This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)—one of the most efficient metaheuristic optimization search techniques. Assembly lines of a specific car factory considered as a case study are balanced by the ACS to optimize their energy resource management. The workload variance of the line is performed as the objective function to be minimized in order to increase the productivity. In this work, the ACS is used to address the number of tasks assigned for each workstation, while the sequence of tasks is assigned by factory. Three real-world assembly line balancing (ALB) problems from a specific car factory are tested. Results obtained by the ACS are compared with those obtained by the genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms. By using the ACS, the productivity can be increased and the energy consumption of the lines can be decreased significantly.
KeywordsAdaptive Current SearchAssembly Line BalancingEnergy Resource ManagementMetaheuristic Optimization
W. C. Turner, “Energy Management Handbook,” Fairmont Press, USA, 2004.
B. L. Capehart, W. C. Turner and W. J. Kennedy, “Guide to Energy Management,” Fairmont Press, USA, 1983.
D. T. Pham and D. Karaboga, “Intelligent Optimization Techniques,” Springer, London, 2000. http://dx.doi.org/10.1007/978-1-4471-0721-7
F. Glover and G. A. Kochenberger, “Handbook of Metaheuristics,” Kluwer Academic Publishers, New York, 2003.
E. G. Talbi, “Metaheuristics form Design to Implementation,” John Wiley & Sons, New Jersey, 2009.
X. S. Yang, “Nature-Inspired Metaheuristic Algorithms,” Luniver Press, United Kingdom, 2010.
X. S. Yang, “Engineering Optimization: An Introduction with Metaheuristic Applications,” John Wiley & Sons, New Jersey, 2010. http://dx.doi.org/10.1002/9780470640425
S. S. Rao, “Engineering Optimization: Theory and Practice,” John Wiley & Sons, New Jersey, 2009. http://dx.doi.org/10.1002/9780470549124
K. Sopian, A. Zaharim, Y. Ali and Z. Nopiah, “Optimal Operational Strategy for Hybrid Renewable Energy System using Genetic Algorithms,” WSEAS Transactions on Mathematics, Vol. 7, No. 4,2008, pp. 130-140.
Y. A. Katsigiannis and P. S. Georgilakis, “Optimal Sizing of Small Isolated Hybrid Power Systems Using Tabu Search,” Journal of Optoelectronics and Advanced Materials, Vol. 10, No. 5, 2008, pp. 1241-1245.
V. Aristidis, P. Maria and L. Christos, “Particle Swarm Optimization (PSO) Algorithm for Wind Farm Optimal Design,” International Journal of Management Science and Engineering Management, Vol. 5, No. 1, 2010, pp. 53-58.
T. Ratniyomchai, A. Oonsivilai, P. Pao-La-Or and T. Kulworawanichpong, “Economic Load Dispatch using Improved Harmony Search,” WSEAS Transactions on Systems and Control, Vol. 5, No. 4, 2010, pp. 248-257.
M. D. Kilbridge and L. Wester, “A Heuristic Method of Assembly Line Balancing,” The Journal of Industrial Engineering, Vol. 12, No. 4, 1961, pp. 292-298.
M. Amen, “Heuristic Methods for Cost-Oriented Assembly Line Balancing: A Comparison on Solution Quality and Computing Time,” International Journal of Production Economics, Vol. 69, No. 3, 2001, pp. 255-264. http://dx.doi.org/10.1016/S0925-5273(99)00096-1
J. Rubinovitz and G. Lavitin, “Genetic Algorithm for Assembly Line Balancing,” International Journal of Production Economics, Vol. 41, No. 1-3, 1995, pp. 343-354. http://dx.doi.org/10.1016/0925-5273(95)00059-3
W. C. Chiang, “The Application of the Tabu Search Metaheuristic to the Assembly Line Balancing Problem,” Journal of Operation Research, Vol. 77, 1998, pp. 209-227.
S. D. Lapierre, A. Ruiz and P. Sariano, “Balancing Assembly Lines with Tabu Search,” European Journal of Operation Research, Vol. 168, No. 3, 2006, pp. 826-837. http://dx.doi.org/10.1016/j.ejor.2004.07.031
J. G. Fernando and J. R. Almeida, “Hybrid Genetic Algorithm for Assembly Line Balancing,” Journal of Heuristics, Vol. 8, No. 6, 2002, pp.629-642. http://dx.doi.org/10.1023/A:1020377910258
S. Suwannarongsri, S. Limnararat and D. Puangdownreong, “A New Hybrid Intelligent Method for Assembly Line Balancing,” Proceeding of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2007), Singapore, 2-4 December 2007, pp.1115-1119. http://dx.doi.org/10.1109/IEEM.2007.4419365
A. Sukulin and D. Puangdownreong, “A Novel Metaheuristic Optimization Algorithm: Current Search,” Proceeding of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED'12), Cambridge, 22-24 February 2012, pp. 125-130.
A. Sukulin and D. Puangdownreong, “Control Synthesis for Unstable Systems via Current Search,” Proceeding of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '12), Cambridge, 22-24 February 2012, pp. 131-136.
A. Sukulin and D. Puangdownreong, “Current Search and Applications in Analog Filter Design Problems,” Communication and Computer, Vol. 9, No. 9, 2012, pp. 1083-1096.
S. Suwannarongsri, T. Bunnag and W. Klinbun, “Energy Resource Management of Assembly Line Balancing Problem Using Modified Current Search,” International Journal of Intelligent Systems and Applications (IJISA), Vol. 6, No. 2, 2013, in Press.
A. L. Gutjahr and G. L. Nemhauser, “An Algorithm for the Balancing Problem,” Management Science, Vol. 11, No. 2, 1964, pp. 23-25. http://dx.doi.org/10.1287/mnsc.11.2.308
A. L. Arcus, “COMSOAL: A Computer Method of Sequencing Operations for Assembly Line,” International Journal of Production Research, Vol. 4, 1966, pp. 25-32.
Sammitr Motors Manufacturing Public Co., Ltd., 2013. http://www.sammitr.com/
D. E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning,” Addison Wesley Publishers, Boston, 1989.
MathWorks, “Genetic Algorithm and Direct Search Toolbox: For Use with MATLAB,” User’s Guide, Version 1, MathWorks, Natick, 2005.
F. Glover, “Tabu Search. Part I,” ORSA Journal on Computing, Vol. 1, No. 3, 1989, pp. 190-206.
F. Glover, “Tabu Search. Part II,” ORSA Journal on Computing, Vol. 2, No. 1, 1990, pp. 4-32. http://dx.doi.org/10.1287/ijoc.2.1.4