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An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing

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Abstract

Remanufacturing is an effective way to realize the reutilization of resources. Disassembly, as an essential step of remanufacturing, is usually finished by manual work which is low efficiency and high labor cost. Robotic disassembly provides an alternative way to reduce labor intensity and disassembly cost. Disassembly line is an efficient method to deal with end-of-life products on a large scale. Balancing the workload of robotic workstations is the main objective of robotic disassembly line balancing problem. In this paper, an improved multi-objective discrete bees algorithm is proposed to solve robotic disassembly line balancing problem. The feasible disassembly sequence is obtained by space interference matrix method. It is used to generate robotic disassembly line solution by robotic workstation assignment method. After that, the multi-objective robotic disassembly line balancing problem is proposed. With the help of efficient non-dominated Pareto sorting method, the improved multi-objective discrete bees algorithm is proposed to find Pareto optimal solutions. Based on a gear pump and a camera, the performance of the improved multi-objective discrete bees algorithm is analyzed under different parameters and compared with the other optimization algorithms. In addition, Pareto fronts of robotic disassembly line balancing problem are also compared with those of the other two cases. The result shows the proposed method can find better solutions using comparable running time compared with the other optimization algorithms.

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References

  1. Tao F, Cheng Y, Zhang L, Nee AYC (2017) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 28(5):1079–1094. https://doi.org/10.1007/s10845-015-1042-8

    Article  Google Scholar 

  2. Wang LH, Wang XV, Gao L, Váncza J (2014) A cloud-based approach for WEEE remanufacturing. CIRP Ann-Manuf Technol 63(1):409–412. https://doi.org/10.1016/j.cirp.2014.03.114

    Article  Google Scholar 

  3. D’Adamo I, Rosa P (2016) Remanufacturing in industry: advices from the field. Int J Adv Manuf Technol 86(9–12):2575–2584. https://doi.org/10.1007/s00170-016-8346-5

    Article  Google Scholar 

  4. Xu BS (2010) State of the art and future development in remanufacturing engineering. Trans Mat Heat T 31(1):10–14

    Google Scholar 

  5. Guide VDR (2000) Production planning and control for remanufacturing: industry practice and research needs. J Oper Manag 18(4):467–483. https://doi.org/10.1016/S0272-6963(00)00034-6

    Article  Google Scholar 

  6. Xu BS (2010) Recent progress of remanufacturing industry and technology in China. Therm Spray Technol 2(3):1–6

    Google Scholar 

  7. Savaskan RC, Bhattacharya S, Van WLN (2004) Closed-loop supply chain models with product remanufacturing. Manag Sci 50(2):239–252. https://doi.org/10.1287/mnsc.1030.0186

    Article  MATH  Google Scholar 

  8. Shakourloo A (2017) A multi-objective stochastic goal programming model for more efficient remanufacturing process. Int J Adv Manuf Technol 91(1–4):1007–1021. https://doi.org/10.1007/s00170-016-9779-6

    Article  Google Scholar 

  9. Priyono A, Ijomah W, Bititci U (2016) Disassembly for remanufacturing: a systematic literature review, new model development and future research needs. J Ind Eng Manag 9(4):899–932. https://doi.org/10.3926/jiem.2053

    Google Scholar 

  10. Alavidoost MH, Zarandi MF, Tarimoradi M, Nemati Y (2017) Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times. J Intell Manuf 28(2):313–336. https://doi.org/10.1007/s10845-014-0978-4

    Article  Google Scholar 

  11. Kim HW, Lee DH (2018) A sample average approximation algorithm for selective disassembly sequencing with abnormal disassembly operations and random operation times. Int J Adv Manuf Technol 1–14. https://doi.org/10.1007/s00170-018-1667-9

  12. Vongbunyong S, Kara S, Pagnucco M (2012) A framework for using cognitive robotics in disassembly automation. In: David AD (ed) Leveraging technology for a sustainable world. Spring, Berkeley, pp 173–178. https://doi.org/10.1007/978-3-642-29069-5_30

    Chapter  Google Scholar 

  13. Vongbunyong S, Kara S, Pagnucco M (2013) Basic behaviour control of the vision-based cognitive robotic disassembly automation. Assem Autom 33(1):38–56. https://doi.org/10.1108/01445151311294694

    Article  Google Scholar 

  14. Vongbunyong S, Kara S, Pagnucco M (2013) Application of cognitive robotics in disassembly of products. CIRP Ann Manuf Technol 62(1):31–34. https://doi.org/10.1016/j.cirp.2013.03.037

