Optimizing Production Planning Decisions with a Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation
DOI:
https://doi.org/10.35335/emod.v15i1.38Keywords:
Production planning, Optimization, Hybrid approach, Grid partitioning, Association rule miningAbstract
This research focuses on optimizing production planning decisions using a hybrid grid partitioning and rough set approach for fuzzy rule generation. The aim is to address the challenges associated with uncertainty, complex relationships, and the need for a systematic methodology tailored specifically for production planning. The proposed approach integrates grid partitioning, rough set theory, and fuzzy rule generation to provide decision-makers with a comprehensive framework for generating robust fuzzy rules. The mathematical formulation formulates the optimization problem by considering input variables such as demand and resource availability, output variables representing production quantities, fuzzy membership functions to model linguistic variables, and fuzzy rules to capture relationships. The objective is to minimize deviations between actual and desired outputs while satisfying relevant constraints. A numerical example is presented to illustrate the application of the proposed approach. The results demonstrate improved decision-making, enhanced operational efficiency, and the applicability of the approach to various production planning scenarios. However, limitations in terms of data quality, generalizability to complex production systems, and computational complexity should be considered. Future research should address these limitations and explore real-time adaptability to further enhance the effectiveness of the proposed approach. Overall, this research contributes to the advancement of production planning methodologies by providing a structured framework for handling uncertainty, capturing complex relationships, and optimizing production planning decisions.
References
Abraham, A. (2005). Rule‐Based expert systems. Handbook of measuring system design.
Acuña-Carvajal, F., Pinto-Tarazona, L., López-Ospina, H., Barros-Castro, R., Quezada, L., & Palacio, K. (2019). An integrated method to plan, structure and validate a business strategy using fuzzy DEMATEL and the balanced scorecard. Expert systems with applications, 122, 351-368.
Adebanjo, D., Laosirihongthong, T., & Samaranayake, P. (2016). Prioritizing lean supply chain management initiatives in healthcare service operations: a fuzzy AHP approach. Production Planning & Control, 27(12), 953-966.
Adrodegari, F., Bacchetti, A., Pinto, R., Pirola, F., & Zanardini, M. (2015). Engineer-to-order (ETO) production planning and control: an empirical framework for machinery-building companies. Production Planning & Control, 26(11), 910-932.
Badiru, A. B. (1996). Project management in manufacturing and high technology operations. John Wiley & Sons.
Bagshaw, K. B. (2019). A Review of Quantitative Analysis (QA) in Production Planning Decisions Using the Linear Programming Model. American Journal of Operations Research, 9(06), 255.
Banos, R., Manzano-Agugliaro, F., Montoya, F. G., Gil, C., Alcayde, A., & Gómez, J. (2011). Optimization methods applied to renewable and sustainable energy: A review. Renewable and sustainable energy reviews, 15(4), 1753-1766.
Behnamian, J., & Ghomi, S. F. (2011). Hybrid flowshop scheduling with machine and resource-dependent processing times. Applied Mathematical Modelling, 35(3), 1107-1123.
Bell, J., Crick, D., & Young, S. (2004). Small firm internationalization and business strategy: an exploratory study of ‘knowledge-intensive’and ‘traditional’manufacturing firms in the UK. International Small business journal, 22(1), 23-56.
Bilgen, B., & Çelebi, Y. (2013). Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Annals of Operations Research, 211, 55-82.
Cai, Y. P., Huang, G. H., Yang, Z. F., & Tan, Q. (2009). Identification of optimal strategies for energy management systems planning under multiple uncertainties. Applied Energy, 86(4), 480-495.
Cohen, M. A., & Kunreuther, H. (2007). Operations risk management: overview of Paul Kleindorfer's contributions. Production and Operations Management, 16(5), 525-541.
Cupek, R., Ziebinski, A., Huczala, L., & Erdogan, H. (2016). Agent-based manufacturing execution systems for short-series production scheduling. Computers in Industry, 82, 245-258.
Dai, M., Tang, D., Giret, A., & Salido, M. A. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59, 143-157.
Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418-429.
Das, S., & Patnaik, A. (2015). Production planning in the apparel industry. In Garment manufacturing technology (pp. 81-108). Woodhead Publishing.
Dimitriou, L., Tsekeris, T., & Stathopoulos, A. (2008). Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C: Emerging Technologies, 16(5), 554-573.
Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., ... & Kellens, K. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP annals, 61(2), 587-609.
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of manufacturing systems, 39, 79-100.
Feyzioglu, O., & Buyukozkan, G. (2006). Evaluation of new product development projects using artificial intelligence and fuzzy logic. In International conference on knowledge mining and computer science (Vol. 11, pp. 183-189).
