Clustering based heuristics for aligning Master Production Schedule and delivery schedule

• Autorzy: Astanti Ririn Diar , Ai The Jin

Identyfikatory

DOI

10.2478/mspe-2024-0037

• Języki publikacji: EN

Abstrakty

EN

Making a Master Production Schedule (MPS) is a very important activity for a manufacturing industry. This is due to the fact that MPS serves as an input for material and production planning. Between the years 2020 and 2022, there were significant fluctuations observed in container freight rates. As response, a lot of manufacturing industry focus on optimizing their container delivery schedule. Hence, there is a need for aligning the master production schedule with the delivery schedule. This paper presents the development of a novel heuristic approach to address problems with the creation of MPS. Specifically, the focus is on the situation where container delivery schedules are prearranged and serve as a main input for creating the MPS. There are two objective functions that are going to be reached: 1) minimize the total number of product variations or Stock Keeping Units (SKU) per month; and 2) minimize the number of SKU per container. The proposed heuristic approach uses the similarity concept to group objects in a clustering technique. It is then implemented in a real-world case of a furniture manufacturing company. Further results were obtained and then compared to the heuristic technology that had previously been used by business entities. The results show that the number of product variations (SKU) that must be performed per month is 10% lower than that of the existing heuristic. In addition, the ratio of SKU variations per container is 9% lower than that of the existing heuristic. The time required to complete the task of creating MPS is less than one minute, as opposed to the one working day required by the company’s existing heuristic.

Słowa kluczowe

EN

clustering technique container; heuristics; master production schedule; similarity concept

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2024
• Tom: nr 3 (32)
• Strony: 401--408
• Opis fizyczny: Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Astanti Ririn Diar

• Department of Industrial Engineering Universitas Atma Jaya Yogyakarta, Indonesia

• https://orcid.org/0000-0001-5234-0283

autor

Ai The Jin

• Department of Industrial Engineering Universitas Atma Jaya Yogyakarta, Indonesia

• https://orcid.org/0000-0002-5946-5027

Bibliografia

• [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.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).