Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm

Affiliation(s)

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.

ABSTRACT

Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered; minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.

Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered; minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.

Cite this paper

nullP. Chutima and P. Olanviwatchai, "Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm,"*Journal of Software Engineering and Applications*, Vol. 3 No. 4, 2010, pp. 347-363. doi: 10.4236/jsea.2010.34040.

nullP. Chutima and P. Olanviwatchai, "Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm,"

References

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[24] U. Martinez and W. S. Duff, “Heuristic Approaches to Solve the U-Shaped Line Balancing Problem Augmented by Genetic Algorithms,” Proceedings of the 2004 Systems and Information Engineering Design Symposium, Char-lottesville, 16 April 2004, pp. 287-293.

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[27] T. L. Urban and W.-C. Chiang, “An Optimal Piece-wise-Linear Program for the U-Line Balancing Problem with Stochastic Task Times,” European Journal of Opera-tional Research, Vol. 168, No. 3, 2006, pp. 771-782.

[28] W.-C. Chiang and T. L. Urban, “The Stochastic U-Line Balancing Problem: A Heuristic Procedure,” European Journal of Operational Research, Vol. 175, No. 3, 2006, pp. 1767-1781.

[29] Y. Kara, T. Paksoy and C. T. Chang, “Binary Fuzzy Goal Programming Approach to Single Model Straight and U-Shaped Assembly Line Balancing,” European Journal of Operational Research, Vol. 195, No. 2, 2009, pp. 335- 347. A. L. Arcus, “COMSOAL: A Computer Method of Sequencing Operations for Assembly Lines,” International Journal of Production Research, Vol. 4, No. 4, 1965, pp. 259-277.

[30] A. Baykasoglu, “Multi-Rule Multi-Objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems,” Journal of Intelligent Manu-facturing, Vol. 17, No. 2, 2006, pp. 217-232.

[31] R. K. Hwang, H. Katayama and M. Gen, “U-Shaped As-sembly Line Balancing Problem with Genetic Algorithm,” International Journal of Production Research, Vol. 46, No. 16, 2008, pp. 4637-4649.

[32] R. K. Hwang and H. Katayama, “A Multi-Decision Genetic Approach for Workload Balancing of Mixed-Model U-Shaped Assembly Line Systems,” International Journal of Production Research, Vol. 47, No. 14, 2009, pp. 3797- 3822.

[33] A. Baykasoglu and T. Dereli, “Simple and U-Type As-sembly Line Balancing by Using an Ant Colony Based Algorithm,” Mathematical and Computational Applica-tions, Vol. 14, No. 1, 2009, pp. 1-12.

[34] G. J. Miltenburg, “Balancing and Scheduling Mixed-Model U-Shaped Production Lines,” International Journal of Flexible Manufacturing Systems, Vol. 14, No. 2, 2002, pp. 119-151.

[35] Y. K. Kim, S. J. Kim and J. Y. Kim, “Balancing and Se-quencing Mixed-Model U-Lines with a Co-Evolutionary Algorithm,” Production Planning and Control, Vol. 11, No. 8, 2000, pp. 754-764.

[36] Y. K. Kim, J. Y. Kim and Y. Kim, “An Endosymbiotic Evolutionary Algorithm for the Integration of Balancing and Sequencing in Mixed-Model U-Lines,” European Journal of Operational Research, Vol. 168, No. 3, 2006, pp. 838-852.

[37] S. Agrawal and M. K. Tiwari, “A Collaborative Ant Col-ony Algorithm to Stochastic Mixed-Model U-Shaped Dis-assembly Line Balancing and Sequencing Problem,” In-ternational Journal of Production Research, Vol. 46, No. 6, 2008, pp. 1405-1429.

[38] I. Sabuncuoglu, E. Erel and A. Alp, “Ant Colony Optimi-zation for the Single Model U-Type Assembly Line Bal-ancing Problem,” International Journal of Production Economics, Vol. 120, No. 2, 2009, pp. 287-300.

