Abstract
In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificial bee colony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Simaria AS, Vilarinho PM (2004) A genetic algorithm based approach to the mixed model assembly line balancing problem of type II. Comput Ind Eng 47:391–407
Celano G, Costa A, Fichera S (2004) A comparative analysis of sequencing heuristics for solving the Toyota goal chasing problem. Robot Comput Integr Manuf 20:573–581
Mansouri SA (2005) A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. Eur J Oper Res 167:696–716
Al-e-hashem SMJM, Aryanezhad MB, Jabbarzadeh A (2011) A new approach to solve a mixed-model assembly line with a bypass sub line sequencing problem. Int J Adv Manuf Technol 52:1053–1066
Dar-El EM, Nadivi A (1981) A mixed-model sequencing application. Int J Prod Res 19:69–84
Kim YK, Hyun CJ, Kim Y (1996) Sequencing in mixed model assembly lines: a genetic algorithm approach. Comput Oper Res 23:1131–1145
Karabati S, Sayin S (2003) Assembly line balancing in a mixed-model sequencing environment with synchronous transfers. Eur J Oper Res 149(2):417–429
Kim YK, Kim JY, Kim Y (2000) A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Appl Intell 13:247–258
Kim YK, Kim SJ, Kim JY (2000) Balancing and sequencing mixed-model U-lines with a co-evolutionary algorithm. Prod Plan Control 11:754–764
Sawik T (2000) Simultaneous vs. sequential loading and scheduling of flexible assembly systems. Int J Prod Res 38:3267–3282
Sawik T (2002) Monolithic vs. hierarchical balancing and scheduling of a flexible assembly line. Eur J Oper Res 143:115–124
Sawik T (2004) Loading and scheduling of a flexible assembly system by mixed integer programming. Eur J Oper Res 154:1–19
Kim YK, Kim JY, Kim Y (2006) An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model U-lines. Eur J Oper Res 168:838–852
Mosadegh HM, Zandieh SMT, Ghomi F (2012) Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines. Appl Soft Comput 12:1359–1370
Ozcan U, Cercioglu H, Gokcen H, Toklu B (2010) Balancing and sequencing of parallel mixed-model assembly lines. Int J Prod Res 48(17):5089–5113
Hwang R, Katayama H (2010) Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach. Int J Prod Res 48:6417–6441
Öztürk C, Tunali S, Hnich B, Örnek MA (2013) Balancing and scheduling of flexible mixed model assembly lines with parallel stations. Int J Adv Manuf Technol 67:2577–2591
Öztürk C, Tunali S, Hnich B, Örnek MA (2013) Balancing and scheduling of flexible mixed model assembly lines. Constraints 18(3):434–469
Merengo C, Nava F, Pozzetti A (1999) Balancing and sequencing manual mixed-model assembly lines. Int J Prod Res 37(12):2835–2860
Yang C, Gao J, Sun L (2013) A multi-objective genetic algorithm for mixed-model assembly line rebalancing. Comput Ind Eng 65(1):109–116
Wu EF, Jin J, Bao JS, Hu XF (2008) A branch-and-bound algorithm for two-sided assembly line balancing. Int J Adv Manuf Technol 39(9–10):1009–1015
Miltenburg J (2002) Balancing and scheduling mixed-model U-shaped production lines. Int J Flex Manuf Syst 14:119–151
Kara Y (2008) Line balancing and model sequencing to reduce work overload in mixed-model U-line production environments. Eng Optim 40(7):669–684
Wang, Y., Dang, C., Li, H., Han, L., Wei, J (2009) A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design. In: Proceeding of IEEE congress on evolutionary computation, Trondheim, Norway, 18–21 May, 2009, 2927–2933
Liu, M., Zou, X., Chen, Y., Wu, Z (2009) Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances. In: Proceeding of IEEE congress on evolutionary computation, Trondheim, Norway, 18–21 May, 2009, 2913–2918
Kukkonen, S., Lampinen, J (2009) Performance assessment of generalized differential evolution with a given set of constrained multi-objective test problems. In: Proceeding of IEEE congress on evolutionary computation, Trondheim, Norway, 18–21 May, 2009, 1943–1950
