Free Access
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S1875 - S1912
Published online 02 March 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. Aghajani, S.A. Didehbani, M. Kazemi and N. Javadian, A dynamic non-linear mixed integer-programming model for the CMS design with production planning. Int. J. Ind. Syst. Eng. 16 (2014) 70–87. [Google Scholar]
  • S. Ahkioon, A.-A. Bulgak and T. Bektas, Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur. J. Oper. Res. 192 (2009) 414–428. [Google Scholar]
  • A. Alfieri and G. Nicosia, Sequencing a batching flexible cell to minimise set-up costs. Int. J. Prod. Res. 52 (2014) 2461–2476. [Google Scholar]
  • F. Alhourani, Cellular manufacturing system design considering machines reliability and parts alternative process routings. Int. J. Prod. Res. 54 (2016) 846–863. [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]
  • A. Azadeh, M. Ravanbakhsh, M. Rezaei-Malek, M. Sheikhalishahi and A. Taheri-Moghaddam, Unique NSGA-II and MOPSO algorithms for improved dynamic cellular manufacturing systems considering human factors. Appl. Math. Model. 48 (2017) 655–672. [Google Scholar]
  • H. Bayram and R. Şahin, A comprehensive mathematical model for dynamic cellular manufacturing system design and Linear Programming embedded hybrid solution techniques. Comput. Ind. Eng. 91 (2016) 10–29. [Google Scholar]
  • J.L. Burbidge, Production flow analysis. Prod. Eng. 50 (1971) 139–152. [Google Scholar]
  • W.G. Cochran and G.M. Cox, Experimental Designs, 2nd edtion. John Wiley & Sons, New York, NY (1992). [Google Scholar]
  • K. Deep and P.K. Singh, Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. J. Manuf. Syst. 35 (2015) 155–163. [Google Scholar]
  • F.M. Defersha and A. Hodiya, A mathematical model and a parallel multiple search path simulated annealing for an integrated distributed layout design and machine cell formation. J. Manuf. Syst. 43 (2017) 195–212. [Google Scholar]
  • A. Delgoshaei and G. Chandima, 2016. A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Appl. Soft Comput. 49 (2016) 27–55. [Google Scholar]
  • A. Delgoshaei, A. Ali, M.K.A. Ariffin and C. Gomes, A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Comput. Ind. Eng. 100 (2016) 110–132. [Google Scholar]
  • J. Drolet, Y. Marcoux and G. Abdulnour, Simulation-based performance comparison between dynamic cells, classical cells and job shops: a case study. Int. J. Prod. Res. 46 (2008) 509–536. [Google Scholar]
  • A. Ebrahimi, R. Kia and A.R. Komijan, Solving a mathematical model integrating unequal-area facilities layout and part scheduling in a cellular manufacturing system by a genetic algorithm. SpringerPlus 5 (2016) 1254. [PubMed] [Google Scholar]
  • H. Feng, L. Xi, T. Xia and E. Pan, Concurrent cell formation and layout design based on hybrid approaches. Appl. Soft Comput. 66 (2018) 346–359. [Google Scholar]
  • V.R. Ghezavati, A new stochastic mixed integer programming to design integrated cellular manufacturing system: a supply chain framework. Int. J. Ind. Eng. Comput. 2 (2011) 563–574. [Google Scholar]
  • V.R. Ghezavati, Designing integrated cellular manufacturing systems with tactical decisions. J. Chin. Inst. Eng. 38 (2015) 332–341. [Google Scholar]
  • V.R. Ghezavati, S. Sadjadi and M. Dehghan Nayeri, Integrating strategic and tactical decisions to robust designing of cellular manufacturing under uncertainty: fixed suppliers in supply chain. Int. J. Comput. Intel. Syst. 4 (2011) 837–854. [Google Scholar]
  • T. Ghosh, B. Doloi and P.K. Dan, Applying soft-computing techniques in solving dynamic multi-objective layout problems in cellular manufacturing system. Int. J. Adv. Manuf. Technol. 86 (2016) 237–257. [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]
  • S.S. Heragu, Group technology and cellular manufacturing. IEEE Trans. Syst. Man Cybern. 24 (1994) 203–214. [Google Scholar]
  • A. Iqbal and K.A. Al-Ghamdi, Energy-efficient cellular manufacturing system: eco-friendly revamping of machine shop configuration. Energy 163 (2018) 863–872. [Google Scholar]
  • F. Khaksar-Haghani, R. Kia, N. Javadian, R. Tavakkoli-Moghaddam and A. Baboli, A comprehensive mathematical model for the design of a dynamic cellular manufacturing system integrated with production planning and several manufacturing attributes. Int. J. Ind. Eng. Prod. Res. 22 (2011) 199–212. [Google Scholar]
  • R. Kia, A. Baboli, N. Javadian, R. Tavakkoli-Moghaddam, M. Kazemi and J. Khorrami, Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Comput. Oper. Res. 39 (2012) 2642–2658. [Google Scholar]
  • R. Kia, N. Javadian, M.M. Paydar and M. Saidi-Mehrabad, A simulated annealing for intra-cellularlayout design of dynamic cellular manufacturing systems with route selection, purchasing machines and cell reconfiguration. Asia-Pac. J. Oper. Res. 30 (2013) 1350004. [Google Scholar]
  • R. Kia, N. Javadian and R. Tavakkoli-Moghaddam, A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system. J. Optim. Ind. Eng. 14 (2014) 37–52. [Google Scholar]