Fung, S. H., Cheung*, C. F., Lee, W. B., & Kwok, S. K. (2005). A virtual warehouse system for production logistics. Production Planning & Control, 16(6), 597-607.
Galli, T. (2013). Fuzzy logic based software product quality model for execution tracing.
Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25, 849-866.
Georgiadis, G. P., Elekidis, A. P., & Georgiadis, M. C. (2019). Optimization-based scheduling for the process industries: from theory to real-life industrial applications. Processes, 7(7), 438.
Georgiadis, P., & Michaloudis, C. (2012). Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis. European Journal of Operational Research, 216(1), 94-104.
Giret, A., Trentesaux, D., & Prabhu, V. (2015). Sustainability in manufacturing operations scheduling: A state of the art review. Journal of Manufacturing Systems, 37, 126-140.
Gnoni, M. G., Iavagnilio, R., Mossa, G., Mummolo, G., & Di Leva, A. (2003). Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. International Journal of production economics, 85(2), 251-262.
Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European journal of operational research, 129(1), 1-47.
Gunasekaran, A., Irani, Z., Choy, K. L., Filippi, L., & Papadopoulos, T. (2015). Performance measures and metrics in outsourcing decisions: A review for research and applications. International Journal of Production Economics, 161, 153-166.
Gungor, A., & Gupta, S. M. (1999). Issues in environmentally conscious manufacturing and product recovery: a survey. Computers & Industrial Engineering, 36(4), 811-853.
Gürsel, G. (2016). Healthcare, uncertainty, and fuzzy logic. Digital Medicine, 2(3), 101.
Hans, E. W., Van Houdenhoven, M., & Hulshof, P. J. (2011). A framework for healthcare planning and control. In Handbook of healthcare system scheduling (pp. 303-320). Boston, MA: Springer US.
Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., ... & Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161-193.
Heikkilä, J. (2002). From supply to demand chain management: efficiency and customer satisfaction. Journal of operations management, 20(6), 747-767.
Isaksson, C., Dunham, M. H., & Hahsler, M. (2012). SOStream: Self organizing density-based clustering over data stream. In Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings 8 (pp. 264-278). Springer Berlin Heidelberg.
Jamalnia, A., & Soukhakian, M. A. (2009). A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning. Computers & Industrial Engineering, 56(4), 1474-1486.
Jensen, R., & Shen, Q. (2007). Fuzzy-rough sets assisted attribute selection. IEEE Transactions on fuzzy systems, 15(1), 73-89.
Jeon, S. M., & Kim, G. (2016). A survey of simulation modeling techniques in production planning and control (PPC). Production Planning & Control, 27(5), 360-377.
Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2013). A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Applied Soft Computing, 13(10), 4133-4147.
Kumar, S. A., & Suresh, N. (2006). Production and operations management. New Age International.
Li, J., Gonzalez, M., & Zhu, Y. (2009). A hybrid simulation optimization method for production planning of dedicated remanufacturing. International Journal of Production Economics, 117(2), 286-301.
Lin, L., & Gen, M. (2018). Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications. International Journal of Production Research, 56(1-2), 193-223.
Liu, Z., Chua, D. K. H., & Yeoh, K. W. (2011). Aggregate production planning for shipbuilding with variation-inventory trade-offs. International Journal of Production Research, 49(20), 6249-6272.
Maccarthy, B. L., & Liu, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. The International Journal of Production Research, 31(1), 59-79.
Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), 1919-1930.
Martínez, L., Ruan, D., Herrera, F., Herrera-Viedma, E., & Wang, P. P. (2009). Linguistic decision making: Tools and applications. Information Sciences, 179(14), 2297-2298.
Méndez, C. A., Cerdá, J., Grossmann, I. E., Harjunkoski, I., & Fahl, M. (2006). State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & chemical engineering, 30(6-7), 913-946.
Meng, D., Zhang, X., & Qin, K. (2011). Soft rough fuzzy sets and soft fuzzy rough sets. Computers & mathematics with applications, 62(12), 4635-4645.
Mitra, S., & Hayashi, Y. (2000). Neuro-fuzzy rule generation: survey in soft computing framework. IEEE transactions on neural networks, 11(3), 748-768.
Nguyen, S., Mei, Y., & Zhang, M. (2017). Genetic programming for production scheduling: a survey with a unified framework. Complex & Intelligent Systems, 3, 41-66.
Øhrn, A. (2000). Discernibility and Rough Sets in Medicine: Tools and Applications (Doctoral dissertation, Norwegian University of Science and Technology, Trondheim, Norway).