    Article  Google Scholar 

  15. Vongbunyong S, Kara S, Pagnucco M (2015) Learning and revision in cognitive robotics disassembly automation. Robot Comput Integr Manuf 34:79–94. https://doi.org/10.1016/j.rcim.2014.11.003

    Article  Google Scholar 

  16. Wang BX, Guan ZL, Ullah S, Xu XH, He ZD (2017) Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm. J Intell Manuf 28(2):419–436. https://doi.org/10.1007/s10845-014-0988-2

    Article  Google Scholar 

  17. Wang LH, Schmidt B, Givehchi M, Adamson G (2015) Robotic assembly planning and control with enhanced adaptability through function blocks. Int J Adv Manuf Technol 77(1–4):705–715. https://doi.org/10.1007/s00170-014-6468-1

    Google Scholar 

  18. Ullah S, Guan ZL, Zhang L, Zhang F, Wang BX, Mirza J (2017) Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line. J Intell Manuf 1–26. https://doi.org/10.1007/s10845-017-1316-4

  19. Gungor A, Gupta SM (1999) Disassembly line balancing. In Proceedings of 1999 annual meeting of the northeast decision sciences institute, Newport, USA, 24–26 March 1999, pp 24–26

  20. Mcgovern SM, Gupta SM (2007) A balancing method and genetic algorithm for disassembly line balancing. Eur J Oper Res 179(3):692–708. https://doi.org/10.1016/j.ejor.2005.03.055

    Article  MATH  Google Scholar 

  21. Ilgin MA, Akçay H, Araz C (2017) Disassembly line balancing using linear physical programming. Int J Prod Res 55(20):1–12. https://doi.org/10.1080/00207543.2017.1324225

    Article  Google Scholar 

  22. Ding LP, Feng YX, Tan JR, Gao YC (2010) A new multi-objective ant colony algorithm for solving the disassembly line balancing problem. Int J Adv Manuf Technol 48(5–8):761–771. https://doi.org/10.1007/s00170-009-2303-5

    Article  Google Scholar 

  23. Ayyuce AK, Turkbey O (2013) Multi-objective optimization of stochastic disassembly line balancing with station paralleling. Comput Ind Eng 65(3):413–425. https://doi.org/10.1016/j.cie.2013.03.014

    Article  Google Scholar 

  24. Tuncel E, Zeid A, Kamarthi S (2014) Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. J Intell Manuf 25(4):647–659. https://doi.org/10.1007/s10845-012-0711-0

    Article  Google Scholar 

  25. Bentaha ML, Battaïa O, Dolgui A (2014) A sample average approximation method for disassembly line balancing problem under uncertainty. Comput Oper Res 51:111–122. https://doi.org/10.1016/j.cor.2014.05.006

    Article  MathSciNet  MATH  Google Scholar 

  26. Hezer S, Kara Y (2015) A network-based shortest route model for parallel disassembly line balancing problem. Int J Prod Res 53(6):1849–1865. https://doi.org/10.1080/00207543.2014.965348

    Article  Google Scholar 

  27. Kalayci CB, Hancilar A, Gungor A, Gupta SM (2015) Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. J Manuf Syst 37:672–682. https://doi.org/10.1016/j.jmsy.2014.11.015

    Article  Google Scholar 

  28. Mete S, Çil ZA, Ağpak K, Özceylan E, Dolgui A (2016) A solution approach based on beam search algorithm for disassembly line balancing problem. J Manuf Syst 41:188–200. https://doi.org/10.1016/j.jmsy.2016.09.002

    Article  Google Scholar 

  29. Kalayci CB, Gupta SM (2013) Ant colony optimization for sequence-dependent disassembly line balancing problem. J Manuf Technol Manage 24(3):413–427. https://doi.org/10.1108/17410381311318909

    Article  Google Scholar 

  30. Kalayci CB, Gupta SM (2013) A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. Int J Adv Manuf Technol 69(1–4):197–209. https://doi.org/10.1007/s00170-013-4990-1

    Article  Google Scholar 

  31. Liu J, Wang S (2017) Balancing disassembly line in product recovery to promote the coordinated development of economy and environment. Sustainability 9(2):309–323. https://doi.org/10.3390/su9020309

    Article  Google Scholar 

  32. Kalayci CB, Gupta SM (2014) A tabu search algorithm for balancing a sequence-dependent disassembly line. Prod Plan Control 25(2):149–160. https://doi.org/10.1080/09537287.2013.782949

