Decision Model and Industry Optimization in Production: A Systematic Literature review
Keywords:
Decision Models and Optimization, Production, Systematic Literature review, PRISMA
Abstract
This article aims to discover the modeling and optimization options relevant to production-related industrial sectors. PRISMA (Preferred Reporting Items for Systematic Review and Meta-analyses) is a preferred submission method with inclusive and exclusive criteria, one of the bases for the selection made from the ScienceDirect index database only for 2018, 2019, 2020, 2021, and 2022 is understanding decision models and optimization with production keywords. As a result, 823 articles were converted to 100 articles and 16 articles adjacent to the final selection of 10 articles were used. The detailed results of the list of journals used as the most common references from the journal Computers & Industrial Engineering are used to identify the results of this publication in more detail. The most common research model is the adaptive decision model, and the most common research methodology is quantitative. Advanced research with sophisticated applications from the latest technologies such as AI (Machine Learning) to (Deep Learning), this wider and varied use of data includes unstructured or unorganized data so that new concepts will lead to new decision model system innovations, still relatively little additional research that can be used in the realm of production in assembly, process quality, and the environment.
References
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Ao, Y., Zhang, H., & Wang, C. (2019). Research of an integrated decision model for production scheduling and maintenance planning with economic objective. Computers and Industrial Engineering, 137. https://doi.org/10.1016/j.cie.2019.106092
Avram, C., Gligor, A., & Avram, L. (2020). A formal model based automated decision making. Procedia Manufacturing, 46, 573–579. https://doi.org/10.1016/j.promfg.2020.03.083
Burggräf, P., Adlon, T., Dackweiler, J., Bröhl, F., & Fölling, C. (2022). Adaptive Remanufacturing - Decision Model for the Intelligent Maintenance of Production Resources. Procedia CIRP, 105, 219–224. Elsevier B.V. https://doi.org/10.1016/j.procir.2022.02.036
Chien, C. F., Dou, R., & Fu, W. (2018). Strategic capacity planning for smart production: Decision modeling under demand uncertainty. Applied Soft Computing Journal, 68, 900–909. https://doi.org/10.1016/j.asoc.2017.06.001
De, S. K., Roy, B., & Bhattacharya, K. (2022). Solving an EPQ model with doubt fuzzy set: A robust intelligent decision-making approach. Knowledge-Based Systems, 235.
https://doi.org/10.1016/j.knosys.2021.107666
Dwilaga, A. T. (2023a). Implementasi Model Artificial Intelligence dalam Warehouse: Systematic Literature Review. JUSTI (Jurnal Sistem Dan Teknik Industri), 3(2), 253–261. https://doi.org/10.30587/justicb.v3i2.5250
Dwilaga, A. T. (2023b). The Use of Artificial Intelligence with Mechanisms in Robots in the Linkage of the Manufacturing Industry: Systematic Literature Review. SITEKIN: Jurnal Sains, Teknologi Dan Industri, 20(2), 544–552. https://doi.org/10.24014/sitekin.v20i2.21730
Ebrahimi, B., Tavana, M., Toloo, M., & Charles, V. (2020). A novel mixed binary linear DEA model for ranking decision-making units with preference information. Computers and Industrial Engineering, 149. https://doi.org/10.1016/j.cie.2020.106720
Fidan, Y., Lüder, A., Meixner, K., Baumann, L., & Arlinghaus, J. C. (2021). Decision Support for Frugal Products and Production Systems based on Product-Process-Resource-Skill & Variability Models. Procedia CIRP, 104, 1619–1625.
https://doi.org/10.1016/j.procir.2021.11.273
Guo, F., Zhou, X., Liu, J., Zhang, Y., Li, D., & Zhou, H. (2019). A reinforcement learning decision model for online process parameters optimization from offline data in injection molding. Applied Soft Computing Journal, 85. https://doi.org/10.1016/j.asoc.2019.105828
Liu, W., Deng, K., Wei, H., Zhao, P., Li, J., & Zhang, Y. (2021). A decision-making model for comparing the energy demand of additive-subtractive hybrid manufacturing and conventional subtractive manufacturing based on life cycle method. Journal of Cleaner Production, 311. https://doi.org/10.1016/j.jclepro.2021.127795
Longo, F., Padovano, A., Cimmino, B., & Pinto, P. (2021). Towards a mass customization in the fashion industry: An evolutionary decision aid model for apparel product platform design and optimization. Computers & Industrial Engineering, 162.
