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A Mathematical Model and an Artificial Bee Colony Algorithm for In-Plant Milk-Run Design

  • Kadir Buyukozkan
  • Sule Itir SatogluEmail author
Conference paper
  • 22 Downloads
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)

Abstract

As a result of the product diversification, many types of components are used in the products’ bill-of-materials. Consequently, smaller quantities of many different types of components are needed to be distributed. All these factors complicated the part-feeding to the assembly lines. In this study, a mathematical model is developed for an in-plant milk-run material supply system that periodically distributes multiple parts by using multiple vehicles to the stations of the assembly lines. This model is called the Multi-Vehicle Milk-Run Model. As the proposed mathematical model is NP-hard, an Artificial Bee Colony Algorithm is developed to solve the large instances. The proposed ABC Algorithm is tested based on the optimum solutions (where available) and the best-known feasible solutions of different sized instances of a real washing machine assembly plant. Hence, the performance of the ABC Algorithm is validated.

Keywords

In-plant milk-run Part feeding Artificial Bee Colony Algorithm Mathematical model 

Notes

Acknowledgment

This study has been financially supported by the Turkish National Science Foundation (TUBITAK), through the 215M143 research project.

References

  1. Baudin M (2004) Lean logistics: the nuts and bolts of delivering materials and goods. Productivity Press, New YorkGoogle Scholar
  2. Boschetti MA, Maniezzo V, Roffilli M, Röhler AB (2009) Matheuristics: optimization, simulation and control. In: International workshop on hybrid metaheuristics. Springer, Heidelberg, pp 171–177CrossRefGoogle Scholar
  3. Buyukozkan K, Bal A, Oksuz MK, Kapukaya EN, Satoglu SI (2019) A mathematical model and a matheuristic for in-plant milk-run systems design and application in white goods industry. In: Calisir F, Cevikcan E, Camgoz Akdag H (eds) Industrial engineering in the big data era. Springer, Cham, pp 99–112CrossRefGoogle Scholar
  4. Buyukozkan K, Kucukkoc I, Satoglu SI, Zhang DZ (2016) Lexicographic bottleneck mixed-model assembly line balancing problem: artificial bee colony and tabu search approaches with optimised parameters. Expert Syst Appl 50:151–166CrossRefGoogle Scholar
  5. Caputo AC, Pelagagge PM, Salini P (2015) Planning models for continuous supply of parts in assembly systems. Assembly Autom 35(1):35–46CrossRefGoogle Scholar
  6. Emde S, Schneider M (2018) Just-in-time vehicle routing for in-house part feeding to assembly lines. Transp. Sci. 52:657–672CrossRefGoogle Scholar
  7. Emde S, Gendreau M (2017) Scheduling in-house transport vehicles to feed parts to automotive assembly lines. Eur J Oper Res 260(1):255–267MathSciNetCrossRefGoogle Scholar
  8. Emde S, Boysen N (2012) Optimally routing and scheduling tow trains for JIT-supply of mixed-model assembly lines. Eur J Oper Res 217:287–299MathSciNetzbMATHGoogle Scholar
  9. Fathi M, Rodríguez V, Fontes DBMM, Alvarez MJ (2015) A modified particle swarm optimization algorithm to solve the part feeding problem at assembly lines. Int J Prod Res 54(3):878–893CrossRefGoogle Scholar
  10. Golz J, Gujjula R, Günther HO, Rinderer S (2012) Part feeding at high-variant mixed-model assembly lines. Flex Serv Manufact J 24:119–141CrossRefGoogle Scholar
  11. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  12. Kilic HS, Durmusoglu MB (2013) A mathematical model and a heuristic approach for periodic material delivery in lean production environment. Int J Adv Manuf Technol 69(5–8):977–992CrossRefGoogle Scholar
  13. Limere V, Landeghem HV, Goetschalckx M, Aghezzaf EH, McGinnis LF (2012) Optimizing part feeding in the automotive assembly industry: deciding between kitting and line stocking. Int J Prod Res 50(15):4046–4060CrossRefGoogle Scholar
  14. Limere V, Van Landeghem H, Goetschalckx M (2015) A decision model for kitting and line stocking with variable operator walking distances. Assembly Autom 35(1):47–56CrossRefGoogle Scholar
  15. Osman IH, Kelly JP (1996) Meta-heuristics: an overview. In: Osman IH, Kelly JP (eds) Meta-heuristics. Springer, Boston, pp 1–21CrossRefGoogle Scholar
  16. Sali M, Sahin E, Patchong A (2015) An empirical assessment of the performances of three line-feeding modes used in the automotive sector: line stocking vs. kitting vs. sequencing. Int J Prod Res 53(5):1439–1459CrossRefGoogle Scholar
  17. Sali M, Sahin E (2016) Line feeding optimization for just in time assembly lines: an application to the automotive industry. Int J Prod Econ 174:54–67CrossRefGoogle Scholar
  18. Satoglu SI, Ucan K (2015) Redesigning the material supply system of the automotive suppliers based on lean principles and an application. In: 2015 international conference on industrial engineering and operations management (IEOM). IEEE, pp 1–6Google Scholar
  19. Satoglu SI, Sahin IE (2013) Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry. Int J Adv Manuf Technol 65:319–332CrossRefGoogle Scholar
  20. Volling T, Grunewald M, Spengler TS (2013) An integrated inventory-transportation system with periodic pick-ups and leveled replenishment. Bus Res 6(2):173–194CrossRefGoogle Scholar
  21. Zhou B, Peng T (2017) Scheduling the in-house logistics distribution for automotive assembly lines with just-in-time principles. Assembly Autom 37(1):51–63CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Industrial Engineering Department, Faculty of ManagementIstanbul Technical UniversityIstanbulTurkey
  2. 2.Industrial Engineering Department, Faculty of EngineeringKaradeniz Technical UniversityTrabzonTurkey

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