    Article  Google Scholar 

  33. Kalayci CB, Polat O, Gupta SM (2016) A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Ann Oper Res 242(2):321–354. https://doi.org/10.1007/s10479-014-1641-3

    Article  MathSciNet  MATH  Google Scholar 

  34. Kalayci CB, Gupta SM (2013) Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Syst Appl 40(18):7231–7241. https://doi.org/10.1016/j.eswa.2013.06.067

    Article  Google Scholar 

  35. Alshibli M, ElSayed A, Kongar E, Sobh TM, Gupta SM (2016) Disassembly sequencing using tabu search. J Intell Robot Syst 82(1):69–79. https://doi.org/10.1007/s10846-015-0289-9

    Article  Google Scholar 

  36. ElSayed A, Kongar E, Gupta SM (2010) A genetic algorithm approach to end-of-life disassembly sequencing for robotic disassembly. In Proceedings of the 2010 northeast decision sciences institute conference, Alexandria, USA, 26–28 March 2010, pp 402–408

  37. ElSayed A, Kongar E, Gupta SM, Sobh T (2011) An online genetic algorithm for automated disassembly sequence generation. In Proceedings of the ASME 2011 international design engineering technical conferences and computers and information in engineering conference, Washington DC, USA, 28–31 August 2011, pp 657–664. https://doi.org/10.1115/DETC2011-48635

  38. ElSayed A, Kongar E, Gupta SM, Sobh T (2012) A robotic-driven disassembly sequence generator for end-of-life electronic products. J Intell Robot Syst 68(1):43–52. https://doi.org/10.1007/s10846-012-9667-8

    Article  Google Scholar 

  39. Agrawal S, Tiwari MK (2008) A collaborative ant colony algorithm to stochastic mixed-model u-shaped disassembly line balancing and sequencing problem. Int J Prod Res 46(6):1405–1429. https://doi.org/10.1080/00207540600943985

    Article  MATH  Google Scholar 

  40. Pham DT, Ghanbarzadeh A (2007) Multi-objective optimisation using the bees algorithm. In Proceedings of the 3rd international virtual conference on intelligent production machines and systems, Cardiff, UK, 3–14 July 2007, pp 529–533

  41. Tapkan P, Özbakır L, Baykasoğlu A (2012) Bees algorithm for constrained fuzzy multi-objective two-sided assembly line balancing problem. Optim Lett 6(6):1–11. https://doi.org/10.1007/s11590-011-0344-9

    Article  MathSciNet  MATH  Google Scholar 

  42. Ercin O, Coban R (2011) Comparison of the artificial bee colony and the bees algorithm for PID controller tuning. In Proceedings of 2011 international symposium on innovations in intelligent systems and applications, Istanbul, Turkey, 15–18 June 2011, pp 595–598. https://doi.org/10.1109/INISTA.2011.5946157

  43. Mastrocinque E, Yuce B, Lambiase A, Packianather MS (2013) A multi-objective optimization for supply chain network using the bees algorithm. Int J Eng Bus Manag 5(38):1–11. https://doi.org/10.5772/56754

    Google Scholar 

  44. Lu W, Quan Z, Liu Q, Zhang D, Xu W (2015) QoE based spectrum allocation optimization using bees algorithm in cognitive radio networks. In Proceedings of 2015 international conference on algorithms and architectures for parallel processing, Zhang Jiajie, China, 18–20 November 2015, pp 327–338. https://doi.org/10.1007/978-3-319-27119-4_23

  45. Xu WJ, Tian SS, Liu Q, Xie YQ, Zhou ZD, Pham DT (2016) An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing. Int J Adv Manuf Technol 84(1–4):17–28. https://doi.org/10.1007/s00170-015-7738-2

    Article  Google Scholar 

  46. Minella G, Ruiz R, Ciavotta M (2008) A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J Comput 20(3):451–471. https://doi.org/10.1287/ijoc.1070.0258

    Article  MathSciNet  MATH  Google Scholar 

  47. Tian GD, Zhou MC, Chu JW, Liu YM (2012) Probability evaluation models of product disassembly cost subject to random removal time and different removal labor cost. IEEE Trans Autom Sci Eng 9(2):288–295. https://doi.org/10.1109/TASE.2011.2176489

    Article  Google Scholar 

  48. Laili YJ, Tao F, Zhang L, Sarker BR (2012) A study of optimal allocation of computing resources in cloud manufacturing systems. Int J Adv Manuf Technol 63(5–8):671–690. https://doi.org/10.1007/s00170-012-3939-0