Øhrn, A. (2000). Discernibility and Rough Sets in Medicine: Tools and Applications (Doctoral dissertation, Norwegian University of Science and Technology, Trondheim, Norway).
Osiro, L., Lima-Junior, F. R., & Carpinetti, L. C. R. (2014). A fuzzy logic approach to supplier evaluation for development. International Journal of Production Economics, 153, 95-112.
Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research, 1-30.
Qin, H., Fan, P., Tang, H., Huang, P., Fang, B., & Pan, S. (2019). An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Computers & Industrial Engineering, 128, 458-476.
Quek, C., Pasquier, M., & Lim, B. (2009). A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour. Expert Systems with applications, 36(10), 12167-12178.
Ramanathan, U., Subramanian, N., & Parrott, G. (2017). Role of social media in retail network operations and marketing to enhance customer satisfaction. International Journal of Operations & Production Management.
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.
Robert S. Kaplan, & Robin Cooper. (1998). Cost & effect: using integrated cost systems to drive profitability and performance. Harvard Business Press.
Safaei, A. S., Moattar Husseini, S. M., Z.-Farahani, R., Jolai, F., & Ghodsypour, S. H. (2010). Integrated multi-site production-distribution planning in supply chain by hybrid modelling. International Journal of Production Research, 48(14), 4043-4069.
Schoemaker, P. J. (2004). Forecasting and scenario planning: the challenges of uncertainty and complexity. Blackwell handbook of judgment and decision making, 274-296.
Shahrabi, J., Hadavandi, E., & Asadi, S. (2013). Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series. Knowledge-Based Systems, 43, 112-122.
Shapiro, J. F. (1993). Mathematical programming models and methods for production planning and scheduling. Handbooks in operations research and management science, 4, 371-443.
Shen, Q., & Jensen, R. (2004). Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern recognition, 37(7), 1351-1363.
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.
Shrestha, R. R., Bárdossy, A., & Rode, M. (2007). A hybrid deterministic–fuzzy rule based model for catchment scale nitrate dynamics. Journal of Hydrology, 342(1-2), 143-156.
Shrouf, F., Ordieres-Meré, J., García-Sánchez, A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197-207.
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.
Subramanian, N., & Ramanathan, R. (2012). A review of applications of Analytic Hierarchy Process in operations management. International Journal of Production Economics, 138(2), 215-241.
Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems–a review. Renewable and sustainable energy reviews, 48, 585-607.
Sun, C. T. (1994). Rule-base structure identification in an adaptive-network-based fuzzy inference system. IEEE Transactions on Fuzzy Systems, 2(1), 64-73.
Sundaramoorthy, A., & Maravelias, C. T. (2011). Computational study of network-based mixed-integer programming approaches for chemical production scheduling. Industrial & Engineering Chemistry Research, 50(9), 5023-5040.
Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in industry, 81, 82-95.
Torabi, S. A., Sahebjamnia, N., Mansouri, S. A., & Bajestani, M. A. (2013). A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Applied Soft Computing, 13(12), 4750-4762.
Tunis, S. R., Stryer, D. B., & Clancy, C. M. (2003). Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. Jama, 290(12), 1624-1632.
Uzsoy, R., Lee, C. Y., & Martin-Vega, L. A. (1992). A review of production planning and scheduling models in the semiconductor industry part I: system characteristics, performance evaluation and production planning. IIE transactions, 24(4), 47-60.
Uzsoy, R., Lee, C. Y., & Martin-Vega, L. A. (1992). A review of production planning and scheduling models in the semiconductor industry part I: system characteristics, performance evaluation and production planning. IIE transactions, 24(4), 47-60.
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.
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 & Industrial Engineering, 129, 354-367.
Vluymans, S. (2019). Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods (Vol. 107, p. 236). Heidelberg: Springer.
Walczak, B., & Massart, D. L. (1999). Rough sets theory. Chemometrics and intelligent laboratory systems, 47(1), 1-16.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.
Wang, H., Xu, Z., & Pedrycz, W. (2017). An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities. Knowledge-Based Systems, 118, 15-30.
Wu, C., & Barnes, D. (2016). Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach. Production planning & control, 27(16), 1356-1372.
Zhai, L. Y., Khoo, L. P., & Fok, S. C. (2002). Feature extraction using rough set theory and genetic algorithms—an application for the simplification of product quality evaluation. Computers & Industrial Engineering, 43(4), 661-676.
Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2009). Design concept evaluation in product development using rough sets and grey relation analysis. Expert systems with applications, 36(3), 7072-7079.
Zheng, X. L., & Wang, L. (2016). A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(5), 790-800.
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