Efficient Production Planning Optimization: A Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Manufacturing Systems
DOI:
https://doi.org/10.35335/emod.v15i1.39Keywords:
Production planning optimization, Hybrid approach, Grid partitioning, Rough set theory, Fuzzy rule generationAbstract
Efficient production planning is crucial for optimizing resource allocation and improving productivity in manufacturing systems. This research proposes a hybrid grid partitioning and rough set approach for fuzzy rule generation to address the challenges of production planning optimization. The integration of grid partitioning, rough set theory, and fuzzy logic enables a comprehensive analysis of input variables, handling uncertainty, and generating fuzzy rules for decision-making. The grid partitioning technique divides the input space into discrete cells, simplifying the optimization process. Rough set analysis within each cell identifies relevant features and dependencies, enhancing the understanding of the production system's behavior. The generated fuzzy rules capture linguistic relationships between input and output variables, facilitating context-aware decision-making. The evaluation and optimization of the rule set ensure the quality and effectiveness of the decision-making process. The proposed approach offers practical benefits, such as improved resource utilization, cost reduction, and enhanced productivity in manufacturing systems. However, the research acknowledges limitations in terms of the simplified scenario, data availability, computational complexity, parameter sensitivity, and generalizability. Further research is needed to validate and refine the framework in diverse industrial settings. The findings contribute to the advancement of production planning optimization and provide valuable insights for researchers and industry practitioners.
References
Adriaenssens, V., De Baets, B., Goethals, P. L., & De Pauw, N. (2004). Fuzzy rule-based models for decision support in ecosystem management. Science of the Total Environment, 319(1-3), 1-12.
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11).
Aytug, H., Lawley, M. A., McKay, K., Mohan, S., & Uzsoy, R. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161(1), 86-110.
Bitran, G. R., & Tirupati, D. (1993). Hierarchical production planning. Handbooks in operations research and management science, 4, 523-568.
Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability engineering & system safety, 50(2), 203-213.
Celikyilmaz, A., & Turksen, I. B. (2009). Modeling uncertainty with fuzzy logic. Studies in fuzziness and soft computing, 240, 149-215.
Chai, J., & Ngai, E. W. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140, 112903.
Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert systems with applications, 40(10), 3872-3885.
Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. Cirp Annals, 63(2), 773-796.
Das, B., Venkatadri, U., & Pandey, P. (2014). Applying lean manufacturing system to improving productivity of airconditioning coil manufacturing. The International Journal of Advanced Manufacturing Technology, 71, 307-323.
Ertay, T., Ruan, D., & Tuzkaya, U. R. (2006). Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Information sciences, 176(3), 237-262.
Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied soft computing, 93, 106282.
Gholamian, N., Mahdavi, I., & Tavakkoli-Moghaddam, R. (2016). Multi-objective multi-product multi-site aggregate production planning in a supply chain under uncertainty: fuzzy multi-objective optimisation. International Journal of Computer Integrated Manufacturing, 29(2), 149-165.
Grossmann, I. (2005). Enterprise‐wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7), 1846-1857.
Gunasekaran, A. (1999). Agile manufacturing: a framework for research and development. International journal of production economics, 62(1-2), 87-105.
Hassanien, A. E., Abraham, A., Peters, J. F., Schaefer, G., & Henry, C. (2009). Rough sets and near sets in medical imaging: A review. IEEE Transactions on Information Technology in Biomedicine, 13(6), 955-968.
Henriksson, V., & Hult, M. (2015). Rationalizing freeform architecture-Surface discretization and multi-objective optimization.
Hirano, S., & Tsumoto, S. (2003). Rough set theory and granular computing (Vol. 125). M. Inuiguchi (Ed.). Berlin: Springer.
Huang, Y. C., & Sun, H. C. (2013). Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic. IEEE Transactions on Dielectrics and Electrical Insulation, 20(3), 974-981.
Jensen, R., & Shen, Q. (2008). Computational intelligence and feature selection: rough and fuzzy approaches.
