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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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Keywords genetic regulatory network; multiple objectives; sequencing problem; mixed-model assembly line; differential equation; gene regulation

Citation: Youlong Lv, Jie Zhang. A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines. Mathematical Biosciences and Engineering, 2019, 16(3): 1228-1243. doi: 10.3934/mbe.2019059

References

  • 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.

 

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