Have a personal or library account? Click to login
Clustering Based Heuristics for Aligning Master Production Schedule and Delivery Schedule Cover

Clustering Based Heuristics for Aligning Master Production Schedule and Delivery Schedule

Open Access
|Sep 2024

References

  1. M. Stevenson, L.C. Hendry, and B.G. Kingsman, “A review of production planning and control: the applicability of key concepts to the make-to-order industry,” International Journal of Production Research, vol. 43, no. 5, pp. 869-898, 2005. DOI:10.1080/0020754042000298520
  2. C.C. Teo, R. Bhatnagar, and S.C. Graves, “An application of master schedule smoothing and planned lead time control,” Production and Operations Management, vol. 21, no. 2, pp. 211-223, 2012. DOI:10.1111/j.1937-5956.2011.01263.x
  3. J. Jiao, L. Zhang, and S. Pokharel, “Coordinating product and process variety for mass customized order fulfillment,” Production Planning and Control, vol. 16, no. 6 (Spec. Iss.), pp. 608-620, 2005. DOI:10.1080/09537280500112181
  4. M. Brettel, D. Bendig, M. Keller, N. Friederichsen, and M. Rosenberg, “Effectuation in manufacturing: How entrepreneurial decision-making techniques can be used to deal with uncertainty in manufacturing,” Procedia CIRP, vol. 17, pp. 611-616, 2014. DOI:10.1016/j.procir.2014.03.119
  5. E. Guzman, B. Andres, and R. Poler, “Matheuristic Algorithms for Production Planning in Manufacturing Enterprises,” in IFIP Advances in Information and Communication Technology, vol. 626, pp. 115-122, 2021. DOI:10.1007/978-3-030-78288-7_11
  6. S. Naima, S. Nguyen, K. Cullinane, V. Gekara, and P. Chhetri, “Forecasting container freight rates using the Prophet forecasting method,” Transport Policy, vol. 133, pp. 86-107, 2023. DOI:10.1016/j.tranpol.2023.01.012
  7. I. Supriyanto and B. Noche, “Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule,” in Logistics Journal: Proceedings, vol. 7, no. 1, pp. 1-14, 2011. DOI:10.2195/LJ_proc_supriyanto_de_201108_01
  8. X. Zhao, J. Xie, and Q. Jiang, “Lot‐sizing rule and freezing the master production schedule under capacity constraint and deterministic demand,” Production and Operations Management, vol. 10, no. 1, pp. 45-67, 2001. DOI:10.1111/j.1937-5956.2001.tb00067.x
  9. J.C. Serrano-Ruiz, J. Mula, and R. Poler, “Smart master production schedule for the supply chain: a conceptual framework,” Computers, vol. 10, no. 12, p. 156, 2021. DOI:10.3390/computers10120156
  10. O. Tang and R.W. Grubbström, “Planning and replanning the master production schedule under demand uncertainty,” International Journal of Production Economics, vol. 78, pp. 145-152, 2002. DOI:10.1016/S0925-5273(00)00100-6
  11. G.E. Vieira and F. Favaretto, “A new and practical heuristic for master production scheduling creation,” International Journal of Production Research, vol. 44, no. 18-19, pp. 3607-3625, 2006. DOI:10.1080/00207540600818187
  12. M. Albrecht, J. Rhode, and M. Wagner, “Master planning,” in Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, H. Stadtler and C. Kilger, Eds. 4th ed., Springer, Berlin, pp. 161-179, 2015. DOI:10.1007/978-3-642-55309-7_8.
  13. M.R.A. Bakar, I.T. Abbas, M.A. Kalal, H.A. AlSattar, A.G.K. Bakhayt, and B.A. Kalaf, “Solution for multi-objective optimization master production scheduling problems based on swarm intelligence algorithms,” Journal of Computational and Theoretical Nanoscience, vol. 14, no. 11, pp. 5184-5194, 2017. DOI:10.1166/jctn.2017.6729
  14. K.E. Stecke and X. Zhao, “Production and transportation integration for a make-to-order manufacturing company with a commit-to-delivery business mode,” Manufacturing & Service Operations Management, vol. 9, no. 2, pp. 206-224, 2007. DOI:10.1287/msom.1060.0138
  15. A. Cakravastia and K. Takahashi, “Integrated model for supplier selection and negotiation in a make-to-order environment,” International Journal of Production Research, vol. 42, no. 21, pp. 4457-4474, 2004. DOI:10.1080/00207540410001727622
  16. F. Sahin, E.P. Robinson, and L.L. Gao, “Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain,” International Journal of Production Economics, vol. 115, no. 2, pp. 528-541, 2008. DOI:10.1016/j.ijpe.2008.05.019
  17. M. Ebadian, M. Rabbani, S.A. Torabi, and F. Jolai, “Hierarchical production planning and scheduling in make-to-order environments: reaching short and reliable delivery dates,” International Journal of Production Research, vol. 47, no. 20, pp. 5761-5789, 2009. DOI:10.1080/00207540802010799
