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A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem


In recent years, part feeding at assembly lines has become a critical issue as the result of a high level of product customization. The assembly line part feeding problem is a complex problem in which a number of decisions should be made in order to select the right quantity of each part to be supplied at the right time under a set of constraints. This study aims to cope with the part feeding problem at assembly lines by introducing a new memetic ant colony optimization-based heuristic algorithm. Due to the novelty of the problem and in order to evaluate the performance of the proposed memetic algorithm, a mathematical formulation is also presented. Case data from an automobile manufacturer and a set of generated instances are used to test both the mathematical model and the proposed memetic algorithm. The results reveal that although it is computationally difficult to solve the problem using the exact mathematical model, the mimetic algorithm was able to find sufficiently good solutions in a short period of time.


  1. 1.

    Golz J, Gujjula R, Günther HO, Rinderer S, Ziegler M (2012) Part feeding at high-variant mixed-model assembly lines. Flex Serv Manuf J 24(2):119–141

  2. 2.

    Battini D, Boysen N, Emde S (2013) Just-in-time supermarkets for part supply in the automobile industry. J Manag Cont 24(2):209–217

  3. 3.

    Bozer YA, McGinnis LF (1992) Kitting versus line stocking: a conceptual framework and a descriptive model. Int J Prod Econ 28(1):1–19

  4. 4.

    Brynzér H, Johansson MI (1995) Design and performance of kitting and order picking systems. Int J Prod Econ 41(1–3):115–125

  5. 5.

    Hua SY, Johnson DJ (2010) Research issues on factors influencing the choice of kitting versus line stocking. Int J Prod Res 48(3):779–800

  6. 6.

    Limère V, Van Landeghem H, Goetschalckx M, Aghezzaf EH, McGinnis LF (2012) Optimising part feeding in the automotive assembly industry: deciding between kitting and line stocking. Int J Prod Res 50(15):4046–4060

  7. 7.

    Wäscher G, Haußner H, Schumann H (2007) An improved typology of cutting and packing problems. Eur J Oper Res 183(3):1109–1130

  8. 8.

    Eilon S, Christofides N (1971) The loading problem. Manag Sci 17(5):259–268

  9. 9.

    Parreño F, Alvarez-Valdés R, Oliveira JF, Tamarit JM (2010) A hybrid GRASP/VND algorithm for two and three-dimensional bin packing. Ann Oper Res 179(1):203–220

  10. 10.

    Florian M, Lenstra JK, Rinnooy Kan AHG (1980) Deterministic production planning: algorithms and complexity. Manag Sci 26(7):669–679

  11. 11.

    De Souza MC, De Carvalho CRV, Brizon WB (2008) Packing items to feed assembly lines. Eur J Oper Res 184(2):480–489

  12. 12.

    Boysen N, Bock S (2011) Scheduling just-in-time part supply for mixed-model assembly lines. Eur J Oper Res 211(1):15–25

  13. 13.

    Choi W, Lee Y (2002) A dynamic part-feeding system for an automotive assembly line. Comp Indu Eng 43(1–2):123–134

  14. 14.

    Emde S, Fliedner M, Boysen N (2012) Optimally loading tow trains for JIT-supply of mixed-model assembly lines. IIE Trans 44(2):121–135

  15. 15.

    Emde S, Boysen N (2012) Optimally routing and scheduling tow trains for JIT-supply of mixed-model assembly lines. Eur J Oper Res 217(2):287–299

  16. 16.

    Kozan E (2000) An integrated material handling system for a truck assembly plant. J Oper Res Soc 51(3):263–271

  17. 17.

    Rao YQ, Wang MC, Wang KP, Wu TM (2013) Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line. Comput Oper Res 40(11):2599–2610

  18. 18.

    Petalas YG, Parsopoulos KE, Vrahatis MN (2007) Memetic particle swarm optimization. Ann Oper Res 156(1):99–127

  19. 19.

    Gallardo JE, Cotta C, Ferandez AJ (2007) On the hybridization of memetic algorithms with branch-and-bound techniques. IEEE Trans Syst Man Cybern B 37(1):77–83

  20. 20.

    Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11(10):957–971

  21. 21.

    Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE. Trans Evol Comp 7(2):204–223

  22. 22.

    Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern 37(1):18–27

  23. 23.

    Chica M, Cordón O, Damas S, Bautista J (2012) Multiobjective memetic algorithms for time and space assembly line balancing. Eng Appl Artif Intell 25(2):254–273

  24. 24.

    Paplinski JP (2011) The memetic ant colony optimization with directional derivatives simplex algorithm for time delays identification. Lecture Notes in Computer Science (LNCS), vol 6922, pp 183–192

  25. 25.

