Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
Page(s) 4819 - 4859
DOI https://doi.org/10.1051/ro/2024195
Published online 19 November 2024
  • O.I. Amariei, D. Frunzaverde, G. Popovicia and C.O. Hamat, WinQSB simulation software – a tool for professional development. Proc. Soc. Behav. Sci. 1 (2009) 2786–2790. [CrossRef] [Google Scholar]
  • A.L. Arcus, COMSOAL: a computer method of sequencing operations for assembly lines. Int. J. Prod. Res. 4 (1965) 259–277. [CrossRef] [Google Scholar]
  • A. Ayough, M. Hosseinzadeh and A. Motameni, Job rotation scheduling in the Seru system: shake enforced invasive weed optimization approach. Assembly Autom. 40 (2020) 461–474. [CrossRef] [Google Scholar]
  • A. Azadeh, M. Moghaddam, S.M. Asadzadeh and A. Negahban, An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: the case of injection process with ambiguous data. Eur. J. Oper. Res. 214 (2011) 768–779. [Google Scholar]
  • R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30 (1984) 1078–1092. [Google Scholar]
  • O. Belgin, Data envelopment analysis based metamodeling for multi objective simulation optimization in a manufacturing line. Sigma J. Eng. Nat. Sci. 37 (2019) 1435–1449. [Google Scholar]
  • A. Boussofiane, R.G. Dyson and E. Thanassoulis, Applied data envelopment analysis. Eur. J. Oper. Res. 52 (1991) 1–15. [CrossRef] [Google Scholar]
  • E. Çalışkan, S.K. İşleyen and H. Çerçioğlu, A mixed integer mathematical model for loading problem in seru manufacturing systems and matheuristic solution approach. J. Facul. Eng. Arch. Gazi Univ. 36 (2021) 793–806. [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
  • V. Coll-Serrano, V. Bolos and R.B. Suarez, deaR: conventional and fuzzy data envelopment analysis. CRAN (2020). [Google Scholar]
  • T.V. Deepthi, K. Ramakotaiah and K. Krishnaveni, Research on performance of multi-skilled workers for sustainable production planning in seru production systems. Int. J. Innov. Technol. Exploring Eng. 8 (2019) 1–13. [CrossRef] [Google Scholar]
  • T.V. Deepthi, K. Ramakotaiah, V.K. Manupati and C. Gangal, Investigating the performance improvement by conversion of assembly line configuration to a pure cell system in manufacturing industry. Eur. J. Ind. Eng. 13 (2019) 723–745. [CrossRef] [Google Scholar]
  • J. Doyle and R. Green, Efficiency and cross-efficiency in DEA: derivations, meanings and uses. J. Oper. Res. Soc. 45 (1994) 567–578. [CrossRef] [Google Scholar]
  • A. Ebrahimnejad and N. Amani, Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points. Complex Intell. Syst. 7 (2021) 379–400. [CrossRef] [Google Scholar]
  • A. Ebrahimnejad and M. Tavana, An interactive MOLP method for identifying target units in output-oriented DEA models: the NATO enlargement problem. Measurement 52 (2014) 124–134. [CrossRef] [Google Scholar]
  • A. Ebrahimnejad, M. Tavana and S.M. Mansourzadeh, An interactive MOLP method for solving output-oriented DEA problems with undesirable factors. J. Ind. Manage. Optim. 11 (2015) 1089–1110. [CrossRef] [Google Scholar]
  • A. Ebrahimnejad, M. Tavana and F.J. Santos-Arteaga, An integrated data envelopment analysis and simulation method for group consensus ranking. Math. Comput. Simul. 119 (2016) 1–17. [CrossRef] [Google Scholar]
  • M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc. Ser. A (General) 120 (1957) 253–281. [Google Scholar]
