Flow-shop scheduling problem with tool changes and tool wear to minimize energy consumption

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, Iran

2 Department of industrial engineering, Faculty of engineering, Semnan University, Semnan, Iran

Abstract

– Nowadays, most production units seek to minimize the amount of energy consumption due to the resulting economic pressures and the shortage of energy carriers such as electricity or fossil fuels. On the other hand, minimizing the total time to complete jobs aligns with reducing energy consumption. Therefore, efficient scheduling is one of the important concerns in industrial units. This research aims to minimize joint energy consumption and total job completion time in a flow-shop environment, considering speed level and tool wear level constraints. The increased speed level reduces the time to complete the job, and on the other hand, increases energy consumption. Moreover, the increase in the machine speed causes an increase in the level of wear and eventually a tool change. Since the flow-shop scheduling problem is NP-hard, it cannot be solved in a short time on a large scale. To cope with this issue, we solve this multi-objective optimization problem with two well-known metaheuristic methods, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The experimental results reveal that MOPSO outperforms NSGA-II in terms of MOCV and MID metrics. Conversely, NSGA-II outperforms MOPSO in terms of CPU time.
 

Keywords

References

Arasteh, A., (2022). Mathematical modeling of flexible production lines with different part types on unreliable machines by a priority rule. Journal of Quality Engineering and Production Optimization, 7(2), 34-59.
Baker, K. R. (2013). Computational results for the flowshop tardiness problem. Computers & Industrial Engineering, 64(3), 812-816.
Bruzzone, A. A., Anghinolfi, D., Paolucci, M., & Tonelli, F. (2012). Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP annals, 61(1), 459-462.
Chaudhry, I. A., Elbadawi, I. A., Usman, M., & Tajammal Chugtai, M. (2018). Minimising total flowtime in a no-wait flow shop (NWFS) using genetic algorithms. Ingeniería e Investigación, 38(3): 68-79.
Chen, T. L., Cheng, C. Y., & Chou, Y. H. (2020). Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming. Annals of Operations Research, 290, 813-836.‏
Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287.
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.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Ding, J. Y., Song, S., & Wu, C. (2016). Carbon-efficient scheduling of flow shops by multi-objective optimization. European Journal of Operational Research, 248(3), 758-771.
Enayati, M., Yousefi Nejad Attari, M., & Lotfian Delouyi, F. (2023). Optimizing Open Shop Scheduling: Minimizing Makespan through Whale Optimization Algorithm and Transportation Time Consideration. Journal of Quality Engineering and Production Optimization, 8(1), 133-150.
Esmaeili, M., Ahmadizar, F., & Sadeghi, H. (2021). Minimizing the sum of earliness and tardiness in single-machine scheduling. Journal of Quality Engineering and Production Optimization, 6(2), 59-78.
Fontes, D.B., Homayouni, S.M. & Fernandes, J.C., (2024). Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources. International Journal of Production Research, 62(3), 867-890.
Gadaleta, M., Pellicciari, M., & Berselli, G. (2019). Optimization of the energy consumption of industrial robots for automatic code generation. Robotics and Computer-Integrated Manufacturing, 57, 452-464.
Geng, K., Ye, C., Dai, Z. H., & Liu, L. (2020). Bi‐Objective Re‐Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time‐of‐Use Electricity Tariffs. Complexity, 2020(1), 8565921.
Gholizadeh, H., Fazlollahtabar, H., Fathollahi-Fard, A. M., & Dulebenets, M. A. (2021). Preventive maintenance for the flexible flowshop scheduling under uncertainty: A waste-to-energy system. Environmental Science and Pollution Research, 1-20.
Ghorbanzadeh M., & Ranjbar, M. (2023). Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints. European Journal of Operational Research, 307(2), 519-537.
Goli, A., Ala, A., & Hajiaghaei-Keshteli, M. (2023). Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem. Expert Systems with Applications, 213, 119077.