Kaplan, R. S. (1992). Measuring manufacturing performance: a new challenge for managerial accounting research (pp. 284-306). Springer US.
Kostek, B. (2013). Soft computing in acoustics: applications of neural networks, fuzzy logic and rough sets to musical acoustics (Vol. 31). Physica.
Lazar, A. (2002). Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets. In Heuristic and optimization for knowledge discovery (pp. 263-278). IGI Global.
Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4-5), 715-727.
Lieberman, C., Willcox, K., & Ghattas, O. (2010). Parameter and state model reduction for large-scale statistical inverse problems. SIAM Journal on Scientific Computing, 32(5), 2523-2542.
Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), 1919-1930.
McBratney, A. B., & Odeh, I. O. (1997). Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma, 77(2-4), 85-113.
Mitra, S., Banka, H., & Pedrycz, W. (2006). Rough–fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(4), 795-805.
Monostori, L. (2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Engineering applications of artificial intelligence, 16(4), 277-291.
Mula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International journal of production economics, 103(1), 271-285.
Nunes, I. L. (2010). Handling human-centered systems uncertainty using fuzzy logics-A review. The Ergonomics Open Journal, 3(1).
Omar, A. S., Waweru, M., & Rimiru, R. (2015). A literature survey: Fuzzy logic and qualitative performance evaluation of supply chain management. Int. J. Engineeirng Sci, 4(5), 56-63.
Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & operations research, 37(4), 668-683.
Pandove, D., Goel, S., & Rani, R. (2018). Systematic review of clustering high-dimensional and large datasets. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(2), 1-68.
Rahmani, D., Ramezanian, R., Fattahi, P., & Heydari, M. (2013). A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 37(20-21), 8957-8971.
Rajesh, T., & Malar, R. S. M. (2013, July). Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images. In International Conference on Advanced Nanomaterials & Emerging Engineering Technologies (pp. 240-244). IEEE.
Rissino, S., & Lambert-Torres, G. (2009). Rough set theory—fundamental concepts, principals, data extraction, and applications. In Data mining and knowledge discovery in real life applications. IntechOpen.
Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(4), 563-577.
Shi, J., Zhang, G., & Sha, J. (2011). Optimal production planning for a multi-product closed loop system with uncertain demand and return. Computers & Operations Research, 38(3), 641-650.
Slim, H., & Nadeau, S. (2020). A proposition for combining rough sets, fuzzy logic and FRAM to address methodological challenges in safety management: A discussion paper. Safety, 6(4), 50.
Song, Y. H., & Johns, A. T. (1997). Applications of fuzzy logic in power systems. Part 1: General introduction to fuzzy logic. Power Engineering Journal, 11(5), 219-222.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
Thangavel, K., & Pethalakshmi, A. (2009). Dimensionality reduction based on rough set theory: A review. Applied soft computing, 9(1), 1-12.
Torabi 1, S. A., & Hassini, E. (2009). Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach. International Journal of Production Research, 47(19), 5475-5499.
Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: a framework of strategies, policies, and methods. Journal of scheduling, 6, 39-62.
Vluymans, S., D'eer, L., Saeys, Y., & Cornelis, C. (2015). Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey. Fundam. Informaticae, 142(1-4), 53-86.
Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer-aided design, 59, 1-14.
Xie, Z., Du, L., Lv, X., Wang, Q., Huang, J., Fu, T., & Li, S. (2020). Evaluation and analysis of battery technologies applied to grid-level energy storage systems based on rough set theory. Transactions of Tianjin University, 26(3), 228-235.
Yang, X. S. (2020). Nature-inspired optimization algorithms. Academic Press.
Yang, Y., & Soh, C. K. (2000). Fuzzy logic integrated genetic programming for optimization and design. Journal of Computing in Civil Engineering, 14(4), 249-254.
Zhang, L., Liu, L., Yang, X. S., & Dai, Y. (2016). A novel hybrid firefly algorithm for global optimization. PloS one, 11(9), e0163230.
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Tao Song-slim Shi, Vieral wu Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License.
