Abstract
In recent years, the iron and steel industry’s operation condition has been continuously worsening, profitability reducing, thus the product mix decision (PMD) for the iron and steel enterprises became research focus to reduce manufacturing costs and maximize profits. Taking into account unit-level, batch-level and product-level cost, an integrated model conducting product mix decision for steelmaking, continuous casting and hot rolling (SM-CC-HR) process was proposed in this paper. A numerical example was presented to illustrate data input, solution method and result analysis. By comparing the model with two traditional ones, it was showed that the model attained higher profit and smoother implementation, because it traced the cost appropriately and effectively reduced the volume of left slabs in manufacturing processes and that of left steel products after order-delivery.
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References
Balakrishnan J, Chun-hung Cheng (2000) Theory of constraints and linear programming: a re-examination. Int J Prod Res 38(6):1459–1463
Baykasoglu A (2009) Quantifying machine flexibility. Int J Prod Res 47(15):4109–4123
Bhattacharya A, Vasant P (2007) Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP. Eur J Oper Res 177:55–70
Bih-Ru Leaa, Fredendallb LD (2002) The impact of management accounting, product structure, product mix algorithm, and planning horizon on manufacturing performance. Int J Prod Econ 79:279–299
Bo-xiong Lan, Nan Jiang, Yan Zheng (2010) A heuristic lot-sizing algorithm for large scale lot-sizing problem (in Chinese). Chin J Manag Sci 18(2):81–88
Hu-sheng Lu, Sen Wu, Bing Liu, Zhen-gang Liu (2004) Maximum profit flow algorithm for optimization of production planning of steel works (in Chinese). Iron Steel 39(3):74–77
Karakas E, Koyuncu M, Erol R, Kokangul A (2010) Fuzzy programming for optimal product mix decisions based on expanded ABC approach. Int J Prod Res 48(3):729–744
Kee R (1995) Integrating activity-based costing with the theory of constraints to enhance production-related decision-making. Account Horiz 9(4):48–61
Kee R, Schmidt C (2000) A comparative analysis of utilizing activity-based costing and the theory of constraints for making product-mix decisions. Int J Prod Econ 3(63):1–17
Li-xin Tang (2005) Intelligent optimization-based production planning and scheduling in iron and steel industry (in Chinese). Chin J Manag 2(3):263–267
Ren-qian Zhang, Yi-yong Xiao (2007) A research on agent-based heuristic production planning of product mix considering build-to-order (in Chinese). Syst Eng Theory Pract 10:54–62
Souren R, Ahn H, Schmitz C (2005) Optimal product mix decisions based on the theory of constraints? Exposing rarely emphasized premises of throughput accounting. Int J Prod Res 43(2):361–374
Tsaia W-H, Shih-Jieh Hung (2009) A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure. Int J Prod Res 47(18):4991–5017
Weeks K, Gao H, Alidaeec B, Rana DS (2010) An empirical study of impacts of production mix, product route efficiencies on operations performance and profitability: a reverse logistics approach. Int J Prod Res 48(4):1087–1104
Wen-Hsien Tsaia, Kuob L, Linc TW, Yi-Chen Kuod, Yu-Shan Shena (2010) Price elasticity of demand and capacity expansion features in an enhanced ABC product-mix decision model. Int J Prod Res 48(21):6387–6416
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Lu, Hs., Lv, Gq. (2013). An Optimal Product Mix Decision Model Considering Unit-Batch-Product Level Cost for Steel Plant. In: Dou, R. (eds) Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33012-4_16
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DOI: https://doi.org/10.1007/978-3-642-33012-4_16
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