Parallel Machine Scheduling with Controllable Processing Time Considering Energy Cost and Machine Failure Prediction

Subject Areas : Business Strategy

Yousef Rabbani ¹ , Ali Qorbani ² , Reza Kamran Rad ³

1 - Department of Industrial Engineering, Faculty of Engineering, University of Semnan
2 - Industrial engineering department, Faculty of Engineering, University of Semnan, Semnan, Iran, PostalCode35131-19111
3 - Industrial engineering Department, Faculty of Engineering, University of Semnan, Semnan, Iran, PostalCode35131-19111

Received: 2022-09-19 Accepted : 2022-12-15 Published : 2023-01-01

Keywords: Just-in-Time Delivery, Machine Failure, Data mining, Parallel machines scheduling, energy,

Abstract :

Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed parallel machine scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and machine availability after repair and maintenance. Failure times have been predicated on using machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of parallel machine scheduling operations. After the previous data were received in the first step, machine failure was predicted by using machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these failures and machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction.

References:

Antoniadis, A., Garg, N., Kumar, G., & Kumar, N. (2020). Parallel machine scheduling to minimize energy consumption. Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms,  DOI:10.1137/1.9781611975994.168.

Arık, O. A., & Toksarı, M. D. (2018). Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. International Journal of Production Research, 56(7), 2488-2505. , DOI: 10.1080/00207543.2017.1388932.

Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. , DOI:10.1016/j.eswa.2021.114598.

Bilski, P. (2014). Application of support vector machines to the induction motor parameters identification. Measurement, 51, 377-386. , https://doi.org/10.1016/j.measurement.2013.12.013.

Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., & Carrotta, A. (2020). SOPHIA: An event-based IoT and machine learning architecture for predictive maintenance in industry 4.0. Information, 11(4) 202. , https://doi.org/10.3390/info11040202.

Chen, C., Liu, Y., Wang, S., Sun, X., Di Cairano-Gilfedder, C., Titmus, S., & Syntetos, A. A. (2020). Predictive maintenance using cox proportional hazard deep learning. Advanced Engineering Informatics, 44, 101054. , https://doi.org/10.1016/j.aei.2020.101054.

Chen, W.-J. (2009). Minimizing number of tardy jobs on a single machine subject to periodic maintenance. Omega, 37(3), 591-599. , DOI:10.1016/j.omega.2008.01.001.

Cheng, C.-Y., & Huang, L.-W. (2017). Minimizing total earliness and tardiness through unrelated parallel machine scheduling using distributed release time control. Journal of manufacturing systems, 42, 1-10. , DOI: 10.1016/j.jmsy.2016.10.005.

Dang, Q.-V., van Diessen, T., Martagan, T., & Adan, I. (2021). A matheuristic for parallel machine scheduling with tool replacements. European Journal of Operational Research, 291(2), 640-660. , https://doi.org/10.1016/j.ejor.2020.09.050.

Ebrahimi Zade, A., Fakhrzad, M. B., & Hasaninezhad, M. (2016). A Heuristic Algorithm for Solving Single Machine Scheduling Problem with Periodic Maintenance. Journal of System Management, 2(4), 1-12. https://sjsm.shiraz.iau.ir/article_525688_807c6c67d740507609f87b0bb9c09233.pdf , doi: 10.30495/JSM.2016.

Exposito-Izquierdo, C., Angel-Bello, F., Melián-Batista, B., Alvarez, A., & Báez, S. (2019). A metaheuristic algorithm and simulation to study the effect of learning or tiredness on sequence-dependent setup times in a parallel machine scheduling problem. Expert Systems with Applications, 117, 62-74. , DOI: https://doi.org/10.1016/j.eswa.2018.09.041.

Goli, A., & Keshavarz, T. (2021). Just-in-time scheduling in identical parallel machine sequence-dependent group scheduling problem. Journal of Industrial and Management Optimization. , doi: 10.3934/jimo.2021124

Hidri, L., Alqahtani, A., Gazdar, A., & Ben Youssef, B. (2021). Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints. Sustainability, 13(16), 9277. , https://doi.org/10.3390/su13169277.

Kayvanfar, V., Zandieh, M., & Teymourian, E. (2017). An intelligent water drop algorithm to identical parallel machine scheduling with controllable processing times: a just-in-time approach. Computational and Applied Mathematics, 36(1), 159-184. , https://doi.org/10.1007/s40314-015-0218-3.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, December 2017, 3149-3157.