Ham, A., Park, M. J., & Kim, K. M. (2021). Energy‐Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming. Mathematical Problems in Engineering, 2021(1), 8035806.
Huang, J., Pan, Q., & Chen, Q. (2019, October). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem with sequence-dependent setup times. In IOP conference series: materials science and engineering (Vol. 646, No. 1, p. 012037).
IOP Publishing.Kumar, N. & Das, A.K., (2024). Critical review on tool wear and its significance on the µ-ECSM process. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 238(4), pp.907-925.
Li, J. Q., Sang, H. Y., Han, Y. Y., Wang, C. G., & Gao, K. Z. (2018). Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. Journal of Cleaner Production, 181, 584-598.
Liou, C. D., & Hsieh, Y. C. (2015). A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times. International Journal of Production Economics, 170, 258-267.
Mahdavi, K., Mohammadi, M. & Ahmadizar, F., (2023). Efficient scheduling of a no-wait flexible job shop with periodic maintenance activities and processing constraints. Journal of Quality Engineering and Production Optimization, 8(1), 33-56.
Mansouri, S. A., Aktas, E., & Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788.
Meng, L., Zhang, C., Shao, X., Ren, Y., & Ren, C. (2019). Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines. International Journal of Production Research, 57(4), 1119-1145.
Mokhtari, H., & Hasani, A. (2017). An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Computers & Chemical Engineering, 104, 339-352.
Mousavi, T.S., Shankar, A., Rezvani, M.H. & Ghadiri, H. (2024). Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information. Concurrency and Computation: Practice and Experience, 36(15), e7950.
Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271.
Nilakantan, J. M., Huang, G. Q., & Ponnambalam, S. G. (2015). An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. Journal of Cleaner Production, 90, 311-325.
Oda, Y., Fujishima, M. & Takeuchi, Y., (2015). Energy-saving machining of multi-functional machine tools. International Journal of Automation Technology, 9(2), 135-142.
Ponzo, F. C., Antonio, D. C., Nicla, L., & Nigro, D. (2021). Experimental estimation of energy dissipated by multistorey post-tensioned timber framed buildings with anti-seismic dissipative devices. Sustain. Struct, 1(000007).
Rabiei, P., Arias-Aranda, D. & Stantchev, V. (2023). Introducing a novel multi-objective optimization model for Volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA. Expert Systems with Applications, 226, 120142.
Rahmati, S. H. A., Hajipour, V., & Niaki, S. T. A. (2013). A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing, 13(4), 1728-1740.
Rastgar, I., Rezaean, J., Mahdavi, I., & Fattahi, P. (2021). Opportunistic maintenance management for a hybrid flow shop scheduling problem. Journal of Quality Engineering and Production Optimization, 6(2), 17-30.
Rezvan, M. T., Gholami, H., & Zakerian, R. (2021). A new algorithm for solving the parallel machine scheduling problem to maximize benefit and the number of jobs processed. Journal of Quality Engineering and Production Optimization, 6(2), 115-142.
Schott, J. R. (1998). Estimating correlation matrices that have common eigenvectors. Computational statistics & data analysis, 27(4), 445-459.
Schulz, S., Buscher, U., & Shen, L. (2020). Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices. Journal of Business Economics, 90, 1315-1343.
Schulz, S., Neufeld, J. S., & Buscher, U. (2019). A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. Journal of cleaner production, 224, 421-434.
Sekkal, D. N. & Belkaid, F. (2023). A multi-objective optimization algorithm for flow shop group scheduling problem with sequence dependent setup time and worker learning. Expert Systems with Applications, 233, 120878.
Shao, W., Shao, Z., & Pi, D. (2022). A multi-neighborhood-based multi-objective memetic algorithm for the energy-efficient distributed flexible flow shop scheduling problem. Neural Computing and Applications, 34(24), 22303-22330.Shen, L., Dauzère-Pérès, S., & Maecker, S. (2023). Energy cost efficient scheduling in flexible job-shop manufacturing systems. European Journal of Operational Research, 310(3), 992-1016.
