Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Incorporating dynamic cellular manufacturing into strategic supply chain design

  • 190 Accesses

  • 4 Citations


For increasing the efficiency of the supply chain (SC), it is necessary to take into account the interactions and relationships between the stages of procurement of raw materials, manufacturing the products, and distributing them. An integrated framework is proposed in this paper for companies interested in meeting the demand for different products in the customer zones by establishing a number of plants and distributors at the candidate sites and in having SC design with reconfiguration capability based on changes in demand and more proper economic opportunities. For this purpose, a geographically distributed cell design is proposed for the selection of the proper location for each of the facilities and the production process of the products. A mixed integer linear programming model is presented here for the integration of the sectors for procurement, production, and distribution of the products in the SC. In light of the NP-hard class of the cell formation problem, a new algorithm titled hybrid genetic ant lion optimization (HGALO) algorithm is presented for finding the optimal or near-optimal solutions. A comparison is also made here between the proposed algorithm and the genetic algorithm (GA) for demonstration of the efficiency of the proposed algorithm. The quality of the solutions generated based on the HGALO algorithm demonstrates the capability and effectiveness of the algorithm in finding high quality solutions.

This is a preview of subscription content, log in to check access.


  1. 1.

    Aalaei A, Davoudpour H (2012) Designing a mathematical model for integrating dynamic cellular manufacturing into supply chain system. AIP Conf Proc 1499:239.

  2. 2.

    Aalaei A, Davoudpour H (2016) Two bounds for integrating the virtual dynamic cellular manufacturing problem into supply chain management. J Ind Manag Optim 12(3):907–930

  3. 3.

    Aalaei A, Davoudpour H (2017) A robust optimization model for cellular manufacturing system into supply chain management. Int J Prod Econ 183:667–679.

  4. 4.

    Aalaei A, Davoudpour H (2016) Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: a case study. Eng Appl Artif Intell 47:3–15.

  5. 5.

    Arkat J, Farahani MH, Hosseini L (2011) Integrating cell formation with cellular layout and operations scheduling. Int J Adv Manuf Technol 61(5–8):637–647

  6. 6.

    Arkat J, Hosseini L, Farahani MH (2011) Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Syst Appl 38(8):9597–9602.

  7. 7.

    Arkat J, Farahani MH, Ahmadizar F (2012) Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling. Int J Comput Integr Manuf 25(7):625–635.

  8. 8.

    Ahkioon S, Bulgak AA, Bektas T (2009) Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur J Oper Res 192(2):414–428.

  9. 9.

    Balakrishnan J, Cheng CHH (2005) Dynamic cellular manufacturing under multiperiod planning horizons. J Manuf Technol Manag 16(5):516–530.

  10. 10.

    Benhalla S, Gharabi A, Olivier C (2011) Multi-plant cellular manufacturing design within a supply chain. J Oper Logist 4(1):II.1–II.17

  11. 11.

    Fahimnia B, Farahani RZ, Marian R, Luong L (2013) A review and critique on integrated production–distribution planning models and techniques. J Manuf Syst 32(1):1–19.

  12. 12.

    Holland JH (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105.

  13. 13.

    Jang YJ, Jang SY, Chang BM, Park J (2002) A combined model of network design and production/distribution planning for a supply network. Comput Ind Eng 43(1–2):263–281.

  14. 14.

    Kazemi A, Zarandi MHF, Husseini SMM (2009) A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach. Int J Adv Manuf Technol 44(1):180–193.

  15. 15.

    Ma Y, Yan F, Kang K, Wei X (2016) A novel integrated production-distribution planning model with conflict and coordination in a supply chain network. Knowl-Based Syst 105(1):119–133.

  16. 16.

    Melo MT, Nickel S, Saldanha-da-Gama F (2005) Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Comput Oper Res 33(1):181–208

  17. 17.

    Melo MT, Nickel S, Saldanha-da-Gama F (2009) Facility location and supply chain management: a review. Eur J Oper Res 196(2):401–412.

  18. 18.

    Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98.

  19. 19.

    Niknamfar AH, Niaki STA, Pasandideh SHR (2015) Robust optimization approach for an aggregate production–distribution planning in a three-level supply chain. Int J Adv Manuf Technol 76(1):623–634.

  20. 20.

    Rao P, Mohanty R (2003) Impact of cellular manufacturing on supply chain management: exploration of interrelationships between design issues. Int J Adv Manuf Technol Manag 5(5–6):507–520

  21. 21.

    Paydar MM, Saidi-Mehrabad M (2015) Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. Int J Comput Integr Manuf 28(3):251–265.

  22. 22.

    Sadigh AN, Fallah H, Nahavandi N (2013) A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions. Int J Adv Manuf Technol 69(1):225–235.

  23. 23.

    Sankaran S, Kasilingam RG (1990) An integrated approach to cell formation and part routing in group technology manufacturing systems. Eng Optim 16(3):235–245.

  24. 24.

    Saxena LK, Jain PK (2012) An integrated model of dynamic cellular manufacturing and supply chain system design. Int J Adv Manuf Technol 62(1):385–404.

  25. 25.

    Schaller J (2008) Incorporating cellular manufacturing into supply chain design. Int J Prod Res 46(17):4925–4945.

  26. 26.

    Soolaki M, Arkat J (2018) Supply chain design considering cellular structure and alternative processing routings. J Ind Syst Eng 11(1). Winter

  27. 27.

    Vaart TVD, Donk PDV (2008) A critical review of survey–based research in supply chain integration. Int J Prod Econ 111(1):42–55.

  28. 28.

    Vokurkar JR, Zank GM, Lund CM (2002) Improving competitiveness through supply chain management: a cumulative improving approach. CR 12(1):14–25

  29. 29.

    Wemmerlov U, Nancy LH (1989) Cellular manufacturing in the US industry: a survey of users. Int J Prod Res 27(9):1511–1530.

  30. 30.

    Wilhelm W, Liang D, Rao B, Warrier D, Zhu X, Bulusu S (2005) Design of international assembly systems and their supply chains under NAFTA. Transport Res Part E: Log Transport Rev 41(6):467–493.

  31. 31.

    Zhang LL, Lee C, Zhang S (2016) An integrated model for strategic supply chain design: formulation and ABC-based solution approach. Expert Syst Appl 52(15):39–49.

Download references

Author information

Correspondence to Jamal Arkat.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Soolaki, M., Arkat, J. Incorporating dynamic cellular manufacturing into strategic supply chain design. Int J Adv Manuf Technol 95, 2429–2447 (2018).

Download citation


  • Procurement-production-distribution
  • Supply chain design
  • Cellular manufacturing system
  • Hybrid genetic ant lion optimization