    Duan H, Yu X (2007) Hybrid ant colony optimization using memetic algorithm for traveling salesman problem. Proceeding of the IEEE symposium on Approximate Dynamic Programming and Reinforcement Learning 368174:92–95

  26. 26.

    Hongfeng W, Ilkyeong M, Shengxiang Y, Dingwei W (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Inf Sci 197(1):38–52

  27. 27.

    Hongfeng W, Shengxiang Y, Ip WH, Dingwei W (2012) A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems. Int J Syst Sci 43(7):1268–1283

  28. 28.

    Yanga J, Suna L, Leeb H, Qiand Y, Liang Y (2008) Clonal selection based memetic algorithm for job shop scheduling problems. J Bio Eng 5:111–119

  29. 29.

    Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. Proceedings of European Conference on Artificial Life, In, pp 134–142

  30. 30.

    Dorigo M, Gianni DC (1999) Ant colony optimization: a new meta-heuristic. Proc Cong Evol Comp 782657:1470–1477

  31. 31.

    Yagmahan B (2011) Mixed-model assembly line balancing using a multi-objective ant colony optimization approach. Expert Syst Appl 38(10):12453–12461

  32. 32.

    Dorigo M, Blum C (2005) Ant colony optimization theory. Theor Comput Sci 344(2–3):243–278

  33. 33.

    Fattahi P, Roshani A, Roshani A (2011) A mathematical model and ant colony algorithm for multi-manned assembly line balancing problem. Int J Adv Manuf Technol 53(1):363–378

  34. 34.

    Akpinar S, Bayhan GM, Baykasoglu A (2013) Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Appl Soft Comput 13(1):574–589

  35. 35.

    Sabuncuoglu I, Erel E, Alp A (2009) Ant colony optimization for the single model U-type assembly line balancing problem. Int J Prod Econ 120(2):287–300

  36. 36.

    Barcos L, Rodriguez V, Alvarez MJ, Robusté F (2010) Routing design for less-than-truckload motor carriers using ant colony optimization. Transp Rev 46(3):367–383

  37. 37.

    Cheng CB, Mao CP (2007) A modified ant colony system for solving the travelling salesman problem with time windows. Math Comput Model 46(9–10):1225–1235

  38. 38.

    Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Comp Indu Eng 59(1):166–180

  39. 39.

    Lin BMT, Lu CY, Shyu SJ, Tsai CY (2008) Development of new features of ant colony optimization for flowshop scheduling. Int J Prod Econ 112(2):742–755

  40. 40.

    Gajpal Y, Rajendran C (2006) An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops. Int J Prod Econ 101(2):259–272

  41. 41.

    Helgeson WB, Birnie DP (1961) Assembly line balancing using the ranked positional weight technique. J Ind Eng 12(6):394–398

  42. 42.

    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

  43. 43.

    Khaw CLE, Ponnambalam SG (2009) Multi-rule multi-objective ant colony optimization for straight and U-type assembly line balancing problem. Proc IEEE Int Conf Autom Sci Eng CASE 5234122:177–182

  44. 44.

    Ponnambalam SG, Aravindan P, Mogileeswar Naidu G (2000) A multi-objective genetic algorithm for solving assembly line balancing problem. Int J Adv Manuf Technol 16(5):341–352

  45. 45.

    Haq AN, Rengarajan K, Jayaprakash J (2006) A hybrid genetic algorithm approach to mixed-model assembly line balancing. Int J Adv Manuf Technol 28(3–4):337–341

  46. 46.

    Baykasoglu A (2006) Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. J Intell Manuf 17(2):217–232

  47. 47.

    Rachamadugu R, Talbot B (1991) Improving the equality of workload assignments in assembly lines. Int J Prod Res 29(3):619–633

  48. 48.

    Liu SB, Ong HL, Huang HC (2003) Two bi-directional heuristics for the assembly line type II problem. Int J Adv Manuf Technol 22(9):656–661

  49. 49.

    Emde S, Boysen N (2012) Optimally locating in-house logistics areas to facilitate JIT-supply of mixed-model assembly lines. Int J Prod Econ 135(1):393–402

  50. 50.

    Kang JH, Kim YD (2010) Coordination of inventory and transportation managements in a two-level supply chain. Int J Prod Econ 123(1):137–145

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Correspondence to Masood Fathi.

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Fathi, M., Rodríguez, V. & Alvarez, M.J. A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem. Int J Adv Manuf Technol 75, 629–643 (2014).

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  • Part feeding
  • Assembly line
  • Mathematical model
  • Memetic algorithm
  • Ant colony optimization