Abstract
Over the last few decades sophisticated line balancing methodologies have been developed in response to the emerging trend of lean production in order to more effectively streamline production sequence scheduling. Newer approaches now demonstrate the potential to achieve lean production while at the same time simplifying what have become increasingly complex analytical methodologies and production sequences. Unlike past studies on line balancing for mixed-model production lines, in this paper the impacts of line balance on Control Dynamic Complexity are investigated and their deployment illustrated with a case study. Precedence matrices have been studied and a combined precedence matrix computed. The mathematical programming language Matlab is employed to illustrate state diagrams representing the relationships between the tasks and terminals. It is found that control dynamic complexities are higher for some particular sequences, and in such cases further explanations are offered. Furthermore, this study reveals that it is possible to minimize the complexities by better exploiting some aspects of line scheduling guidelines. Since line balancing and line scheduling are NP hard issues, this paper has also demonstrated ways in which computational analysis of multiple iterations can be significantly accelerated in future by deploying ant colony optimization techniques. Analysis of complexity level highlights what may otherwise be intuitive, that excessive sophistication of line balance techniques is very likely to be detrimental to an overall organization if resulting scheduling becomes difficult to practically monitor.





















Similar content being viewed by others
References
Vigano, R., Ginezm, G.O.: Automatic assembly sequence exploration without precedence definition. Int. J. Interact. Des. Manuf. 7, 79–89 (2013)
Vosniakos, G.C., Gogouvitis, X.V.: Structured design of flexibly automated manufacturing cells through semantic models and petri nets in a virtual reality environment. Int. J. Interact. Des. Manuf. 9, 45–63 (2015)
Luiza, D., Daniela, G., Alexandrus, E.: Modeling and estimation of the product time and cost in manufacturing system-market relationship. Int. J. Interact. Des. Manuf. 8, 277–282 (2014)
Zhao, C., Li, J., Huang, N., DeCroix, G.: Optimal planning of plant flexibility: Problem formulation and performance analysis. IEEE Trans. Autom. Sci. Eng. 14(2), 718–731 (2017)
Thomopoulos, N.T.: Mixed model line balancing with smoothed station assigments. Manag. Sci. 16(9), B59–B75 (1970)
Fokkert, J.I.Z.: The mixed and multi model line balancing problem: a comparison. Eur. J. Oper. Res. 100, 399–412 (1997)
Emde, S., Boysen, N., Scholl, A.: Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload. Int. J. Prod. Res. 48(11), 3173–3191 (2010)
Raj, V., Mathewa, J., Josea, P., Sivana, G.: Optimization of cycle time in an assembly line balancing problem. ScienceDirect. Procedia Technol. 25, 1146–1153 (2016)
Legarretaetxebarria, A., Quartulli, M., Olaizola, I., Serrano, M.: Optimal scheduling of manufacturing processes across multiple production lines by polynomial optimization and bagged bounded binary knapsackp. Int. J. Interact. Des. Manuf. 8, 277–282 (2014)
Young R.: 8 ways to... reduce complexity. Financial Manag. 36–37 (2011)
Dix, J.: Getting a handle on complexity. Network World. ProQuest (2011)
Shannon, C.E.: The mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
ElMaraghy, W.H., Urbanic, R.J.: Modelling of manufacturing systems complexity. CIRP Ann. Manuf. Technol. 52(1), 363–366 (2003)
Fanti, M.P., Rotunno, G., Stecco, G., Ukovich, W., Mininel, S.: An integrated system for production scheduling in steelmaking and casting plants. IEEE Trancs. Autom. Sci. Eng. 13(2), 1112–1128 (2016)
Deshmukh, A.V., Talavag, J.J., Barash, M.M.: Complexity in manufacturing systems Part 1: analysis of static complexity. IIE Trans. 30(7), 645–655 (1998)
Alamoudi, R.H.: Interaction based measure of manufacturing systems complexity and supply chain systems vulnerability using Information Entropy. Ph.D. Dissertation Submitted to the Faculty of the University of Miami (2008)
Martin, K.: Entropy as a fixed point. Theor. Comput. Sci. 350, 292–324 (2006)
Zhang, Z., Xiao, R.: Empirical study on entropy models of cellular manufacturing systems. Prog. Nat. Sci. 19, 389–395 (2009)
Zhang, Z.: Manufacturing complexity and its measurement based on entropy models. Int. J. Adv. Manuf. Technol. 62, 867–873 (2012)
Zhang, Z.: Modeling complexity of cellular manufacturing systems. Appl. Math. Model. 35, 4189–4195 (2011)
Smart, J., Calinescu, A., Huatuco, L.H.: Extending the informationtheoretic measures of the dynamic complexity of manufacturing systems. Int. J. Prod. Res. 51(2), 362–379 (2013)
Frizelle, G., Woodcock, E.: Measuring complexity as an aid to developing operational strategy. Int. J. Oper. Prod. Manag. 15(5), 26–39 (1995)
Cho, S., Alamoudi, R., Asfour, S.: Interaction-based complexity measure of manufacturing systems using information entropy. Int. J. Comput. Integr. Manuf. 22(10), 909–922 (2009)
Baewicz, J., Ecker, K.H., Pesch, E., Schmid, G., Weglarz, J.: Scheduling Computer and Manufacturing Processes, 2nd edn, pp. 9–36. Springer, Berlin (2001)
Duan, Q., Zeng, J., Chakrabarty, K., Dispoto, G.: Real-time production scheduler for digital-print-service providers based on a dynamic incremental evolutionary algorithm. IEEE Trans. Autom. Sci. Eng. 12(2), 701–715 (2015)
Li, D., Zhan, R., Zheng, D., Li, M., Kaku, I.: A hybrid evolutionary hyper-heuristic approach for intercell scheduling considering transportation capacity. IEEE Trans. Autom. Sci. Eng 13(2), 1072–1089 (2016)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern. 26(1), 29–41 (1996)
Kucukkoc, I., Zhang, D.Z.: Balancing of parallel U-shaped assembly lines. Comput. Oper. Res. 64, 233–244 (2015)
Li, D., Li, M., Meng, X., Tian, Y.: A hyperheuristic approach for intercell scheduling with single processing machines and batch drocessing machines. IEEE Trans. Syst. Man Cybern. Syst. 45(2), 315–325 (2015)
Xing, L.N., Chen, Y.W., Yang, K.W.: Multi-objective flexible job shop schedule: design and evaluation by simulation modeling. Appl. Soft Comput. 9, 362–376 (2009)
Law, E.K.W., Yung, W.K.C.: Optimizing the smoothness of production lines with considerations of structural and dynamic complexities for electronics manufacturing. In: IEEE International Conference on Consumer Electronics-China. IEEE Xplore, 13 February 2017, INSPEC Accession Number: 16669831 (2017)
Law, E.K.W., Yung, W.K.C.: Production line configuration and impact on complexities. Int. J. Technol. Eng. Stud. (IJTES) 3(4), 141–146 (2017)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Law, E.K.W., Yung, W.K.C. Optimizing control dynamic complexity and production schedule. Int J Interact Des Manuf 13, 47–58 (2019). https://doi.org/10.1007/s12008-018-0488-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12008-018-0488-2