Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 6, November-December 2021
Page(s) 3575 - 3602
DOI https://doi.org/10.1051/ro/2021138
Published online 06 December 2021
  • A. Aalaei and H. Davoudpour, Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: a case study. Eng. App. Artif. Intell. 47 (2016) 3–15. [Google Scholar]
  • A. Aalaei and H. Davoudpour, A robust optimization model for cellular manufacturing system into supply chain management. Int. J. Prod. Econ. 183 (2017) 667–679. [Google Scholar]
  • A. Ahi, M.B. Aryanezhad, B. Ashtiani and A. Makui, A novel approach to determine cell formation, intracellular machine layout and cell layout in the CMS problem based on TOPSIS method. Comput. Oper. Res. 36 (2009) 1478–1496. [Google Scholar]
  • J. Arkat, M.H. Farahani and L. Hosseini, Integrating cell formation with cellular layout and operations scheduling. Int. J. Adv. Manuf. Technol. 61 (2012) 637–647. [Google Scholar]
  • J. Arkat, M.H. Farahani and F. Ahmadizar, Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling. Int. J. Comput. Integr. Manuf. 25 (2012) 625–635. [Google Scholar]
  • A. Ballakur, An investigation of part family/machine group formation in designing a cellular manufacturing system. Ph.D. thesis. University of Wisconsin (1985). [Google Scholar]
  • M.V. Batsyn, E.K. Batsyna and I.S. Bychkov, NP-completeness of cell formation problem with grouping efficacy objective. Int. J. Prod. Res. 58 (2020) 6159–6169. [Google Scholar]
  • S. Benhalla, A. Gharbi and C. Olivier, Multi-plant cellular manufacturing design within a supply chain. J. Oper. Logistics 4 (2011) II.1–II.17. [Google Scholar]
  • M. Boulif and K. Atif, A new branch&bound-enhanced genetic algorithm for the manufacturing cell formation problem. Comput. Oper. Res. 33 (2006) 2219–2245. [Google Scholar]
  • J. Chai, J.N. Liu and E.W. Ngai, Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst. App. 40 (2013) 3872–3885. [Google Scholar]
  • S. Chopra and P. Meindl, Supply chain management: strategy, planning & operation. In: Das summa summarum des management. Springer (2007) 265–275. [Google Scholar]
  • S. Croom, P. Romano and M. Giannakis, Supply chain management: an analytical framework for critical literature review. Eur. J. Purchasing Supply Manage. 6 (2000) 67–83. [Google Scholar]
  • D. Deliktas, O. Torkul and O. Ustun, A flexible job shop cell scheduling with sequence-dependent family setup times and intercellular transportation times using conic scalarization method. Int. Trans. Oper. Res. 26 (2019) 2410–2431. [Google Scholar]
  • G. Egilmez, E.M. Mese, B. Erenay and G.A. Süer, Group scheduling in a cellular manufacturing shop to minimise total tardiness and nT: a comparative genetic algorithm and mathematical modelling approach. Int. J. Serv. Oper. Manage. 24 (2016) 125–146. [Google Scholar]
  • I. Eguia, J. Racero, F. Guerrero and S. Lozano, Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search. Simulation 89 (2013) 1056–1072. [Google Scholar]
  • I. Erozan, O. Torkul and O. Ustun, Proposal of a nonlinear multi-objective genetic algorithm using conic scalarization to the design of cellular manufacturing systems. Flexible Serv. Manuf. J. 27 (2015) 30–57. [Google Scholar]
  • S.A. Fahmy, Mixed integer linear programming model for integrating cell formation, group layout and group scheduling. In: 2015 IEEE International Conference on Industrial Technology (ICIT) (2015) 2403–2408. [Google Scholar]
  • T. Farzad, O. Mohammad Rasid, A. Aidy and Y. Rosnah Mohd, A review of supplier selection methods in manufacturing industries. Suranaree. J. Sci. Technol. 15 (2008) 201–208. [Google Scholar]
  • Y. Feng, G. Li and S.P. Sethi, A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing. Int. J. Prod. Econ. 196 (2018) 269–283. [Google Scholar]