[39] Y. Kara, U. Ozcan and A. Peker, “An Approach for Bal-ancing and Sequencing Mixed-Model JIT U-Lines,” In-ternational Journal of Advanced Manufacturing Technol-ogy, Vol. 32, No. 11-12, 2007, pp. 1218-1231.

[40] Y. Kara, “Line Balancing and Model Sequencing to Reduce Work Overload in Mixed-Model U-Line Production Environments,” Engineering Optimization, Vol. 40, No. 7, 2008, pp. 669-684.

[41] A. Konak, D. W. Coit and A. E. Smith, “Multi-Objective Optimization Using Genetic Algorithms: A Tutorial,” Re-liability Engineering & System Safety, Vol. 91, No. 9, 2006, pp. 992-1007.

[42] C. A. C. Coello, D. A. Veldhuizen and G. B. Lamont, “Evolutionary Algorithms for Solving Multi-Objective Problems,” Kluwer Academic Publishers, Dordrecht, 2002.

[43] E. Zitzler and L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, 1999, pp. 257-271.

[44] C. M. Fonseca and P. J. Fleming, “Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization,” Proceedings of 5th International Conference on Genetic Algorithm, Urbana, June 1993, pp. 416- 423.

[45] C. M. Fonseca and P. J. Fleming, “An overview of Evolu-tionary Algorithms in Multiobjective Optimization,” Evo-lutionary Computation, Vol. 3, No. 1, 1995, pp. 1-16.

[46] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA II,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, 2002, pp. 182-197.

[47] D. Corne, M. Dorigo and F. Glover, “New Ideas in Opti-mization,” McGraw-Hill, London, 1999.

[48] W. Wattanapornprom, P. Olanviwitchai, P. Chutima and P. Chongsatitvatana, “Multi-Objective Combinatorial Op-timisation with Coincidence Algorithm,” Proceedings of IEEE Congress on Evolutionary Computation, Norway, 11 February 2009, pp. 1675-1682.

[49] J. R. Jackson, “A Computing Procedure for a Line Bal-ancing Problem,” Management Science, Vol. 2, No. 3, 1956, pp. 261-271.

[50] D. E. Goldberg, “Genetic Algorithms in Search, Optimiza-tion, and Machine Learning,” Addison-Wesley, Boston, 1989.

[51] J. Horn, N. Nafpliotis and D. E. Goldberg, “A Niched Pareto Genetic Algorithm for Multiobjective Optimization,” Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Orlando, 27-29 June 1994.

[52] P. Lacomme, C. Prins and M. Sevaux, “A Genetic Algo-rithm for a Bi-Objective Capacitated ARC Routing Prob-lem,” Computer & Operations Research, Vol. 33, No. 12, 2006, pp. 3473-3493.

[53] P. Chutima and P. Pinkoompee, “An Investigation of Local Searches in Memetic Algorithms for Multi-Objective Sequencing Problems on Mixed-Model Assembly Lines,” Proceedings of Computers and Industrial Engineering, Beijing, 31 October-2 November 2008.

[54] R. Kumar and P. K. Singh, “Pareto Evolutionary Algorithm Hybridized with Local Search for Bi-Objective TSP,” Studies in Computational Intelligence (Hybrid Evolutionary Algorithms), Springer, Berlin/Heidelberg, Vol. 75, 2007, pp. 361-398.

[55] D. C. Montomery, “Design and Analysis of Experiments,” John Wiley & Sons, Inc., Hoboken, 2009.

[56] N. T. Thomopoulos, “Mixed Model Line Balancing with Smoothed Station Assignment,” Management Science, Vol. 14, No. 2, 1970, pp. B59-B75.

[57] A. L. Arcus, “COMSOAL: A Computer Method of Se-quencing Operations for Assembly Lines,” International Journal of Production Research, Vol. 4, No. 4, 1965, pp. 259-277.

[1] N. Boysen, M. Fliedner and A. Scholl, “Assembly Line Balancing: Which Model to Use When?” International Journal of Production Economics, Vol. 111, No. 2, 2008, pp. 509-528.

[2] R. J. Schonberger, “Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity,” Free Press, New York, 1982, pp. 140-141.

