Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines
Received: 25 May 2024 | Revised: 1 July 2024 and 7 July 2024 | Accepted: 8 July 2024 | Online: 26 July 2024
Corresponding author: Khalid Ait Ben Hamou
Abstract
Production planning in supply chain management faces considerable challenges due to the dynamics and unpredictability of the production environment. Decision support systems based on the evolution of artificial intelligence can provide innovative solutions. In this paper, an approach based on machine learning techniques to solve the problem of scheduling the production of N products on M non-identical parallel machines is proposed. Using regression and classification models, our approach aims to predict overall production costs and assign products to the right machines. Some experiments carried out on simulated data sets demonstrate the relevance of the proposed approach. In particular, the XGBoost model stands out for its superior performance compared with the other tested ML algorithms. The proposed approach makes a significant contribution to the optimization of production scheduling, offering significant potential for improvement in Supply Chain Management.
Keywords:
decision support system, machine learning, scheduling problem, supply chain managementDownloads
References
D. Ni, Z. Xiao, and M. K. Lim, "A systematic review of the research trends of machine learning in supply chain management," International Journal of Machine Learning and Cybernetics, vol. 11, no. 7, pp. 1463–1482, Jul. 2020.
G. Schmidt, "A Decision Support System for Production Scheduling," Journal of Decision Systems, vol. 1, no. 2–3, pp. 243–260, Jan. 1992.
M. Đumić, D. Šišejković, R. Čorić, and D. Jakobović, "Evolving priority rules for resource constrained project scheduling problem with genetic programming," Future Generation Computer Systems, vol. 86, pp. 211–221, Sep. 2018.
B. Waschneck et al., "Optimization of global production scheduling with deep reinforcement learning," Procedia CIRP, vol. 72, pp. 1264–1269, Jan. 2018.
Y.-R. Shiue, K.-C. Lee, and C.-T. Su, "Real-time scheduling for a smart factory using a reinforcement learning approach," Computers & Industrial Engineering, vol. 125, pp. 604–614, Nov. 2018.
S. L. Takeda Berger, R. M. Zanella, and E. M. Frazzon, "Towards a data-driven predictive-reactive production scheduling approach based on inventory availability," IFAC-PapersOnLine, vol. 52, no. 13, pp. 1343–1348, Jan. 2019.
L. He, W. Li, Y. Zhang, and Y. Cao, "A discrete multi-objective fireworks algorithm for flowshop scheduling with sequence-dependent setup times," Swarm and Evolutionary Computation, vol. 51, pp. 100575, Dec. 2019.
C. D. Hubbs, C. Li, N. V. Sahinidis, I. E. Grossmann, and J. M. Wassick, "A deep reinforcement learning approach for chemical production scheduling," Computers & Chemical Engineering, vol. 141, pp. 106982, Oct. 2020.
K. Guo, M. Yang, and H. Zhu, "Application research of improved genetic algorithm based on machine learning in production scheduling," Neural Computing and Applications, vol. 32, no. 7, pp. 1857–1868, Apr. 2020.
Y. Li, S. Carabelli, E. Fadda, D. Manerba, R. Tadei, and O. Terzo, "Machine learning and optimization for production rescheduling in Industry 4.0," The International Journal of Advanced Manufacturing Technology, vol. 110, no. 9, pp. 2445–2463, Oct. 2020.
C. Morariu, O. Morariu, S. Răileanu, and T. Borangiu, "Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems," Computers in Industry, vol. 120, pp. 103244, Sep. 2020.
C. Zhang, W. Song, Z. Cao, J. Zhang, P. S. Tan, and X. Chi, "Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning," in Advances in Neural Information Processing Systems, 2020, vol. 33, pp. 1621–1632.
Z. Shi, H. Ma, M. Ren, T. Wu, and A. J. Yu, "A learning-based two-stage optimization method for customer order scheduling," Computers & Operations Research, vol. 136, pp. 105488, Dec. 2021.
W. Guo, M. Vanhoucke, J. Coelho, and J. Luo, "Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem," Expert Systems with Applications, vol. 167, pp. 114116, Apr. 2021.