  18. B.D. Neureuther, G.G. Polak, and N.R. Sanders, “A hierarchical production plan for a make-to-order steel fabrication plant,” Production Planning & Control, vol. 15, no. 3, pp. 324-335, 2004. DOI:10.1080/09537280410001703893
  19. L. Zhang and T.N. Wong, “Solving integrated process planning and scheduling problem with constructive meta-heuristics,” Information Sciences, vol. 340, pp. 1-16, 2016. DOI:10.1016/j.ins.2016.01.001
  20. . Ekici, M. Elyasi, O.Ö. Özener, and M.B. Sarıkaya, “An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics,” Computers & Operations Research, vol. 111, pp. 130-140, 2019. DOI:10.1016/j.cor.2019.06.007
  21. S.C. Nwanya, C.N. Achebe, O.O. Ajayi, and C.A. Mgbemene, “Process variability analysis in make-to-order production systems,” Cogent Engineering, vol. 3, no. 1, art. 1269382, 2016. DOI:10.1080/23311916.2016.1269382
  22. X. Li and J.A. Ventura, “Exact algorithms for a joint order acceptance and scheduling problem,” International Journal of Production Economics, vol. 223, art. 107516, 2020. DOI:10.1016/j.ijpe.2019.107516
  23. X. Li, J.A. Ventura, and K.A. Bunn, “A joint order acceptance and scheduling problem with earliness and tardiness penalties considering overtime,” Journal of Scheduling, vol. 24, pp. 49-68, 2021. DOI:10.1007/s10951-020-00672-5
  24. T.J. Ai and R.D. Astanti, “Coordinating Production and Delivery Schedule of Multi-Product and Multi-Customer through Mathematical Programming,” Applied System Innovation, vol. 5, no. 4, p. 59, 2022. DOI:10.3390/asi5040059
  25. T.E. Vollmann, W.L. Berry, D.C. Whybark, and F.R. Jacobs, “Manufacturing planning and control systems for supply chain management,” 5th ed., McGraw-Hill, New York, 2005.
  26. M. Ehrgott and X. Gandibleux, “A survey and annotated bibliography of multi-objective combinatorial optimization,” OR Spektrum, vol. 22, no. 4, pp. 425-460, 2000. DOI:10.1007/s002910000046.
  27. A.A. Zaidan, B. Atiya, M.R. Abu Bakar, and B.B. Zaidan, “A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on a fuzzy environment,” Neural Computing and Applications, vol. 31, pp. 1823-1834, 2019. DOI:10.1007/s00521-017-3159-5
  28. Z.J. Wu, W. Wang, J. Zhou, F.F. Ren, and C. Zhang, “Research on double objective optimization of master production schedule based on ant colony algorithm,” in Proceedings of the 2010 International Conference on Computational Intelligence and Security, Y. Wang and G. Ping, Eds., pp. 200-204, 2010. DOI:10.1109/CIS.2010.49.
  29. S.S. Sadiq, A.M. Abdulazeez, and H. Haron, “Solving Multi-Objective Master Production Scheduling Model of Kalak Refinery System Using Hybrid Evolutionary Imperialist Competitive Algorithm,” Journal of Computer Science, vol. 16, no. 2, pp. 137-149, 2020. DOI:10.3844/jcssp.2020.137.149.
  30. S. Wattitham, T. Somboonwiwat, and S. Prombanpong, “Master production scheduling for the production planning in the pharmaceutical industry,” in Industrial Engineering, Management Science and Applications 2015, M. Gen, K. Kim, X. Huang, and Y. Hiroshi, Eds., Lecture Notes in Electrical Engineering, vol. 349, pp. 267-276, 2015. DOI:10.1007/978-3-662-47200-2_30.
  31. G.E. Vieira and P.C. Ribas, “A new multi-objective optimization method for master production scheduling problems using simulated annealing,” International Journal of Production Research, vol. 42, no. 21, pp. 4609-4622, 2004. DOI:10.1080/00207540410001733869
  32. J.H. Blackstone, “APICS Dictionary,” 14th ed., APICS, Chicago, 2014.
  33. S.M. Easa, “Resource leveling in construction by optimization,” Journal of Construction Engineering and Management, vol. 115, no. 2, pp. 302-316, 1989. DOI:10.1061/(ASCE)0733-9364(1989)115:2(302)
  34. M. Bandelloni, M. Tucci, and R. Rinaldi, “Optimal resource leveling using non-serial dynamic programming,” European Journal of Operational Research, vol. 78, no. 2, pp. 162-177, 1994. DOI:10.1016/0377-2217(94)90380-8
  35. J. Rieck, J. Zimmermann, and T. Gather, “Mixed-integer linear programming for resource leveling problems,” European Journal of Operational Research, vol. 221, no. 1, pp. 27-37, 2012. DOI:10.1016/j.ejor.2012.03.003
  36. J.P.U. Cadavid, S. Lamouri, B. Grabot, R. Pellerin, and A. Fortin, “Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0,” Journal of Intelligent Manufacturing, vol. 31, pp. 1531-1558, 2020. DOI:10.1007/s10845-019-01531-7