  • A. Furugi and M. Haliloğlu, Seru üretim sisteminde hat-seru dönü¸sümü ve çizelgeleme problemi için matematiksel model önerisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37 (2022) 1213–1224. [Google Scholar]
  • Y. Gai, Y. Yin, J. Tang and S. Liu, Minimizing makespan of a production batch within concurrent systems: seru production perspective. J. Manage. Sci. Eng. 7 (2022) 1–18. [Google Scholar]
  • J. Gong, V.-V. Prabhu and W. Liu, Simulation-based performance comparison between assembly lines and assembly cells with real-time distributed arrival time control system. Int. J. Prod. Res. 49 (2011) 1241–1253. [CrossRef] [Google Scholar]
  • Y. Han, C. Long, Z. Geng and K. Zhang, Carbon emission analysis and evaluation of industrial departments in China: an improved environmental DEA cross model based on information entropy. J. Environ. Manage. 205 (2018) 298–307. [CrossRef] [Google Scholar]
  • W.B. Helgeson and D.-P. Birnie, Assembly line balancing using the ranked positional weight technique. J. Ind. Eng. 12 (1961) 394–398. [Google Scholar]
  • S. Jain, K.P. Triantis and S. Liu, Manufacturing performance measurement and target setting: a data envelopment analysis approach. Eur. J. Oper. Res. 214 (2011) 616–626. [CrossRef] [Google Scholar]
  • D.-J. Johnson, Converting assembly lines to assembly cells at sheet metal products: insights on performance improvements. Int. J. Prod. Res. 43 (2005) 1483–1509. [CrossRef] [Google Scholar]
  • I. Kaku, Is seru a sustainable manufacturing system? Proc. Manuf. 8 (2017) 723–730. [Google Scholar]
  • I. Kaku, Y. Murase and Y. Yin, A study on human-task-related performances in converting conveyor assembly line to cellular manufacturing. Eur. J. Ind. Eng. 2 (2008) 17–34. [CrossRef] [Google Scholar]
  • I. Kaku, J. Gong, J. Tang and Y. Yin, Modeling and numerical analysis of line-cell conversion problems. Int. J. Prod. Res. 47 (2009) 2055–2078. [CrossRef] [Google Scholar]
  • E.-E. Karsak and F. İşcan, Çimento sektöründe göreli faaliyet performanslarının ağırlık kısıtlamaları ve çapraz etkinlik kullanılarak veri zarflama analizi ile değerlendirilmesi. Endüstri Mühendisliği 11 (2000) 2–10. [Google Scholar]
  • S. B. Kiris, E. Eryarsoy, S. Zaim, and D. Delen, An integrated approach for lean production using simulation and data envelopment analysis. Annals of Operations Research 320 (2023) 863–886. [CrossRef] [Google Scholar]
  • S.G. Lee, L.P. Khoo and X.F. Yin, Optimising an assembly line through simulation augmented by genetic algorithms. Int. J. Adv. Manuf. Technol. 16 (2000) 220–228. [CrossRef] [Google Scholar]
  • X. Li, D. Li, X. Wu, H. Zheng and Y. Yin, A cooperative co-evolution approach for a line-seru conversion problem, in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE (2017) 1406–1411. [Google Scholar]
  • X. Li, Y. Yu and M. Huang, Multi-objective cooperative coevolution algorithm with a Master–Slave mechanism for Seru Production. Appl. Soft Comput. 119 (2022) 108593. [CrossRef] [Google Scholar]
  • X. Li, Y. Yu, W. Sun and J. Tang, Reducing tardy batches by seru production: model, exact solution, cooperative coevolution solution, and insights. Comput. Oper. Res. 160 (2023) 106048. [CrossRef] [Google Scholar]
  • X. Li, Z. Zhang, W. Sun, Y. Liu and J. Tang, Parallel dynamic NSGA-II with multi-population search for rescheduling of seru production considering schedule changes under different dynamic events. Expert Syst. App. 238 (2024) 121993. [CrossRef] [Google Scholar]