Singh, H., Oberoi, J. S., & Singh, D. (2021). Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review. RAIRO-Operations Research, 55(1), 27-50.
Sola, A. V., & Mota, C. M. (2020). Influencing factors on energy management in industries. Journal of Cleaner Production, 248, 119263.
Soleimani, H., Ghaderi, H., Tsai, P. W., Zarbakhshnia, N., & Maleki, M. (2020). Scheduling of unrelated parallel machines considering sequence-related setup time, start time-dependent deterioration, position-dependent learning and power consumption minimization. Journal of Cleaner Production, 249, 119428.
Stewart, R., Raith, A., & Sinnen, O. (2023). Optimising makespan and energy consumption in task scheduling for parallel systems. Computers & Operations Research, 154, 106212.
Todorov, G. N., Volkova, E. E., Vlasov, A. I., & Nikitina, N. I. (2019). Modeling energy-efficient consumption at industrial enterprises. International Journal of Energy Economics and Policy, 9(2), 10-18.
Unal, R. & Dean, E.B. (1990, January). Taguchi approach to design optimization for quality and cost: an overview. In 1991 Annual conference of the international society of parametric analysts.
Utama, D. M. (2021, March). Minimizing number of tardy jobs in flow shop scheduling using a hybrid whale optimization algorithm. In Journal of Physics: Conference Series (Vol. 1845, No. 1, p. 012017). IOP Publishing.
Utama, D. M., Primayesti, M. D., Umamy, S. Z., Kholifa, B. M. N., & Yasa, A. D. (2023). A systematic literature review on energy-efficient hybrid flow shop scheduling. Cogent Engineering, 10(1), 2206074.
Wang, F., Deng, G., Jiang, T., & Zhang, S. (2018). Multi-objective parallel variable neighborhood search for energy consumption scheduling in blocking flow shops. Ieee Access, 6, 68686-68700.
Wang, S., Yao, J., Zhang, Z., & Zhang, K. (2019). Design and numerical simulations of a temperature tunable hybrid structure metamaterials. Journal of Nanophotonics, 13(3), 036019-036019.
Wang, Y. J., Wang, G. G., Tian, F. M., Gong, D. W., & Pedrycz, W. (2023). Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II. Engineering Applications of Artificial Intelligence, 121, 105977.
Wen, X., Sun, Y., Ma, H. L., & Chung, S. H. (2023). Green smart manufacturing: energy-efficient robotic job shop scheduling models. International Journal of Production Research, 61(17), 5791-5805.
Wu, X., & Che, A. (2019). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling. Omega, 82, 155-165.
Xin, X., Jiang, Q., Li, S., Gong, S., & Chen, K. (2021). Energy-efficient scheduling for a permutation flow shop with variable transportation time using an improved discrete whale swarm optimization. Journal of Cleaner Production, 293, 126121.
Xiong, H., Shi, S., Ren, D., & Hu, J. (2022). A survey of job shop scheduling problem: The types and models. Computers & Operations Research, 142, 105731.
Yan, J., Li, L., Zhao, F., Zhang, F., & Zhao, Q. (2016). A multi-level optimization approach for energy-efficient flexible flow shop scheduling. Journal of cleaner production, 137, 1543-1552.
Zhang, H., Deng, Z., Fu, Y., Lv, L., & Yan, C. (2017). A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. Journal of Cleaner Production 148: 174-184.
Zhang, J., Li, R., Yang, H., Qing, Z. & Chen, S. (2024). Tool wear characteristics of interrupted-cutting Zr-based bulk metallic glasses with cemented carbide tool. The International Journal of Advanced Manufacturing Technology, 133(5), 2623-2637.
Zhang, L., Deng, Q., Gong, G. & Han, W., (2020). A new unrelated parallel machine scheduling problem with tool changes to minimise the total energy consumption. International Journal of Production Research, 58(22), 6826-6845.
Zhang, W., Geng, H., Li, C., Gen, M., Zhang, G. & Deng, M., (2023). Q-learning-based multi-objective particle swarm optimization with local search within factories for energy-efficient distributed flow-shop scheduling problem. Journal of Intelligent Manufacturing, 36(1), 185-208.
Journal of Quality Engineering and Production Optimization
Volume 9, Issue 2
December 2024
Pages 19-46

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