  37. E. Alpaydin, “Introduction to Machine Learning,” 2nd ed., MIT Press, Cambridge, 2010.
  38. R. Xu and D.C. Wunsch, “Clustering algorithms in biomedical research: a review,” IEEE Reviews in Biomedical Engineering, vol. 3, pp. 120-154, 2010. DOI:10.1109/rbme.2010.2083647
  39. A.L. Fred and A.K. Jain, “Combining multiple clusterings using evidence accumulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835-850, 2005. DOI:10.1109/TPAMI.2005.113
  40. A.K. Jain, M.N. Murty, and P.J. Flynn, “Data clustering: a review,” ACM Computing Surveys (CSUR), vol. 31, no. 3, pp. 264-323, 1999. DOI:10.1145/331499.331504
  41. T.W. Liao, “Clustering of time series data – a survey,” Pattern Recognition, vol. 38, no. 11, pp. 1857-1874, 2005. DOI:10.1016/j.patcog.2005.01.025
  42. I. Bose and X. Chen, “Detecting the migration of mobile service customers using fuzzy clustering,” Information & Management, vol. 52, no. 2, pp. 227-238, 2015. DOI:10.1016/j.im.2014.11.001
  43. S. Samoilenko and K.M. Osei-Bryson, “Representation matters: An exploration of the socio-economic impacts of ICT-enabled public value in the context of sub-Saharan economies,” International Journal of Information Management, vol. 49, pp. 69-85, 2019. DOI:10.1016/j.ijinfomgt.2019.03.006
  44. W.B. Xie, Y.L. Lee, C. Wang, D.B. Chen, and T. Zhou, “Hierarchical clustering supported by reciprocal nearest neighbors,” Information Sciences, vol. 527, pp. 279-292, 2020. DOI:10.1016/j.ins.2020.04.016
  45. J. Han, J. Pei, and M. Kamber, “Data mining: concepts and techniques,” Elsevier, Amsterdam, 2011.
  46. S. Landau, M. Leese, D. Stahl, and B.S. Everitt, “Cluster analysis,” Wiley, Hoboken, 2011.
  47. A.E. Ezugwu, A.M. Ikotun, O.O. Oyelade, L. Abualigah, J.O. Agushaka, C.I. Eke, and A.A. Akinyelu, “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,” Engineering Applications of Artificial Intelligence, vol. 110, p. 104743, 2022. DOI:10.1016/j.engappai.2022.104743
  48. S. Anand, P. Padmanabham, A. Govardhan, and R. H. Kulkarni, “An extensive review on data mining methods and clustering models for an intelligent transportation system,” Journal of Intelligent Systems, vol. 27, no. 2, pp. 263-273, 2018. DOI:10.1515/jisys-2016-0159
  49. E.S. Negara and R. Andryani, “A review on overlapping and non-overlapping community detection algorithms for social network analytics,” Far East Journal of Electronics and Communications, vol. 18, no. 1, pp. 1-27, 2018.
  50. A. Delgoshaei, A. Delgoshaei, and A. Ali, “Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review,” International Journal of Industrial Engineering Computations, vol. 10, no. 2, pp. 177-198, 2019. DOI:10.5267/j.ijiec.2018.8.002
  51. K.R. Kashwan and C.M. Velu, “Customer segmentation using clustering and data mining techniques,” International Journal of Computer Theory and Engineering, vol. 5, no. 6, pp. 856-861, 2013. DOI:10.7763/IJCTE.2013.V5.811
  52. D. Zakrzewska and J. Murlewski, “Clustering algorithms for bank customer segmentation,” in Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, H. Kwasnicka and M. Paprzycki, Eds., pp. 197-202, 2005. DOI: 10.1109/ISDA.2005.33.
  53. J.R. Fonseca and M.G. Cardoso, “Supermarket customers segments stability,” Journal of Targeting, Measurement and Analysis for Marketing, vol. 15, no. 4, pp. 210-221, 2007. DOI:10.1057/palgrave.jt.5750052
  54. D.C. Li, W.L. Dai, and W.T. Tseng, “A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business,” Expert Systems with Applications, vol. 38, no. 6, pp. 7186-7191, 2011. DOI:10.1016/j.eswa.2010.12.041
  55. X. Lei and H. Ouyang, “Image segmentation algorithm based on improved fuzzy clustering,” Cluster Computing, vol. 22, Suppl 6, pp. 13911-13921, 2019. DOI:10.1007/s10586-018-2128-9
  56. M. Subramaniyan, A. Skoogh, A. S. Muhammad, J. Bokrantz, B. Johansson, and C. Roser, “A generic hierarchical clustering approach for detecting bottlenecks in manufacturing,” Journal of Manufacturing Systems, vol. 55, pp. 143-158, 2020. DOI:10.1016/j.jmsy.2020.02.011
  57. H. Ahn and T. W. Chang, “A similarity-based hierarchical clustering method for manufacturing process models,” Sustainability, vol. 11, no. 9, p. 2560, 2019. DOI:10.3390/su11092560
DOI: https://doi.org/10.2478/mspe-2024-0037 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
Language: English
Page range: 401 - 408
Submitted on: Nov 1, 2023
Accepted on: Jul 1, 2024
Published on: Sep 5, 2024
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Ririn Diar Astanti, The Jin Ai, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution 4.0 License.