  • J. Lian, C. Liu, W. Li and Y. Yin, A multiskilled worker assignment problem in seru production systems considering the worker heterogeneity. Comput. Ind. Eng. 118 (2018) 366–382. [CrossRef] [Google Scholar]
  • C. Liu, J. Lian, Y. Yin and W. Li, Seru Seisan-an innovation of the production management mode in Japan. Asian J. Technol. Innov. 18 (2010) 89–113. [CrossRef] [Google Scholar]
  • C. Liu, W. Li, J. Lian and Y. Yin, Reconfiguration of assembly systems: from conveyor assembly line to serus. J. Manuf. Syst. 31 (2012) 312–325. [CrossRef] [Google Scholar]
  • C. Liu, N. Yang, W. Li, J. Lian, S. Evans and Y. Yin, Training and assignment of multiskilled workers for implementing seru production systems. Int. J. Adv. Manuf. Technol. 69 (2013) 937–959. [CrossRef] [Google Scholar]
  • C. Liu, K.-E. Stecke, J. Lian and Y. Yin, An implementation framework for seru production. Int. Trans. Oper. Res. 21 (2014) 1–19. [CrossRef] [MathSciNet] [Google Scholar]
  • C. Liu, F. Dang, W. Li, J. Lian, S. Evans and Y. Yin, Production planning of multi-stage multi-option seru production systems with sustainable measures. J. Clean. Prod. 105 (2015) 285–299. [CrossRef] [Google Scholar]
  • M. Majdi, A. Ebrahimnejad and A. Azizi, Common-weights fuzzy DEA model in the presence of undesirable outputs with ideal and anti-ideal points: development and prospects. Complex Intell. Syst. 9 (2023) 6223–6240. [CrossRef] [Google Scholar]
  • P.-R. McMullen and G.-V. Frazier, Using simulation and data envelopment analysis to compare assembly line balancing solutions. J. Prod. Anal. 11 (1999) 149–168. [CrossRef] [Google Scholar]
  • Q. Miao, Z. Bai, X. Liu and M. Awais, Modelling and numerical analysis for seru system balancing with lot splitting. Int. J. Prod. Res. 61 (2023) 7410–7433. [CrossRef] [Google Scholar]
  • H. Mosadegh, S.-M.-T. Fatemi Ghomi and G.A. Süer, A control theoretical modelling for velocity tuning of the conveyor belt in a dynamic mixed-model assembly line. Int. J. Prod. Res. 55 (2017) 7473–7495. [CrossRef] [Google Scholar]
  • J. Prakash, C.C. Yang and J.F. Chin, Labour-intensive line-cell reconfiguration with cycle time adjustment attributed to changes in situational awareness. Int. J. Ind. Syst. Eng. 27 (2017) 210–232. [Google Scholar]
  • S. Reinhard, C.A.K. Lovell and G. Thijssen, Econometric estimation of technical and environmental efficiency: an application to Dutch dairy farms. Am. J. Agric. Econ. 81 (1999) 44–60. [CrossRef] [Google Scholar]
  • H. Sameie and M. Arvan, A simulation-based Data Envelopment Analysis (DEA) model to evaluate wind plants locations. Decis. Sci. Lett. 4 (2015) 165–180. [CrossRef] [Google Scholar]
  • L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [Google Scholar]
  • K. Sengupta and F.-R. Jacobs, Impact of work teams: a comparison study of assembly cells and assembly line for a variety of operating environments. Int. J. Prod. Res. 42 (2004) 4173–4193. [CrossRef] [Google Scholar]
  • T.R. Sexton, R.H. Silkman and A.J. Hogan, Data envelopment analysis: critique and extensions. New Directions Program Eval. 1986 (1986) 73–105. [CrossRef] [Google Scholar]
  • H. Shan, M. Qin, C. Zou, P. Peng and Z. Meng, Assembly line-Seru conversion in the C2M enterprise: an empirical study in China. Assembly Autom. 42 (2022) 506–520. [CrossRef] [MathSciNet] [Google Scholar]
