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A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines

College of Mechanical Engineering, Donghua University, 2999 North Renmin Road, Shanghai, China

Special Issues: Optimization methods in Intelligent Manufacturing

This research proposes a genetic regulatory network based sequencing method that minimizes multiple objectives including utility work costs, production rate variation costs and setup costs in mixed-model assembly lines. After constructing mathematical model of this multi-objective sequencing problem, the proposed method generates a set of genes to represent the decision variables and develops a gene regulation equation to describe decision variable interactions composed of production constraints and some validated sequencing rules. Moreover, a gene expression procedure that determines each gene’s expression state based on the gene regulation equation is designed. This enables the generation of a series of problem solutions by indicating decision variable values with related gene expression states, and realizes the minimization of weighted sum of multiple objectives by applying a regulatory parameter optimization mechanism in regulation equations. The proposed genetic regulatory network based sequencing method is validated through a series of comparative experiments, and the results demonstrate its effectiveness over other methods in terms of solution quality, especially for industrial instances collected from a diesel engine assembly line.
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1. J. Bautista, C. Batalla-García and R. Alfaro-Pozo, Models for assembly line balancing by temporal, spatial and ergonomic risk attributes, Eur. J. Oper. Res., 251 (2016), 814–829.

2. Y. Delice, E. K. Aydoğan and U. Özcan, A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing, J. Intell. Manuf., 28 (2017), 23–36.

3. H. Mosadegh, S. M. T. Fatemi Ghomi and G. A. Süer, A control theoretical modelling for velocity tuning of the conveyor belt in a dynamic mixed-model assembly line, Int. J. Prod. Res., 55 (2017), 7473–7495.

4. Z. Li, M. N. Janardhanan and Q. Tang, Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line, Eng. Optimiz., 50 (2018), 877–893.

5. U. Saif, Z. Guan and L. Zhang, Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line, J. Intell. Manuf., (2017) 1–26.

6. N. Boysen, M. Fliedner and A. Scholl, Production planning of mixed-model assembly lines: Overview and extensions, Prod. Plan. Control., 20 (2009), 455–471.

7. K. Lian, C. Zhang and L. Gao, et al., A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem, Int. J. Prod. Res., 50 (2012), 5117–5131.

8. N. Boysen, M. Fliedner and A. Scholl, Sequencing mixed-model assembly lines: Survey, classification and model critique, Eur. J. Oper. Res., 192 (2009), 349–373.

9. U. Golle, F. Rothlauf and N. Boysen, Car sequencing versus mixed-model sequencing: A computational study, Eur. J. Oper. Res., 237 (2014), 50–61.

10. P. Chutima and S. Olarnviwatchai, A multi-objective car sequencing problem on two-sided assembly lines, J. Intell. Manuf., 29 (2018), 1617–1636.

11. J. Pereira and M. Vilà, An exact algorithm for the mixed-model level scheduling problem, Int. J. Prod. Res., 53 (2015), 5809–5825.

12. J. Bautista, R. Alfaro-Pozo and C. Batalla-García, Consideration of human resources in the mixed-model sequencing problem with work overload minimization: Legal provisions and productivity improvement, Expert. Syst. Appl., 42 (2015), 8896–8910.

13. J. Bautista and A. Cano, Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimisation and interruption rules, Eur. J. Oper. Res., 210 (2011), 495–513.

14. S. Zhang, D. Yu and X, Shao, et al., A novel artificial ecological niche optimization algorithm for car sequencing problem considering energy consumption, P. I. Mech. Eng. B-J. Eng., 229 (2015), 546–562.

15. S. A. Mansouri, A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines, Eur. J. Oper. Res., 167 (2005), 696–716.

16. P. R. McMullen and G. V. Frazier, A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line, IIE. Trans., 32 (2000), 679–686.

17. P. R. McMullen, A Kohonen self-organizing map approach to addressing a multiple objective, mixed-model JIT sequencing problem, Int. J. Prod. Econ., 72 (2001), 59–71.

18. O. S. Akgündüz and S. Tunalı, An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem, Int. J. Prod. Res., 48 (2010), 5157–5179.

19. F. Y. Ding, J. Zhu and H. Sun,Comparing two weighted approaches for sequencing mixed-model assembly lines with multiple objectives, Int. J. Prod. Econ., 102 (2006), 108–131.

20. C. J. Hyun, Y. Kim and Y. K. Kim, A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines, Comput. Oper. Res., 25 (1998), 675–690.

21. R. Tavakkoli-Moghaddam and A. R. Rahimi-Vahed, Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system, Appl. Math. Comput., 181 (2006), 1471–1481.

22. P. Chutima and W. Naruemitwong, A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect, Comput. Ind. Eng., 69 (2014), 89–104.

23. J. L. Liu, L. L. Wei and X. P. Xie, et al., Quantized stabilization for T–S fuzzy systems with hybrid-triggered mechanism and stochastic cyber-attacks, IEEE T. Fuzzy. Syst., 26 (2018), 3820–3834.

24. J. L. Liu, Y. Y. Gu and X. P. Xie, et al., Hybrid-driven-based h∞ control for networked cascade control systems with actuator saturations and stochastic cyber attacks, IEEE T. Syst. Man. Cy-S., (2018), 1–12.

25. S. S. Kara and S. Onut, A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling, Expert. Syst. Appl., 37 (2010), 6129–6137.

26. J. B. Sheu and C. Pan, A method for designing centralized emergency supply network to respond to large-scale natural disasters, Trans. Res. B-Meth., 67 (2014), 284–305.

27. B. Jesse and G. Marian, Critical transitions in a model of a genetic regulatory system, Math. Biosci. Eng., 11 (2014), 723–740.

28. J. Qiu, K. Sun and C. Yang, et al., Finite-time stability of genetic regulatory networks with impulsive effects, Neurocomputing 219 (2017), 9–14.

29. C. Y. William, E. R. Adrian and Y. Y. Ka, A posterior probability approach for gene regulatory network inference in genetic perturbation data, Math. Biosci. Eng., 13 (2016), 1241–1251.

30. J. Cano-Belmán, R. Z. Ríos-Mercado and J. Bautista, A scatter search based hyper-heuristic for sequencing a mixed-model assembly line, J. Heuristics., 16 (2010), 749–770.

31. Q. Zhu and J. Zhang, Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line, Int. J. Prod. Res., 49 (2011), 4605–4626.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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