[3] J. Miltenburg, “U-Shaped Production Lines: A Review of Theory and Practice,” International Journal of Production Economics, Vol. 70, No. 3, 2001, pp. 201-214.

[4] Y. Monden, “Toyota Production System,” 2nd Edition, Industrial Engineering Press, Institute of Industrial Engi-neering, Norcross, 1993.

[5] G. J. Miltenburg and J. Wijngaard, “The U-Line Balancing Problem,” Management Science, Vol. 40, No. 10, 1994, pp. 1378-1388.

[6] C. H. Cheng, G. J. Miltenburg and J. Motwani, “The Effect of Straight- and U-Shaped Lines on Quality,” IEEE Transactions on Engineering Management, Vol. 47, No. 3, 2000, pp. 321-334.

[7] G. J. Miltenburg, “The Effect of Breakdowns on U-Shaped Production Lines,” International Journal of Production Research, Vol. 38, No. 2, 2000, pp. 353-364.

[8] J. Miltenburg, “One-Piece Flow Manufacturing on U- Shaped Production Lines: A Tutorial,” IIE Transactions, Vol. 33, No. 4, 2001, pp. 303-321.

[9] Y. Kara, U. Ozcan and A. Peker, “Balancing and Se-quencing Mixed-Model just-in-Time U-Lines with Multiple Objectives,” Applied Mathematics and Computation, Vol. 184, No. 2, 2007, pp. 566-588.

[10] M. E. Salveson, “The Assembly Line Balancing Problem,” The Journal of Industrial Engineering, Vol. 6, No. 3, 1955, pp. 18-25.

[11] I. Baybars, “A Survey of Exact Algorithms for the Simple Assembly Line Balancing Problem,” Management Science, Vol. 32, No. 8, 1986, pp. 909-932.

[12] S. Ghosh and R. J. Gagnon, “A Comprehensive Literature Review and Analysis of the Design, Balancing and Sched-uling of Assembly Systems,” International Journal of Production Research, Vol. 27, No. 4, 1989, pp. 637-670.

[13] E. Erel and S. C. Sarin, “A Survey of the Assembly Line Balancing Procedures,” Production Planning and Control, Vol. 9, No. 5, 1998, pp. 414-434.

[14] C. Becker and A. Scholl, “A Survey on Problems and Methods in Generalized Assembly Line Balancing,” European Journal of Operational Research, Vol. 168, No. 3, 2006, pp. 694-715.

[15] N. Boysen and M. Fliedner, “A Versatile Algorithm for Assembly Line Balancing,” European Journal of Opera-tional Research, Vol. 184, No. 1, 2008, pp. 39-56.

[16] D. Sparling and J. Miltenburg, “The Mixed-Model U-Line Balancing Problem,” International Journal of Production Research, Vol. 36, No. 2, 1998, pp. 485-501.

[17] G. J. Miltenburg, “Balancing U-Lines in a Multiple U- Line Facility,” European Journal of Operational Research, Vol. 109, No. 1, 1998, pp. 1-23.

[18] T. L. Urban, “Optimal Balancing of U-Shaped Assembly Lines,” Management Science, Vol. 44, No. 5, 1998, pp. 738-741.

[19] D. A. Ajenblit and R. L. Wainwright, “Applying Genetic Algorithms to the U-Shaped Assembly Line Balancing Problem,” Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, Alaska, 1998, pp. 96-101.

[20] A. School and R. Klein, “ULINO: Optimally Balancing U-Shaped JIT Assembly Lines,” International Journal of Production Research, Vol. 37, No. 4, 1999, pp. 721-736.

[21] E. Erel, I. Sabuncuoglu and B. A. Aksu, “Balancing of U-Type Assembly Systems Using Simulated Annealing,” International Journal of Production Research, Vol. 39, No. 13, 2001, pp. 3003-3015.

[22] G. R. Aase, M. J. Schniederjans and J. R. Olson, “U-OPT: An Analysis of Exact U-Shaped Line Balancing Proce-dures,” International Journal of Production Research, Vol. 41, No. 17, 2003, pp. 4185-4210.