  • D. Gross and C.M. Harris, Fundamentals of Queueing Theory. John Wiley and Sons Inc, New York (2008). [Google Scholar]
  • K. Halat and R. Bashirzadeh, Concurrent scheduling of manufacturing cells considering sequence-dependent family setup times and intercellular transportation times. Int. J. Adv. Manuf. Technol. 77 (2015) 1907–1915. [Google Scholar]
  • R. Hammami, Y. Frein and A.B. Hadj-Alouane, An international supplier selection model with inventory and transportation management decisions. Flexible Serv. Manuf. J. 24 (2012) 4–27. [Google Scholar]
  • M. Hazarika and D. Laha, A heuristic approach for machine cell formation problems with alternative routings. Proc. Comput. Sci. 89 (2016) 228–242. [Google Scholar]
  • S.S. Heragu, Group technology and cellular manufacturing. IEEE Trans. Syst. Man Cybern. 24 (1994) 203–215. [Google Scholar]
  • W. Ho, X. Xu and P.K. Dey, Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202 (2010) 16–24. [Google Scholar]
  • M. Igarashi, L. de Boer and A.M. Fet, What is required for greener supplier selection? A literature review and conceptual model development. J. Purchasing Supply Manage. 19 (2013) 247–263. [Google Scholar]
  • S.A. Irani, Handbook of Cellular Manufacturing Systems. John Wiley & Sons (1999). [Google Scholar]
  • K. Jankauskas, L.G. Papageorgiou and S.S. Farid, Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture. Comput. Chem. Eng. 121 (2019) 212–223. [Google Scholar]
  • A.K. Kamrani, H.R. Parsaei and D.H. Liles, Planning, Design, and Analysis of Cellular Manufacturing Systems. Newnes 24 (1995). [Google Scholar]
  • S.E. Kesen and Z. Güngör, Job scheduling in virtual manufacturing cells with lot-streaming strategy: a new mathematical model formulation and a genetic algorithm approach. J. Oper. Res. Soc. 63 (2012) 683–695. [Google Scholar]
  • J.R. King and V. Nakornchai, Machine-component group formation in group technology: review and extension. Int. J. Prod. Res. 20 (1982) 117–133. [Google Scholar]
  • R. Kumar and S.P. Singh, Modified SA algorithm for bi-objective robust stochastic cellular facility layout in cellular manufacturing systems. In: Advanced Computing and Communication Technologies. Springer (2019) 19–33. [Google Scholar]
  • S.-W. Lin and K.-C. Ying, Makespan optimization in a no-wait flowline manufacturing cell with sequence-dependent family setup times. Comput. Ind. Eng. 128 (2019) 1–7. [Google Scholar]
  • C. Liu and J. Wang, Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing. Int. J. Comput. Intell. Syst. 9 (2016) 765–777. [Google Scholar]
  • C. Liu, J. Wang, J.Y.-T. Leung and K. Li, Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm. Int. J. Prod. Res. 54 (2016) 923–944. [Google Scholar]
  • C. Liu, J. Wang and J.Y.-T. Leung, Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning. Appl. Soft Comput. 62 (2018) 602–618. [Google Scholar]
  • F. Mallor, C. Azcárate and J. Barado, Control problems and management policies in health systems: application to intensive care units. Flexible Serv. Manuf. J. 28 (2016) 62–89. [Google Scholar]
  • O. Pal, A.K. Gupta and R. Garg, Supplier selection criteria and methods in supply chains: a review. Int. J. Soc. Manage. Econ. Bus. Eng. 7 (2013) 1403–1409. [Google Scholar]
  • V.C. Pasupuleti, Scheduling in cellular manufacturing systems. Iberoam. J. Ind. Eng. 4 (2012) 231–243. [Google Scholar]
  • M.M. Paydar and M. Saidi-Mehrabad, Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. Int. J. Comput. Integr. Manuf. 28 (2015) 251–265. [Google Scholar]
  • M.M. Paydar and M. Saidi-Mehrabad, A hybrid genetic algorithm for dynamic virtual cellular manufacturing with supplier selection. Int. J. Adv. Manuf. Technol. 92 (2017) 3001–3017. [Google Scholar]