    Article  Google Scholar 

  49. Guo XW, Liu SX, Zhou MC, Tian GD (2016) Disassembly sequence optimization for large-scale products with multiresource constraints using scatter search and petri nets. IEEE Trans Cybern 46(11):2435–2446. https://doi.org/10.1109/TCYB.2015.2478486

    Article  Google Scholar 

  50. Jin GQ, Li WD, Xia K (2013) Disassembly matrix for liquid crystal displays televisions. Procedia CIRP 11:357–362. https://doi.org/10.1016/j.procir.2013.07.015

    Article  Google Scholar 

  51. Jin GQ, Li WD, Wang S, Gao SM (2015) A systematic selective disassembly approach for waste electrical and electronic equipment with case study on liquid crystal display televisions. Proc Inst Mech Eng B J Eng Manuf 231(13):1–18. https://doi.org/10.1177/0954405415575476

    Google Scholar 

  52. Pham QT, Pham DT, Castellani M (2012) A modified bees algorithm and a statistics-based method for tuning its parameters. Proc Inst Mech Eng I J Syst Control Eng 226(3):287–301. https://doi.org/10.1177/0959651811422759

    Google Scholar 

  53. Zhang XY, Tian Y, Cheng R, Jin YC (2015) An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Trans Evol Comput 19(2):201–213. https://doi.org/10.1109/TEVC.2014.2308305

    Article  Google Scholar 

  54. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  55. Roy PC, Islam MM, Deb K (2016) Best order sort: a new algorithm to non-dominated sorting for evolutionary multi-objective optimization. In Proceedings of 2016 on genetic and evolutionary computation conference companion, Colorado, USA, 20–24 July 2016, pp 1113–1120. https://doi.org/10.1145/2908961.2931684

  56. Igor S (2017) CNC Camera box #ARIADNE. https://grabcad.com/library/cnc-camera-box-ariadne-1. Accessed 14 September 2017

  57. KUKA (2017) KUKA LBR linear axis. https://www.kuka.com/en-de/products/robot-systems/ robot-periphery/linear-units/lbr-linear-axis. Accessed 01 January 2017

  58. Mathworks (2014) R2014b release highlights. https://www.mathworks.com/products/new_products/ release2014b.html. Accessed 01 January 2014

  59. Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da FVG (2003) Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans Evol Comput 7(2):117–132. https://doi.org/10.1109/TEVC.2003.810758

    Article  Google Scholar 

  60. Okabe T, Jin Y, Sendhoff B (2003) A critical survey of performance indices for multi-objective optimisation. In Proceedings of 2003 congress on IEEE on evolutionary computation, Canberra, Australia, 8–12 December 2003, pp 878–885. https://doi.org/10.1109/CEC.2003.1299759

  61. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In Proceedings of 1998 international conference on parallel problem solving from nature, Amsterdam, Netherlands, 27–30 September 1998, pp 292–301. https://doi.org/10.1007/BFb0056872

  62. Beume N, Fonseca CM, López-Ibáñez M, Paquete L, Vahrenhold J (2009) On the complexity of computing the hypervolume indicator. IEEE Trans Evol Comput 13(5):1075–1082. https://doi.org/10.1109/TEVC.2009.2015575

    Article  Google Scholar 

  63. Akbari R, Hedayatzadeh R, Ziarati K, Hassanizadeh B (2012) A multi-objective artificial bee colony algorithm. Swarm Evol Comput 2:39–52. https://doi.org/10.1016/j.swevo.2011.08.001

    Article  Google Scholar 

  64. Yang CL, Kuo RJ, Chien CH, Quyen NTP (2015) Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering. Appl Soft Comput 30:113–122. https://doi.org/10.1016/j.asoc.2015.01.031

    Article  Google Scholar 

  65. Akpınar S, Bayhan GM (2011) A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints. Eng Appl Artif Intell 24(3):449–457. https://doi.org/10.1016/j.engappai.2010.08.006

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51775399 and 51475343), the Keygrant Project of Hubei Technological Innovation Special Fund (Grant No. 2016AAA016), Engineering and Physical Sciences Research Council (EPSRC), UK (Grant No. EP/N018524/1), and the China Scholarship Council (201606950054).

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Correspondence to Junwei Yan.

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Liu, J., Zhou, Z., Pham, D.T. et al. An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing. Int J Adv Manuf Technol 97, 3937–3962 (2018). https://doi.org/10.1007/s00170-018-2183-7

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