[23] G. R. Aase, J. R. Olson and M. J. Schniederjans, “U- Shaped Assembly Line Layouts and their Impact on Labor Productivity: An Experimental Study,” European Journal of Operational Research, Vol. 156, No. 3, 2004, pp. 698- 711.

[24] U. Martinez and W. S. Duff, “Heuristic Approaches to Solve the U-Shaped Line Balancing Problem Augmented by Genetic Algorithms,” Proceedings of the 2004 Systems and Information Engineering Design Symposium, Char-lottesville, 16 April 2004, pp. 287-293.

[25] J. Balakrishnan, C.-H. Cheng, K.-C. Ho and K. K. Yang, “The Application of Single-Pass Heuristics for U-Lines,” Journal of Manufacturing Systems, Vol. 28, No. 1, 2009, pp. 28-40.

[26] H. Gokcen and K. Agpak, “A Goal Programming Ap-proach to Simple U-Line Balancing Problem,” European Journal of Operational Research, Vol. 171, No. 2, 2006, pp. 577-585.

[27] T. L. Urban and W.-C. Chiang, “An Optimal Piece-wise-Linear Program for the U-Line Balancing Problem with Stochastic Task Times,” European Journal of Opera-tional Research, Vol. 168, No. 3, 2006, pp. 771-782.

[28] W.-C. Chiang and T. L. Urban, “The Stochastic U-Line Balancing Problem: A Heuristic Procedure,” European Journal of Operational Research, Vol. 175, No. 3, 2006, pp. 1767-1781.

[29] Y. Kara, T. Paksoy and C. T. Chang, “Binary Fuzzy Goal Programming Approach to Single Model Straight and U-Shaped Assembly Line Balancing,” European Journal of Operational Research, Vol. 195, No. 2, 2009, pp. 335- 347. A. L. Arcus, “COMSOAL: A Computer Method of Sequencing Operations for Assembly Lines,” International Journal of Production Research, Vol. 4, No. 4, 1965, pp. 259-277.

[30] A. Baykasoglu, “Multi-Rule Multi-Objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems,” Journal of Intelligent Manu-facturing, Vol. 17, No. 2, 2006, pp. 217-232.

[31] R. K. Hwang, H. Katayama and M. Gen, “U-Shaped As-sembly Line Balancing Problem with Genetic Algorithm,” International Journal of Production Research, Vol. 46, No. 16, 2008, pp. 4637-4649.

[32] R. K. Hwang and H. Katayama, “A Multi-Decision Genetic Approach for Workload Balancing of Mixed-Model U-Shaped Assembly Line Systems,” International Journal of Production Research, Vol. 47, No. 14, 2009, pp. 3797- 3822.

[33] A. Baykasoglu and T. Dereli, “Simple and U-Type As-sembly Line Balancing by Using an Ant Colony Based Algorithm,” Mathematical and Computational Applica-tions, Vol. 14, No. 1, 2009, pp. 1-12.

[34] G. J. Miltenburg, “Balancing and Scheduling Mixed-Model U-Shaped Production Lines,” International Journal of Flexible Manufacturing Systems, Vol. 14, No. 2, 2002, pp. 119-151.

[35] Y. K. Kim, S. J. Kim and J. Y. Kim, “Balancing and Se-quencing Mixed-Model U-Lines with a Co-Evolutionary Algorithm,” Production Planning and Control, Vol. 11, No. 8, 2000, pp. 754-764.

[36] Y. K. Kim, J. Y. Kim and Y. Kim, “An Endosymbiotic Evolutionary Algorithm for the Integration of Balancing and Sequencing in Mixed-Model U-Lines,” European Journal of Operational Research, Vol. 168, No. 3, 2006, pp. 838-852.

[37] S. Agrawal and M. K. Tiwari, “A Collaborative Ant Col-ony Algorithm to Stochastic Mixed-Model U-Shaped Dis-assembly Line Balancing and Sequencing Problem,” In-ternational Journal of Production Research, Vol. 46, No. 6, 2008, pp. 1405-1429.

