Abstract
Hybrid flow shop with energy consumption (HFS-EC) combine the flow shop scheduling and parallel machine scheduling problem with the aim to optimize energy utilization, besides regular makespan in the production scheduling. This paper optimizes an HFS-EC case study using Moth Flame Optimization (MFO). The case study has been conducted in a machine shop concentrating on three machining types; lathe, milling and deburring. The objectives were to optimize makespan and total energy consumption in the machine schedule. Optimization using MFO has been conducted and the results was compared with well-established algorithm like Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization. The results were also compared with relatively recent algorithm such as Whale Optimization Algorithm and Harris Haws Optimization. Based on the optimization results, the MFO outperformed other comparison algorithms for the mean fitness and also the best fitness. Although there were other solutions with better individual optimization objectives, but results obtained by MFO compromised between minimum makespan and energy consumption. The proposed HFS-EC model and MFO algorithm has a great potential to be implemented in other scheduling case study due to benefit of reducing carbon emission and at the same time maintain the production output.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu C, Gao L, Pan Q, Li X, Zheng J (2019) A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Appl Soft Comput J 75:728–749
Schulz S (2019) A genetic algorithm to solve the hybrid flow shop scheduling problem with subcontracting options and energy cost consideration. Adv Intell Syst Comput 854:263–273
Schulz S, Neufeld JS, Buscher U (2019) A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. J Clean Prod 224:421–434
Jiang S-L, Zhang L (2019) Energy-oriented scheduling for hybrid flow shop with limited buffers through efficient multi-objective optimization. IEEE Access 7:34477–34487
Shi L, Guo G, Song X (2019) Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment. Int J Prod Res (in Press)
Yan J, Wen J, Li L (2014) Genetic algorithm based optimization for energy-aware hybrid flow shop scheduling. In: Proceedings of the 2014 international conference on artificial intelligence, ICAI 2014—WORLDCOMP, pp 358–364
Chen T-L, Cheng C-Y, Chou Y-H (2020) Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming. Ann Oper Res 290:813–836
Zeng L-L, Zou F-X, Xu X-H, Gao Z (2009) Dynamic scheduling of multi-task for hybrid flow-shop based on energy consumption. In: 2009 IEEE international conference on information and automation, ICIA 2009, pp 478–482
Luo H, Du B, Huang GQ, Chen H, Li X (2013) Hybrid flow shop scheduling considering machine electricity consumption cost. Int J Prod Econ 146(2):423–439
Wang S, Wang X, Chu F, Yu J (2020) An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int J Prod Res 58(8):2283–2314
Zhang B, Pan Q-K, Gao L, Li X-Y, Meng L-L, Peng K-K (2019) A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem. Comput Ind Eng 136:325–344
Li J-Q et al (2020) Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots. Swarm Evol Comput 52 (in Press)
Geng K, Ye C, Dai ZH, Liu L (2020) Bi-objective re-entrant hybrid flow shop scheduling considering energy consumption cost under time-of-use electricity tariffs. Complexity 2020:8565921
Li M, Lei D, Cai J (2019) Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives. Swarm Evol Comput 49:34–43
Geng K, Ye C, Cao L, Liu L (2019) Multi-objective reentrant hybrid flowshop scheduling with machines turning on and off control strategy using improved multi-verse optimizer algorithm. Math Probl Eng 2019:2573873
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249
Acknowledgements
The authors would like to acknowledge Universiti Malaysia Pahang for funding this research under research grant RDU190317.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ab. Rashid, M., Mohd Rose, A., Nik Mohamed, N. (2022). Hybrid Flow Shop Scheduling with Energy Consumption in Machine Shop Using Moth Flame Optimization. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-33-4597-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4596-6
Online ISBN: 978-981-33-4597-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)