  • M.M. Paydar, M. Saidi-Mehrabad and E. Teimoury, A robust optimisation model for generalised cell formation problem considering machine layout and supplier selection. Int. J. Comput. Integr. Manuf. 27 (2014) 772–786. [Google Scholar]
  • B. Rabbouch, F. Saâdaoui and R. Mraihi, Efficient implementation of the genetic algorithm to solve rich vehicle routing problems. Oper. Res. 21 (2021) 1763–1791. [Google Scholar]
  • R. Rachamadugu, U. Nandkeolyar and T. Schriber, Scheduling with sequencing flexibility. Decis. Sci. 24 (1993) 315–342. [Google Scholar]
  • H. Rafiei, M. Rabbani, H. Gholizadeh and H. Dashti, A novel hybrid SA/GA algorithm for solving an integrated cell formation–job scheduling problem with sequence-dependent set-up times. Int. J. Manage. Sci. Eng. Manage. 11 (2016) 134–142. [Google Scholar]
  • R. Ramezanian and S. Khalesi, Integration of multi-product supply chain network design and assembly line balancing. Oper. Res. 21 (2021) 453–483. [Google Scholar]
  • P.P. Rao and R. Mohanty, Impact of cellular manufacturing on supply chain management: exploration of interrelationships between design issues. Int. J. Manuf. Technol. Manage. 5 (2003) 507–520. [Google Scholar]
  • A. Sadeghi, G. Suer, R.Y. Sinaki and D. Wilson, Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: a simulation-based analysis. Comput. Ind. Eng. 141 (2020) 106282. [Google Scholar]
  • M. Saravanan and S. Karthikeyan, Scheduling optimization cell formation problem for cellular manufacturing system using meta-heuristic methods. Appl. Mech. Mater. 786 (2015) 340–344. [Google Scholar]
  • L.K. Saxena and P. Jain, An integrated model of dynamic cellular manufacturing and supply chain system design. Int. J. Adv. Manuf. Technol. 62 (2012) 385–404. [Google Scholar]
  • J. Schaller, Incorporating cellular manufacturing into supply chain design. Int. J. Prod. Res. 46 (2008) 4925–4945. [Google Scholar]
  • D. Shishebori and S. Dehnavi-Arani, A multi-stage stochastic programming approach in a dynamic cell formation problem with uncertain demand: a case study. Int. J. Supply Oper. Manage. 6 (2019) 67–87. [Google Scholar]
  • D. Simchi-Levi, P. Kaminsky and E. Simchi-Levi, Managing the Supply Chain: Definitive Guide. Tata McGraw-Hill Education (2004). [Google Scholar]
  • M. Solimanpur and A. Elmi, A tabu search approach for cell scheduling problem with makespan criterion. Int. J. Prod. Econ. 141 (2013) 639–645. [Google Scholar]
  • M. Soolaki and J. Arkat, Incorporating dynamic cellular manufacturing into strategic supply chain design. Int. J. Adv. Manuf. Technol. 95 (2018) 2429–2447. [Google Scholar]
  • L. Tang, Z. Jin, X. Qin and K. Jing, Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design. Ann. Oper. Res. 275 (2019) 685–714. [Google Scholar]
  • S. Taouji Hassanpour, R. Bashirzadeh, A. Adressi and B. Bahmankhah, Scheduling problem of virtual cellular manufacturing systems (VCMS); Using simulated annealing and genetic algorithm based heuristics. J. Mod. Processes Manuf. Prod. 3 (2014) 45–60. [Google Scholar]
  • M. Wazed, S. Ahmed and Y. Nukman, Uncertainty factors in real manufacturing environment. Aust. J. Basic Appl. Sci. 3 (2009) 342–351. [Google Scholar]
  • U. Wemmerlöv and N.L. Hyer, Research issues in cellular manufacturing. Int. J. Prod. Res. 25 (1987) 413–431. [Google Scholar]
  • U. Wemmerlöv and N.L. Hyer, Cellular manufacturing in the US industry: a survey of users. Int. J. Prod. Res. 27 (1989) 1511–1530. [Google Scholar]
  • X. Wu, C.-H. Chu, Y. Wang and D. Yue, Genetic algorithms for integrating cell formation with machine layout and scheduling. Comput. Ind. Eng. 53 (2007) 277–289. [Google Scholar]
  • G. Xue and O.F. Offodile, Integrated optimization of dynamic cell formation and hierarchical production planning problems. Comput. Ind. Eng. 139 (2020) 106155. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.