Its Efficiency by Means of Genetic Algorithm. ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis – Volume 3: Engineering Systems; Heat Transfer and Thermal Engineering; Materials and Tribology; Mechatronics; Robotics Copenhagen, Denmark, ASME.

Beaverstock, M., Greenwood, A., Lavery, E., Nordgren, W., 2011. Applied Simulation. Modeling and Analysis using Flexsim, Flexsim Software Products, Inc., Canyon Park Technology Center, Orem, USA.

Bozarth, C., Handfield, R. B., 2012. Introduction to Operations and Supply Chain Management, Prentice Hall, 3 edition, Pearson.

Chandra, S., Al Salamah, M., Ali, V., 2014. Stochastic simulation of assembly line for optimal sequence using Petri Nets (PN). In IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 11(2):26–33.

Cox, J. F., Blackstone, J. H., 2002. APICS Dictionary, Alexandria, VA, APICS.

Di Gironimo, G., Patalano, S., and Tarallo, A., 2009. Innovative assembly process for modular train and feasibility analysis in virtual environment. In International Journal on Interactive Design and Manufacturing (IJIDeM) May 2009, 3(2):93–101.

Fortino, G., North, M. J., 2013. Simulation-based development and validation of multi-agent systems: AOSE and ABMS approaches. In Journal of Simulation, 7(3).

Huang, Y., Verbraeck A., Seck M., 2016. Graph transformation based simulation model generation. In Journal of Simulation, November 2016, 10(4):283–309.

Jasiulewicz-Kaczmarek, M., Dro?yner, P., 2011. Maintenance Management Initiatives towards Achieving Sustainable Development. In P. Golinska et al. (eds.): Information Technologies in Environmental Engineering Environmental Science and Engineering, pages 707-721. Springer-Verlag Berlin Heidelber.

Jayaprakash, J., Manoj, K., Ambedkar, P., 2015. Simulation of Mixed Model Assembly Line Sequencing Using PRO-Model Software. In International Journal of Applied Engineering Research, 10 (68).

Korytkowski, P., Karkoszka, R., 2016. Simulation based efficiency analysis of an in-plant milk-run operator under disturbances. In International Journal of Advanced Manufacturing Technology, 82(5):827–837.

Kucha?, Š., Vondrák, I., 2016. Automatic allocation of resources in software process simulations using their capability and productivity. In Journal of Simulation, August 2016, 10(3):227–236.

Li Da Xu, Wang, Ch., Bi, M. Z., Yu, J., 2012. AutoAssem: An Automated Assembly Planning System for Complex Products. In IEEE Transactions on Industrial Informatics, 8(3):669–678.

Macal, Ch. M., North, M. J., 2007. Agent-Based Modeling and Simulation: Desktop ABMS. In Henderson, S. G., Biller, B., Hsieh, M. H., Shortle, J., Tew, D. J., Barton, R. R. (eds) Proceedings of the 2007 Winter Simulation Conference (WSC).

Merdan, M., Moser, T., Sunindyo, W., Biff's, S., Vrba, P., 2013. Workflow scheduling using multi-agent systems in a dynamically changing environment. In Journal of Simulation, 7(3):144–158. Https://

Pawlewski, P., 2015. DES/ABS Approach to Simulate Warehouse Operations. In Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection Communications in Computer and Information Science, Volume 524. Springer.

Pechoucek, M., Riha, A., Vokrinek, J., Marik, V., Prazma, V., 2002. ExPlanTech: applying multi-agent systems in production planning. In International Journal of Production Research, 40(15):3681–3692.

Savino, M. M., Mazza, A., 2012. Agent Based Resources Allocation in Job Shop with Re-entrant Features: A Benchmarking Analysis. In Advances in Production Management Systems Competitive Manufacturing for Innovative Products and Services. Springer.

Scholl, A., Becker, C., 2003. A survey on problems and methods in generalized assembly line balancing. In European Journal of Operational Research, 168(3):694–715. Elsevier.

Siebers, P. O., Macal, C. M., Garnett, J., Buxton, D., and Pidd, M., 2010. Discrete-Event Simulation is Dead, Long Live Agent-Based Simulation!. In Journal of Simulation, 4(3):204–210. Springer.

Vrba, P., Haecuba, O., Klima, M., Marik, V., 2015. Agent-Based Production Scheduling for Aircraft Manufacturing Ramp-up. In Mark, V., Schirrman, A., Trentesaux, D., Vrba, V. (eds.) Industrial Applications of Holonic and Multi-Agent Systems, Lecture Notes in Computer Science, 9266:145–156. Springer.

Yazgan, H. R., Beypinar, I., Boran, S., Ocak, C., 2011. A new algorithm and multi-response Taguchi method to solve line balancing problem in an automotive industry. In The International Journal of Advanced Manufacturing Technology, 57(1):379–392. Springer.

Zemczak, M., 2013. Zagadnienie balansowania linii monta?owej i szeregowania zada? w systemach produkcji mixed-model. Informatyczne systemy zarz?dzania, tom 4 (Wybrane zastosowania). red. nauk. Marcin Relich., Wydawnictwo Uczelniane Politechniki Koszali?skiej.

Zhang, T., Rose, O., 2013. Scheduling in a flexible job shop with continuous operations at the last stage. In Dangelmaier, W., Laroque, Ch., Klaas, A. (eds.)Simulation in Produktion und Logistik Entscheidungsunterstützung von der Planung bis zur Steuerung, Paderborn, HNI-Verlagsschriftenreihe.