[38] I. Sabuncuoglu, E. Erel and A. Alp, “Ant Colony Optimi-zation for the Single Model U-Type Assembly Line Bal-ancing Problem,” International Journal of Production Economics, Vol. 120, No. 2, 2009, pp. 287-300.

[39] Y. Kara, U. Ozcan and A. Peker, “An Approach for Bal-ancing and Sequencing Mixed-Model JIT U-Lines,” In-ternational Journal of Advanced Manufacturing Technol-ogy, Vol. 32, No. 11-12, 2007, pp. 1218-1231.

[40] Y. Kara, “Line Balancing and Model Sequencing to Reduce Work Overload in Mixed-Model U-Line Production Environments,” Engineering Optimization, Vol. 40, No. 7, 2008, pp. 669-684.

[41] A. Konak, D. W. Coit and A. E. Smith, “Multi-Objective Optimization Using Genetic Algorithms: A Tutorial,” Re-liability Engineering & System Safety, Vol. 91, No. 9, 2006, pp. 992-1007.

[42] C. A. C. Coello, D. A. Veldhuizen and G. B. Lamont, “Evolutionary Algorithms for Solving Multi-Objective Problems,” Kluwer Academic Publishers, Dordrecht, 2002.

[43] E. Zitzler and L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, 1999, pp. 257-271.

[44] C. M. Fonseca and P. J. Fleming, “Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization,” Proceedings of 5th International Conference on Genetic Algorithm, Urbana, June 1993, pp. 416- 423.

[45] C. M. Fonseca and P. J. Fleming, “An overview of Evolu-tionary Algorithms in Multiobjective Optimization,” Evo-lutionary Computation, Vol. 3, No. 1, 1995, pp. 1-16.

[46] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA II,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, 2002, pp. 182-197.

[47] D. Corne, M. Dorigo and F. Glover, “New Ideas in Opti-mization,” McGraw-Hill, London, 1999.

[48] W. Wattanapornprom, P. Olanviwitchai, P. Chutima and P. Chongsatitvatana, “Multi-Objective Combinatorial Op-timisation with Coincidence Algorithm,” Proceedings of IEEE Congress on Evolutionary Computation, Norway, 11 February 2009, pp. 1675-1682.

[49] J. R. Jackson, “A Computing Procedure for a Line Bal-ancing Problem,” Management Science, Vol. 2, No. 3, 1956, pp. 261-271.

[50] D. E. Goldberg, “Genetic Algorithms in Search, Optimiza-tion, and Machine Learning,” Addison-Wesley, Boston, 1989.

[51] J. Horn, N. Nafpliotis and D. E. Goldberg, “A Niched Pareto Genetic Algorithm for Multiobjective Optimization,” Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Orlando, 27-29 June 1994.

[52] P. Lacomme, C. Prins and M. Sevaux, “A Genetic Algo-rithm for a Bi-Objective Capacitated ARC Routing Prob-lem,” Computer & Operations Research, Vol. 33, No. 12, 2006, pp. 3473-3493.

[53] P. Chutima and P. Pinkoompee, “An Investigation of Local Searches in Memetic Algorithms for Multi-Objective Sequencing Problems on Mixed-Model Assembly Lines,” Proceedings of Computers and Industrial Engineering, Beijing, 31 October-2 November 2008.

[54] R. Kumar and P. K. Singh, “Pareto Evolutionary Algorithm Hybridized with Local Search for Bi-Objective TSP,” Studies in Computational Intelligence (Hybrid Evolutionary Algorithms), Springer, Berlin/Heidelberg, Vol. 75, 2007, pp. 361-398.

[55] D. C. Montomery, “Design and Analysis of Experiments,” John Wiley & Sons, Inc., Hoboken, 2009.

[56] N. T. Thomopoulos, “Mixed Model Line Balancing with Smoothed Station Assignment,” Management Science, Vol. 14, No. 2, 1970, pp. B59-B75.

[57] A. L. Arcus, “COMSOAL: A Computer Method of Se-quencing Operations for Assembly Lines,” International Journal of Production Research, Vol. 4, No. 4, 1965, pp. 259-277.