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2023 | 14 | nr 4 | 56--70
Tytuł artykułu

A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied. (original abstract)
Rocznik
Tom
14
Numer
Strony
56--70
Opis fizyczny
Twórcy
  • Universita Politecnica delle Marche, Italy
  • Universita Politecnica delle Marche, Italy
  • Universita Politecnica delle Marche, Italy
  • Universita Politecnica delle Marche, Italy
Bibliografia
  • Agrawal, R., Srikant, R. (1994). Fast Algorithms for Mining Association Rules.
  • Babor, M., Paquet-Durand, O., Kohlus, R., Hitzmann, B. (2023). Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time. Scientific Reports, 13(1). DOI: 10.1038/s41598-022-26866-9.
  • Chen, Y. (2019). Research on Resource Allocation Optimization of Information Management System Based on Big data Association Mining. Journal of Physics: Conference Series, 1345(2). DOI: 10.1088/1742-6596/1345/2/022073.
  • Fani, V., Antomarioni, S., Bandinelli, R., Bevilacqua, M. (2023). Data-driven decision support tool for production planning: a framework combining association rules and simulation. Computers in Industry, 144. DOI: 10.1016/j.compind.2022.103800.
  • Farizal, Joelian, A. (2020). Engine replacement scheduling optimization using Data Mining. Journal of Physics: Conference Series, 1500(1). DOI: 10.1088/1742-6596/1500/1/012111.
  • Habib Zahmani, M., Atmani, B. (2021). Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation. Journal of Scheduling, 24(2), 175-196. DOI: 10.1007/s10951-020-00664-5.
  • Jiménez-Pastor, A., Petkovšek, M. (2023). The factorial-basis method for finding definite-sum solutions of linear recurrences with polynomial coefficients. Journal of Symbolic Computation, 117, 15-50. DOI: 10.1016/j.jsc.2022.11.002.
  • Nasiri, M.M., Salesi, S., Rahbari, A., Salmanzadeh Meydani, N., Abdollai, M. (2019). A data mining approach for population-based methods to solve the JSSP. Soft Computing, 23(21), 11107-11122. DOI: 10.1007/s00500-018-3663-2.
  • Qiu, Y., Sawhney, R., Zhang, C., Chen, S., Zhang, T., Lisar, V.G., Jiang, K., Ji, W. (2019). Data mining-based disturbances prediction for job shop scheduling. Advances in Mechanical Engineering, 11(3). DOI: 10.1177/1687814019838178.
  • Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack. Journal of Open Source Software, 3(24), 638. DOI: 10.21105/joss.00638.
  • Troncoso-García, A.R., Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A. (2023). A new approach based on association rules to add explainability to time series forecasting models. Information Fusion. DOI: 10.1016/j.inffus.2023.01.021.
  • Wang, L., Lin, B., Chen, R., Lu, K.H. (2022). Using data mining methods to develop manufacturing production rule in IoT environment. Journal of Supercomputing, 78(3), 4526-4549. DOI: 10.1007/s11227-021-04034-6.
  • Wu, Y., Yao, L., Liu, J., Zhuang, C. (2018). A New Method of Resource-Scheduling-Strategy Generation for the Assembly of Complex Products Based on the Apriori Algorithm (IEEE). IEEE.
  • Zhang, Y., Zhu, H., Tang, D., Zhou, T., Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78. DOI: 10.1016/j.rcim.2022.102412.
  • Zhao, A., Liu, P., Gao, X., Huang, G., Yang, X., Ma, Y., Xie, Z., Li, Y. (2022). Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem. Mathematics, 10(23). DOI: 10.3390/math10234608.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171679824

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