https://doi.org/10.1016/j.cie.2021.107742
Materi, S., D’Angola, A., & Renna, P. (2020). A dynamic decision model for energy-efficient scheduling of manufacturing system with renewable energy supply. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122028
Ngo, Q. H., Schmitt, S., Ellerich, M., & Schmitt, R. H. (2020). Artificial intelligence based decision model for a quality oriented production ramp-up. Procedia CIRP, 88, 521–526. Elsevier B.V. https://doi.org/10.1016/j.procir.2020.05.090
Pamucar, D., Lordache, M., Daveci, M., Schitea, D., & Lordache, L. (2021). A new hybrid fuzzy multi-criteria decision methodology model for prioritizing the alternatives of the hydrogen bus development: A case study from Romania. International Journal of Hydrogen Energy, 46(57), 29616–29637. https://doi.org/10.1016/j.ijhydene.2020.10.172
Petroodi, S. E. H., Thevenin, S., Kovalen, S., & Dolgui, A. (2023). Markov decision process for multi-manned mixed-model assembly lines with walking workers. International Journal of Production Economics, 225. https://doi.org/10.1016/j.ijpe.2022.108661
Saez, M., Barton, K., Maturana, F., & Tilbury, D. M. (2022). Modeling framework to support decision making and control of manufacturing systems considering the relationship between productivity, reliability, quality, and energy consumption. Journal of Manufacturing Systems, 62, 925–938. https://doi.org/10.1016/j.jmsy.2021.03.011
Sarwar, M., Ali, G., & Chaudhry, N. R. (2023). Decision-making model for failure modes and effect analysis based on rough fuzzy integrated clouds. Applied Soft Computing, 136. https://doi.org/10.1016/j.asoc.2023.110148
Sitanggang, R., & Sibagariang, S. (2019). MODEL PENGAMBILAN KEPUTUSAN DENGAN TEKNIK METODE PROFILE MATCHING. Journal of Computer Engineering System and Science, 4(1), 44–50.
Sohr, A., Listl, F. G., Ecker, K., Fischer, J., Wehrstedt, J. C., & Weyrich, M. (2022). Decision Modeling for an ISA-95 based Production Ontology. IFAC-PapersOnLine, 55(10), 371–376. https://doi.org/10.1016/j.ifacol.2022.09.421
Sun, X., Opulencia, M. J. C., Alexandrovich, T. P., Khan, A., Algarni, M., & Abdelrahman, A. (2022). Modeling and optimization of vegetable oil biodiesel production with heterogeneous nano catalytic process: Multi-layer perceptron, decision regression tree, and K-Nearest Neighbor methods. Environmental Technology and Innovation, 27.
https://doi.org/10.1016/j.eti.2022.102794
Terzi, M., Ouazene, yassine, Yalaoui, A., & Yalaoui, F. (2023). Lot-sizing and pricing decisions under attraction demand models and multi-channel environment: New efficient formulations. Operations Research Perspectives, 10. https://doi.org/10.1016/j.orp.2023.100269
Tsai, W. H., & Jhong, S. Y. (2019). Production decision model with carbon tax for the knitted footwear industry under activity-based costing. Journal of Cleaner Production, 207, 1150–1162. https://doi.org/10.1016/j.jclepro.2018.09.104
Tsai, W.-H., Lu, Y.-H., & Hsieh, chu-L. (2022). Comparison of Production Decision-Making Models Under Carbon tax and Carbon rights Trading. Journal of Cleaner Production, 379. https://doi.org/10.1016/j.jclepro.2022.134462
Vieira, M., Pinto-Varela, T., & Barbosa-Póvoa, A. P. (2019). A model-based decision support framework for the optimisation of production planning in the biopharmaceutical industry. Computers and Industrial Engineering, 129, 354–367.
https://doi.org/10.1016/j.cie.2019.01.045
Wikarek, J., Sitek, P., & Nielsen, P. (2019). Model of decision support for the configuration of manufacturing system. IFAC-PapersOnLine, 52(13), 826–831. Elsevier B.V.
https://doi.org/10.1016/j.ifacol.2019.11.232
Xiang, W., Xue, S., Qin, S., Xiao, L., Liu, F., & Yi, Z. (2018). Development of a multi-criteria decision making model for evaluating the energy potential of Miscanthus germplasms for bioenergy production. Industrial Crops and Products, 125, 602–615.
https://doi.org/10.1016/j.indcrop.2018.09.050
Zhang, W., Hou, L., & Jiao, R. J. (2021). Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach. Computers and Industrial Engineering, 161. https://doi.org/10.1016/j.cie.2021.107616
Zomparelli, F., Petrillo, L., Salvo, B. Di, & Petrillo, A. (2018). Re-engineering and Relocation of manufacturing process through a simulative and multicriteria decision model. IFAC-PapersOnLine, 51(11), 1649–1654. https://doi.org/10.1016/j.ifacol.2018.08.220