  • R. Kia, M. Kazemi, S. Shafiee Gol, R. Tavakkoli-Moghaddam and J. Khorrami, A mathematical model for assessing the effects of a lot splitting feature on a dynamic cellular manufacturing system. Prod. Eng. 11 (2017) 557–573. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. [Google Scholar]
  • J. Li, A. Wang and C. Tang, Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm. Int. J. Adv. Manuf. Technol. 74 (2014) 47–64. [Google Scholar]
  • Y. Li, X. Li and J.N. Gupta, Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search. Expert Syst. App. 42 (2015) 1409–1417. [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 and J.Y.T. Leung, Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Comput. Ind. Eng. 96 (2016) 162–179. [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]
  • K.S. Lokesh and P.K. Jain, An integrated model of dynamic cellular manufacturing and supply chain system design. Int. J. Adv. Manuf. Technol. 62 (2012) 385–404. [Google Scholar]
  • E. Mehdizadeh and V. Rahimi, An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Appl. Soft Comput. 42 (2016) 325–341. [Google Scholar]
  • S.P. Mitrofanov, The Scientific Principles of Group Technology. National Lending Library Translation, Boston, MA (1966). [Google Scholar]
  • M. Mohammadi and K. Forghani, Designing cellular manufacturing systems considering S-shaped layout. Comput. Ind. Eng. 98 (2016) 221–236. [Google Scholar]
  • S. Molla-Alizadeh-Zavardehi, S. Sadi Nezhad, R. Tavakkoli-Moghaddam and M. Yazdani, Solving a fuzzy fixed charge solid transportation problem by metaheuristics. Math. Comput. Model. 57 (2013) 1543–1558. [Google Scholar]
  • D.C. Montgomery, Design and Analysis of Experiments. John Wiley & Sons, New York, NY (2017). [Google Scholar]
  • F. Niakan, A. Baboli, T. Moyaux and V. Botta-Genoulaz, A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment. J. Manuf. Syst. 38 (2016) 46–62. [Google Scholar]
  • J. Olhager, Evolution of operations planning and control: from production to supply chains. Int. J. Prod. Res. 51 (2013) 6836–6843. [Google Scholar]
  • M. Pajoutan, A. Golmohammadi and M. Seifbarghy, CMS scheduling problem considering material handling and routing flexibility. Int. J. Adv. Manuf. Technol. 72 (2014) 881–893. [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, 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]
  • A. Pailla, A.R. Trindade, V. Parada and L.S. Ochi, A numerical comparison between simulated annealing and evolutionary approaches to the cell formation problem. Expert Syst. App. 37 (2010) 5476–5483. [Google Scholar]
  • K. Rafiee, M. Rabbani, H. Rafiei and A. Rahimi-Vahed, A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system. Appl. Math. Modell. 35 (2011) 1810–1819. [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]
  • H. Raoofpanah, V. Ghezavati and R. Tavakkoli-Moghaddam, Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition. Eng. Optim. 51 (2019) 1229–1250. [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]
  • P. Renna and M. Ambrico, Design and reconfiguration models for dynamic cellular manufacturing to handle market changes. Int. J. Comput. Integr. Manuf. 28 (2015) 170–186. [Google Scholar]
  • M. Rheault, J. Drolet and G. Abdulnour, Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment. Comput. Ind. Eng. 29 (1995) 221–225. [Google Scholar]
  • R. Ruiz, C. Maroto and J. Alcaraz, Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics. Eur. J. Oper. Res. 165 (2005) 34–54. [Google Scholar]
  • M. Sakhaii, R. Tavakkoli-Moghaddam, M. Bagheri and B. Vatani, A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl. Math. Modell. 40 (2016) 169–191. [Google Scholar]
  • F. Shafigh, F.M. Defersha and S.E. Moussa, A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration. Int. J. Adv. Manuf. Technol. 88 (2017) 1119–1140. [Google Scholar]
  • G. Taguchi, Introduction to quality engineering: designing quality into products and processes. White Plains: Asian Productivity Organisation/UNIPUB, Tokyo (1986). [Google Scholar]
  • J. Tang, X. Wang, I. Kaku and K.L. Yung, Optimization of parts scheduling in multiple cells consideringintercell move using scatter search approach. J. Intell. Manuf. 21 (2010) 525–537. [Google Scholar]
  • J. Tang, C. Yan, X. Wang and C. Zeng, Using Lagrangian relaxation decomposition with heuristic to integrate the decisions of cell formation and parts scheduling considering intercell moves. IEEE Trans. Autom. Sci. Eng. 11 (2014) 1110–1121. [Google Scholar]
  • U. Wemmerlov and N.L. Hyer, Procedures for the part family/machine group identification problem in cellular manufacturing. J. Oper. Manage. 6 (1986) 125–147. [Google Scholar]
  • U. Wemmerlov 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]
  • J. Wang, C. Liu and K. Li, A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement. Automatika 60 (2019) 172–180. [Google Scholar]
  • A. Zeb, M. Khan, N. Khan, A. Tariq, L. Ali, F. Azam and S.H.I. Jaffery, Hybridization of simulated annealing with genetic algorithm for cell formation problem. Int. J. Adv. Manuf. Technol. 86 (2016) 2243–2254. [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.