S. Lang, T. Reggelin, J. Schmidt, M. Müller, and A. Nahhas, "NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies," Expert Systems with Applications, vol. 172, pp. 114666, Jun. 2021.
H. Yamashiro and H. Nonaka, "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, vol. 8,pp. 100196, Jan. 2021.
S. Zhai, B. Gehring, and G. Reinhart, "Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning," Journal of Manufacturing Systems, vol. 61, pp. 830–855, Oct. 2021.
J. Juros, M. Brcic, M. Koncic, and M. Kovac, "Exact solving scheduling problems accelerated by graph neural networks," in 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), Feb. 2022, pp. 865–870.
K. Lei et al., "A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem," Expert Systems with Applications, vol. 205, pp. 117796, Nov. 2022.
R. Braune, F. Benda, K. F. Doerner, and R. F. Hartl, "A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems," International Journal of Production Economics, vol. 243, pp. 108342, Jan. 2022.
S. Jungbluth, N. Gafur, J. Popper, V. Yfantis, and M. Ruskowski, "Reinforcement Learning-based Scheduling of a Job-Shop Process with Distributedly Controlled Robotic Manipulators for Transport Operations," IFAC-PapersOnLine, vol. 55, no. 2, pp. 156–162, Jan. 2022.
A. Rinciog and A. Meyer, "Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems," Procedia CIRP, vol. 107, pp. 1112–1119, Jan. 2022.
E. Morinaga, X. Tang, K. Iwamura, and N. Hirabayashi, "An improved method of job shop scheduling using machine learning and mathematical optimization," Procedia Computer Science, vol. 217, pp. 1479–1486, Jan. 2023.
C.-L. Liu, C.-J. Tseng, T.-H. Huang, and J.-W. Wang, "Dynamic Parallel Machine Scheduling With Deep Q-Network," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 6792–6804, Aug. 2023.
W. Song, X. Chen, Q. Li, and Z. Cao, "Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning," IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1600–1610, Oct. 2023.
S. C. Graves, "A Review of Production Scheduling," Operations Research, vol. 29, no. 4, pp. 646–675, May 1981.
F. A. Rodammer and K. P. White, "A recent survey of production scheduling," IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, no. 6, pp. 841–851, Aug. 1988.
M. Pinedo and K. Hadavi, "Scheduling: Theory, Algorithms and Systems Development," in Operations Research Proceedings 1991, Berlin, Heidelberg, 1992, pp. 35–42.
S. Angra and S. Ahuja, "Machine learning and its applications: A review," in 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Mar. 2017, pp. 57–60.
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, May 2016, pp. 785–794.
A. Saini, K. Guleria, and S. Sharma, "An Efficient Deep Learning Model for Eye Disease Classification," Jun. 2023, pp. 1–6.
P. Hajek, M. Z. Abedin, and U. Sivarajah, "Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework," Information Systems Frontiers, vol. 25, no. 5, pp. 1985–2003, Oct. 2023.
S. Ramaneswaran, K. Srinivasan, P. M. D. R. Vincent, and C.-Y. Chang, "Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification," Computational and Mathematical Methods in Medicine, vol. 2021, no. 1, pp. 2577375, 2021.
R. Szczepanek, "Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost," Hydrology, vol. 9, no. 12, pp. 226, Dec. 2022.
W. Ismaiel, A. Alhalangy, A. O. Y. Mohamed, and A. I. A. Musa, "Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13757–13764, Apr. 2024.
C. Matara, S. Osano, A. O. Yusuf, and E. O. Aketch, "Prediction of Vehicle-induced Air Pollution based on Advanced Machine Learning Models," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12837–12843, Feb. 2024.
H. Al-Dossari, F. A. Nughaymish, Z. Al-Qahtani, M. Alkahlifah, and A. Alqahtani, "A Machine Learning Approach to Career Path Choice for Information Technology Graduates," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6589–6596, Dec. 2020.
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Copyright (c) 2024 Khalid Ait Ben Hamou, Zahi Jarir, Selwa Elfirdoussi
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