  • L. Shao, Z. Zhang and Y. Yin, A bi-objective combination optimisation model for line-seru conversion based on queuing theory. Int. J. Manuf. Res. 11 (2016) 322–338. [CrossRef] [Google Scholar]
  • L. Shao, Z. Zhang and Y. Yin, Production system performance improvement by assembly line-seru conversion, in Proceedings of the Tenth International Conference on Management Science and Engineering Management. Springer, Singapore (2017) 1165–1180. [Google Scholar]
  • S. Singh, Study on seru production system. Int. J. Appl. Res. Sci. Eng. (2017) 83–89. [Google Scholar]
  • H. Söyler and A. Koç, Bir kamu hastanesi için acil servis simülasyonu ve veri zarflama analizi ile etkinlik ölçümü. Aksaray Universites Iktisadi ve Idari Bilimler Fakuultesi Dergisi 6 (2014) 117–132. [Google Scholar]
  • K.E. Stecke, Y. Yin and I. Kaku, Seru production: an extension of just-in-time approach for volatile business environments, in Analytical Approaches to Strategic Decision-Making: Interdisciplinary Considerations. IGI Global (2014). DOI: 10.4018/978-1-4666-5958-2.ch003. [Google Scholar]
  • W. Sun, Q. Li, C. Huo, Y. Yu and K. Ma, Formulations, features of solution space, and algorithms for line-pure seru system conversion. Math. Prob. Eng. 2016 (2016) 9748378. [Google Scholar]
  • W. Sun, Y. Yu, Q. Lou and Y. Yu, A cooperative coevolution algorithm for the seru production with minimizing makespan. IEEE Access 7 (2019) 5662–5670. [CrossRef] [Google Scholar]
  • W. Sun, Y. Yu, Q. Lou, J. Wang and Y. Guan, Reducing the total tardiness by Seru production: model, exact and cooperative coevolution solutions. Int. J. Prod. Res. 58 (2020) 6441–6452. [CrossRef] [Google Scholar]
  • Y. Wang and J. Tang, Multi-objective optimization model for seru production system formation under uncertain condition, in 2017 International Conference on Service Systems and Service Management. IEEE (2017). [Google Scholar]
  • Y. Wang and J. Tang, Cost and service-level-based model for a seru production system formation problem with uncertain demand. J. Syst. Sci. Syst. Eng. 27 (2018) 519–537. [CrossRef] [Google Scholar]
  • L. Wang, Z. Zhang and Y. Yin, A bi-level nested heuristic algorithm for divisional seru order acceptance and scheduling problems. Appl. Soft Comput. 143 (2023) 110354. [CrossRef] [Google Scholar]
  • S.J. Weng, B.-S. Tsai, L.-M. Wang, C.-Y. Chang and D. Gotcher, Using simulation and data envelopment analysis in optimal healthcare efficiency allocations, in Proceedings of the 2011 Winter Simulation Conference (WSC). IEEE (2011) 1295–1305. [Google Scholar]
  • Y. Wu, L. Wang and J.-F. Chen, A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization. Complex Intell. Syst. 7 (2021) 2559–2576. [CrossRef] [Google Scholar]
  • Y. Wu, L. Wang, X. Zhuang, J.J. Wang, J.F. Chen and J. Zheng, A cooperative coevolutionary algorithm with problem-specific knowledge for energy-efficient scheduling in seru system. Knowl.-Based Syst. 274 (2023) 110663. [CrossRef] [Google Scholar]
  • Ö.-F. Yılmaz, Attaining flexibility in seru production system by means of Shojinka: an optimization model and solution approaches. Comput. Oper. Res. 119 (2020) 104917. [CrossRef] [MathSciNet] [Google Scholar]
  • B.G. Yılmaz, Ö.F. Yılmaz and E. Çevikcan, Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy. Comput. Ind. Eng. 185 (2023) 109680. [CrossRef] [Google Scholar]