Kramer, A., Iori, M., & Lacomme, P. (2021). Mathematical formulations for scheduling jobs on identical parallel machines with family setup times and total weighted completion time minimization. European Journal of Operational Research, 289(3), 825-840. , https://doi.org/10.1016/j.ejor.2019.07.006.

Kubiak, W. (1993). Minimizing variation of production rates in just-in-time systems: A survey. European Journal of Operational Research, 66(3), 259-271. , https://doi.org/10.1016/0377-2217(93)90215-9.

Mohr, F., Mejía, G., & Yuraszeck, F. (2021). Single and parallel machine scheduling with variable release dates. arXiv preprint arXiv:2103.01785. ,
https://doi.org/10.48550/arXiv.2103.01785.

Nanthapodej, R., Liu, C.-H., Nitisiri, K., & Pattanapairoj, S. (2021). Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems. Sustainability, 13(10), 5470. , https://doi.org/10.3390/su13105470.

Nicolo, G., Ferrer, S., Salido, M. A., Giret, A., & Barber, F. (2019). A multi-agent framework to solve energy-aware unrelated parallel machine scheduling problems with machine-dependent energy consumption and sequence-dependent setup time. Proceedings of the International Conference on Automated Planning and Scheduling,    DOI: https://doi.org/10.1609/icaps.v29i1.3492.

Nowicki, E., & Zdrzałka, S. (1990). A survey of results for sequencing problems with controllable processing times. Discrete Applied Mathematics, 26(2-3), 271-287. , https://doi.org/10.1016/0166-218X(90)90105-L.

Polyakovskiy, S., & M'Hallah, R. (2014). A multi-agent system for the weighted earliness tardiness parallel machine problem. Computers & operations research, 44, 115-136. , https://doi.org/10.1016/j.cor.2013.10.013.

Rabbani Yousef (2021). A Goal Programming Linear Model for Simultaneous Project Scheduling and Resource Leveling - a Huge Civil Project as a Case Study. Journal of system management, Volume 7, Issue 4, Pages 1-22, doi: 10.30495/JSM.2021.1936452.1503

 

Salimifard, K., Mohammadi, D., Moghdani, R., & Abbasizad, A. (2019). Green fuzzy parallel machine scheduling with sequence-dependent setup in the plastic moulding industry. Asian Journal of Management Science and Applications, 4(1), 27-48.

Schmidt, B., & Wang, L. (2018). Predictive maintenance of machine tool linear axes: A case from manufacturing industry. Procedia manufacturing, 17, 118-125. , https://doi.org/10.1016/j.promfg.2018.10.022.

Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing. Advanced Engineering Informatics, 45, 101101,  https://doi.org/10.1016/j.aei.2020.101101.

Schwendemann, S., Amjad, Z., & Sikora, A. (2021). A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Computers in Industry, 125, 103380. , https://doi.org/10.1016/j.compind.2020.103380., doi: https://doi.org/10.1016/j.compind.2020.103380.

Su, L.-H. (2009). Minimizing earliness and tardiness subject to total completion time in an identical parallel machine system. Computers & operations research, 36(2), 461-471. , https://doi.org/10.1016/j.cor.2007.09.013.

Vollert, S., Atzmueller, M., & Theissler, A. (2021). Interpretable Machine Learning: A brief survey from the predictive maintenance perspective. 2021 26th IEEE international conference on emerging technologies and factory automation (ETFA),  https://dl.acm.org/doi/10.1109/ETFA45728.2021.9613467, DOI:10.1109/ETFA45728.2021.9613467.

Wang, S., & Liu, M. (2015). Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning. Journal of manufacturing systems, 37, 182-192. , https://doi.org/10.1016/j.jmsy.2015.07.002.

Wang, S., Wang, X., Yu, J., Ma, S., & Liu, M. (2018). Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan. Journal of Cleaner Production, 193, 424-440., https://doi.org/10.1016/j.jclepro.2018.05.056.

Yazdani, M., & Jolai, F. (2015). A Genetic Algorithm with Modified Crossover Operator for a Two-Agent Scheduling Problem. Journal of System Management, 1(3), 1-13. https://sjsm.shiraz.iau.ir/article_517109_e5b8fcb61a96077c14f7372cc7ac9d52.pdf , doi: 10.30495/JSM.2015.

Zarandi, M., & Kayvanfar, V. (2015). A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach. The International Journal of Advanced Manufacturing Technology, 77(1), 545-563, https://doi.org/10.1007/s00170-014-6461-8.