Abstract
In this paper, a multi-product multi-machine serial production line operated under a constant-work-in-process protocol is considered. A mathematical model for the system is first presented, and then an artificial bee colony optimization algorithm is applied to simultaneously find the optimal work-in-process inventory level as well as job sequence order in order to minimize the overall makespan time. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, the proposed method does not use a linearized or simplified model of the system. A production line simulator implemented on MATLAB is, instead, employed to model the highly nonlinear dynamics of the production line and is used to evaluate the candidate solutions. The efficiency of the proposed approach, even for systems of large sizes, is validated via numerical simulations.
Similar content being viewed by others
References
Akay, B., & Karaboga, D. (2010). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, Published online March 2010. doi:10.1007/s10845-010-0393-4.
Bonvik A. M., Couch C. E., Gershwin S. B. (1997) A comparison of production-line control mechanisms. International Journal of Production Research 25(3): 789–804
Cao D., Chen M. (2005) A mixed integer programming model for a two line CONWIP-based production and assembly system. International Journal of Production Economics 95(3): 317–326
Chang T. M., Yih Y. (1994) Generic Kanban systems for dynamic environments. International Journal of Production Research 32: 889–902
Framinan J. M., Gonzleza P. L., Ruiz-Usano R. (2003) The CONWIP production control system: Review and research issues. Production Planning and Control 14: 255–265
Framinan J. M., Gonzleza P. L., Ruiz-Usano R. (2006) Dynamic card controlling in a Conwip system. International Journal of Production Economics 99(1/2): 102–116
Garey M. R., Johnson D. S. (1979) Computers and interactability: A guide to the theory of NP-completeness. Freeman, San Francisco
Gaury E. G. A., Pierreval H., Kleijnen J. P. C. (2000) An evolutionary approach to select a pull system among Kanban, Conwip and Hybrid. Journal of Intelligent Manufacturing 11: 157–167
Golany B., Dar-EL E. M., Zeev N. (1999) Controlling shop floor operations in a multi-family, multi-cell manufacturing environment through constant work-in-process. IIE Transactions 31: 771–781
Goldratt E. M., Cox J. (1985) The goal. North River Press, Croton-on-Hudson, NY
Gstettner S., Kuhn H. (1996) Analysis of production control systems Kanban and Conwip. International Journal of Production Research 34(11): 3253–3274
Herer Y. T., Masin M. (1997) Mathematical programming formulation of CONWIP-based production lines; and relationships to MRP. International Journal of Production Research 35(4): 1067–1076
Hopp W. J., Spearman M. L. (1991) Throughput of a constant work in process manufacturing line subject to failures. International Journal of Production Research 29(3): 635–655
Hopp W. J., Roof M. L. (1998) Setting WIP levels with statistical throughput control (STC) in CONWIP production lines. International Journal of Production Research 36(4): 867–882
Ip W. H., Huang M., Yung K. L., Wang D., Wang X. (2007) CONWIP based control of a lamp assembly production line. Journal of Intelligent Manufacturing 18(2): 261–271
Karaboga N. (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute 346: 328–348
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06 , Kayseri, Turkey: Erciyes University.
Karaboga D., Basturk B. (2008) On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8: 687–697
Karaboga D., Akay B. (2009) A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214(1): 108–132
Khojasteh-Ghamari, Y. (2010). Developing a framework for performance analysis of a production process controlled by Kanban and CONWIP. Journal of Intelligent Manufacturing, Published online October 2009. doi:10.1007/s10845-009-0338-y.
Knolmayer G., Mertens P., Zeier A. (2002) Supply chain management based on SAP systems. Springer, Berlin
Kumar S. K., Tiwari M. K., Babiceanu R. F. (2010) Minimisation of supply chain cost with embedded risk using computational intelligence approaches. International Journal of Production Research 48(13): 3717–3739
Lambrecht M., Segaert A. (1990) Buffer stock allocation and assembly type production lines. International Journal of Operations and Production Management 10(2): 47–61
Li N., Zhang M. T., Deng S., Lee Z. H., Zhang L., Zheng L. (2007) Single-station performance evaluation and improvement in semiconductor manufacturing: A graphical approach. International Journal of Production Economics 107(2): 397–403
Luh P. B., Zhou X., Tomastik R. N. (2000) An effective method to reduce inventory in job shops. IEEE Transactions on Robotics and Automation 16: 420–424
Marek, R. P., Elkins, D. A., & Smith, D. R. (2001). Understanding the fundamentals of Kanban and CONWIP pull systems using simulation. In Proceedings of the 2001 winter simulation conference, Vol. 2, pp. 921–929.
Pan Q. K., Tasgetiren M. F., Suganthan P. N., Chua T. J. (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences 181(12): 2455–2468
Sato, R., & Khojasteh-Ghamari, Y. (2010). An integrated framework for card-based production control systems. Journal of Intelligent Manufacturing, Published online June 2010. doi:10.1007/s10845-010-0421-4.
Singh A. (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing 9(2): 625–631
Spearman M. L., Woodruff D. L., Hopp W. J. (1989) A hierarchical control architecture for constant work-in-process (CONWIP) production systems. Journal of Manufacturing and Operations Management 2: 147–171
Spearman M. L., Woodruff D. L., Hopp W. J. (1990) Conwip: a pull alternative to Kanban. International Journal of Production Research 28: 879–894
Spearman M. L., Zazanis M. A. (1992) Push and Pull Production Systems: Issues and Comparison. Operations Research 40(3): 521–532
Sundar, S., & Singh, A. (2010). A hybrid heuristic for the set covering problem. Operational Research: An International Journal, Pub- lished online September 2010. doi:10.1007/s12351-010-0086-y.
Szeto W. Y., Wu Y., Ho S. C. (2011) An artificial bee colony algorithm for the capacitated vehicle routing problem. European Journal of Operational Research 215(1): 126–135
Yeh W. C., Hsieh T. J. (2011) Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Computers and Operations Research 38(11): 1465–1473
Zhang W., Chen M. (2001) A mathematical programming model for production planning using CONWIP. International Journal of Production Research 39(12): 2723–2734
Zhang C., Ouyang D., Ning J. (2010) An artificial bee colony approach for clustering. Expert Systems with Applications 37: 4761–4767
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ajorlou, S., Shams, I. Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment. J Intell Manuf 24, 1145–1156 (2013). https://doi.org/10.1007/s10845-012-0646-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-012-0646-5