  • Y. Yin, K.-E. Stecke, M. Swink and I. Kaku, Lessons from seru production on manufacturing competitively in a high cost environment. J. Oper. Manage. 49 (2017) 67–76. [CrossRef] [Google Scholar]
  • K.-C. Ying and Y.-J. Tsai, Minimising total cost for training and assigning multiskilled workers in seru production systems. Int. J. Prod. Res. 55 (2017) 2978–2989. [CrossRef] [Google Scholar]
  • Y. Yu and J. Tang, Review of seru production. Front. Eng. Manage. 6 (2019) 183–192. [CrossRef] [Google Scholar]
  • Y. Yu, J. Gong, J. Tang, Y. Yin and I. Kaku, How to carry out assembly line–cell conversion? A discussion based on factor analysis of system performance improvements. Int. J. Prod. Res. 50 (2012) 5259–5280. [CrossRef] [Google Scholar]
  • Y. Yu, J. Tang, W. Sun, Y. Yin and I. Kaku, Combining local search into non-dominated sorting for multi-objective line-cell conversion problem. Int. J. Comput. Integr. Manuf. 26 (2013) 316–326. [CrossRef] [Google Scholar]
  • Y. Yu, J. Tang, W. Sun, Y. Yin and I. Kaku, Reducing worker(s) by converting assembly line into a pure cell system. Int. J. Prod. Econ. 145 (2013) 799–806. [CrossRef] [Google Scholar]
  • Y. Yu, J. Tang, J. Gong, Y. Yin and I. Kaku, Mathematical analysis and solutions for multi-objective line-cell conversion problem. Eur. J. Oper. Res. 236 (2014) 774–786. [CrossRef] [Google Scholar]
  • Y. Yu, S. Wang, J. Tang, I. Kaku and W. Sun, Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization. SpringerPlus 5 (2016) 1–26. [CrossRef] [PubMed] [Google Scholar]
  • Y. Yu, W. Sun, J. Tang, I. Kaku and J. Wang, Line-seru conversion towards reducing worker(s) without increasing makespan: models, exact and meta-heuristic solutions. Int. J. Prod. Res. 55 (2017) 2990–3007. [CrossRef] [Google Scholar]
  • Y. Yu, W. Sun, J. Tang and J. Wang, Line-hybrid seru system conversion: models, complexities, properties, solutions and insights. Comput. Ind. Eng. 103 (2017) 282–299. [CrossRef] [Google Scholar]
  • S. Zeng, Y. Wu and Y. Yu, Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness. Kybernetes 52 (2023) 3495–3518. [CrossRef] [Google Scholar]
  • X. Zhang and Y. Chen, Carbon emission evaluation based on multi-objective balance of sewing assembly line in apparel industry. Energies 12 (2019) 2783. [CrossRef] [Google Scholar]
  • X. Zhang, C. Liu, W. Li, S. Evans and Y. Yin, Effects of key enabling technologies for seru production on sustainable performance. Omega 66 (2017) 290–307. [CrossRef] [Google Scholar]
  • X. Zhang, L. Wang and Y. Chen, Carbon emission reduction of apparel material distribution based on multi-objective genetic algorithm (NSGA-II). Sustainability 11 (2019) 1–15. [Google Scholar]
  • Z. Zhang, X. Gong, X. Song, Y. Yin, B. Lev and X. Zhou, An effective two phase heuristic for synchronized seru production scheduling and 3PL transportation problems. Int. J. Prod. Econ. 268 (2024) 109126. [CrossRef] [Google Scholar]
  • P. Zwierzyński, Conversion of a serial line assembly into a cellular structure, in 2019 IEEE Technology & Engineering Management Conference (TEMSCON). IEEE, Atlanta, GA, USA (2019) 1–5. [Google Scholar]
  • P. Zwierzyński and H. Ahmad, Seru production as an alternative to a traditional assembly line. Eng. Manage. Prod. Serv. 10 (2018) 62–69. [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.