Chen, C.M., Chen, Y., Zhang, Q (2009) Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization. In: Proceeding of congress on evolutionary computation, Trondheim, Norway, 18–21 May, 2009, 209–216
Guo ZX, Wong WK, Li Z, Ren P (2013) Modeling and Pareto optimization of multi objective order scheduling problems in production planning. Comput Ind Eng 64:972–986
Guo ZX, Wong WK, Leung SYS (2013) A hybrid intelligent model for order allocation planning in make to order manufacturing. Applied Soft Computing 13:1376–1390
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–97
Baykasoglu A, Dereli T (2008) Two-sided assembly line balancing using an ant-colony-based heuristic. Int J Adv Manuf Technol 36(5–6):582–588
Agrawal S, Dashora Y, Tiwari MK, Son YJ (2008) Interactive particle swarm: a Pareto-adaptive metaheuristic to multiobjective optimization. IEEE Trans Syst Man Cybern A 38(2):258–277
Coello Coello CA, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report TR06, Computer Engineering Department. Erciyes University, Turkey
Karoboga and Gorkemli, B (2008) A combinatorial artificial bee colony bee algorithm for traveling salesman problem. 2011 international symposium on innovations in intelligent systems and applications (INISTA)
Omkar S, Senthilnath N, Khandelwal JR, Narayana Naik G, Gopalakrishnan S (2011) Artificial bee colony (ABC) for multi-objective design optimization of composite structures. Appl Soft Comput 11(1):489–499
Akbari R, Hedayatzadeh R, Ziarati K, Hassanizadeh B (2012) A multi-objective artificial bee colony algorithm. Swarm Evol Comput 2:39–52
Tapkan P, Ozbakir L, Baykasoglu L (2012) Modeling and solving constrained two aided assembly line balancing problem via bee algorithms. Appl Soft Comput 12(1):3343–3355
Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci 181(12):2455–2468
Li JQ, Pan QK, Gao KZ (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol 55:1159–1169
Zhang R, Song S, Wu C (2013) A hybrid artificial bee colony algorithm for job shop scheduling problem. Int J Prod Res 141(1):167–178
Wang L, Zhou G, Xu Y, Wang S (2012) An effective artificial bee colony algorithm for flexible job shop scheduling problem. Int J Adv Manuf Technol 60:303–315
Wang L, Zhou G, Xu Y, Liu M (2012) An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int J Adv Manuf Technol 60:1111–1123
Tasgetiren MF, Pan QK, Suganthan PN, Chen AH-L (2011) A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Inf Sci 181(16):3459–3475
Kalayci CB, Gupta SM (2013) Artificial bee colony algorithm for solving sequence dependent disassembly line balancing problem. Expert Syst Appl 40(18):7231–7241
Vollmer D. T., Soule T., and Manic M (2010) A distance measure comparison to improve crowding in multi-modal optimization problems. In Proceedings of the 3rd international symposium on resilient control systems (ISRCS ’10), Idaho Fall, USA, Aug 10–12, 1020, 31–36
Scholl, A (1993) Data of assembly line balancing problems. Schriften zur quantitativen betriebswirtschaftslehre 16/93, TU Darmstadt
Scholl A (1999) Balancing and sequencing assembly lines, 2nd edn. Physica, Heidelberg
Coello CAC, Cortes NC (2005) Solving multi objective optimization problems using an artificial immune system. Genet Program Evolvable Mach 6:163–190
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. J Evol Comput 8(2):173–195
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Saif, U., Guan, Z., Liu, W. et al. Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line. Int J Adv Manuf Technol 75, 1809–1827 (2014). https://doi.org/10.1007/s00170-014-6153-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-014-6153-4