Abstract
With the continuous development of the economy, various enterprises pay more and more attention to the scheduling problem of multi-objective workshops. The multi-objective flow shop scheduling problem is extremely widely used in real generation. Reasonable use of workshop resources and assignment of work tasks can improve the work efficiency of workshops and increase work income. The existing multi-objective flow workshop has the problems of high scheduling risk index and low work efficiency. The multi-objective optimization problem solving method in the wireless network genetic algorithm is used to solve this practical problem, and the optimization is used to transform the operators from single task to multi-task direction, and arrange the working time and sequence according to certain rules, so as to achieve the purpose of efficient production. In the field of artificial intelligence, this paper used the wireless network genetic algorithm (GA) to design a multi-objective flow shop scheduling analysis system, analyzed and adjusted the scheduling problems of the workshop, and completed the scheduling design of the entire workshop. Through experimental tests on different workshops: workshop machine error test, workshop work time consumption test, work efficiency test and workshop worker satisfaction test, it is found that the use of wireless network GA for multi-objective workshop flow scheduling can greatly reduce the working error of the workshop. It can reduce the time-consuming work of the workshop, and the wireless network genetic algorithm can improve the work efficiency of the assembly line. The multi-objective flow scheduling based on the wireless network GA improves the worker’s satisfaction by 10.7%.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Carlucci A, Messina G, Cascone R (2018) Video showed the main steps of the procedure as the diagnosis of dislocated stent, the extraction through the rigid bronchoscopy, and the endoscopic follow-up. J Intell Fuzzy Syst 212(4):12–19
Diao H (2018) Deep mining of redundant data in wireless sensor network based on genetic algorithm. Autom Control Comput Sci 52(4):291–296
Goudarzi S, Hassan WH, Anisi MH (2017) MDP-based network selection scheme by genetic algorithm and simulated annealing for vertical-handover in heterogeneous wireless networks. Wirel Pers Commun 92(2):399–436
Javidtash N, Jabbari M, Niknam T (2017) A novel mixture of non-dominated sorting genetic algorithm and fuzzy method to multi-objective placement of distributed generations in Microgrids. J Intell Fuzzy Syst 33(3):1–8
Khalaf OI, Abdulsahib GM, Sabbar BM (2020) Optimization of wireless sensor network coverage using the bee algorithm. J Inf Sci Eng 36(2):377–386
Lebbar G, Abbassi IE, Barkany AE (2018) Solving the multi objective flow shop scheduling problems using an improved NSGA-II. Int J Oper Quant Manag 24(3):211–230
Li Z, Xu Y, Fang S (2020) Multiobjective coordinated energy dispatch and voyage scheduling for a multienergy ship microgrid. IEEE Trans Ind Appl 56(2):989–999
Mittal P, Mitra K, Kulkarni K (2017) Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: a hybrid approach. Energy Convers Manag 132(7):147–160
Paul H, Aziz RA, Karim TM (2017) Modeling and scheduling of multi-stage and multi-processor flow shop. Global J Manag Bus Res 16(11):13–23
Rady A, Shokair M, El-Rabaie S (2021) Efficient clustering based genetic algorithm in mobile wireless sensor networks. Menoufia J Electron Eng Res 30(1):1–12
Rahmani Hosseinabadi AA, Vahidi J, Saemi B, Sangaiah AK, Elhoseny M (2019) Extended genetic algorithm for solving open-shop scheduling problem. Soft computing 23:5099–5116
Ren Y, Tian G, Zhao F (2017) Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm. Eng Appl Artif Intell 64(2):415–431
Rydel M, Stanisawski R (2018) A new frequency weighted Fourier-based method for model order reduction. Automatica 88(6):107–112
Sangaiah AK, Suraki MY, Sadeghilalimi M, Bozorgi SM, Hosseinabadi AAR, Wang J (2019) A new meta-heuristic algorithm for solving the flexible dynamic job-shop problem with parallel machines. Symmetry 11(2):165
Sangaiah AK, Hosseinabadi AAR, Shareh MB, Bozorgi Rad SY, Zolfagharian A, Chilamkurti N (2020) IoT resource allocation and optimization based on heuristic algorithm. Sensors 20(2):539
Schutze O, Esquivel X, Lara A (2019) Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522
Vargas A (2018) On the Pareto compliance of the averaged hausdorff distance as a performance indicator. Univ Sci 23(3):333–354
Vishal P, Ramesh BA (2018) Deployment of context-aware sensor in wireless sensor network based on the variants of genetic algorithm. Int J Artif Life Res 8(2):1–24
Wang J, Ersoy OK, Chen X (2017) A method of initial population generation of intelligent optimization algorithms for constrained global optimization. Int J Hybrid Inf Technol 10(6):47–56
Wu X, Cui Q (2018) Multi-objective flexible flow shop scheduling problem with renewable energy. Comput Integr Manuf Syst 24(11):2792–2807
Yin L, Zhuang M, Jia J (2020) Energy saving in flow-shop scheduling management: an improved multiobjective model based on grey wolf optimization algorithm. Math Probl Eng 202(3):1–14
Zeng Y, Tang L, Wu N (2017) Analysis of influencing factors of production performance of enhanced geothermal system: a case study at Yangba**g geothermal field. Energy 127(15):218–235
Zhang W, Chen J, Wang H (2019) Analyses of inverted generational distance for many-objective optimisation algorithms. Int J Bio-Inspir Comput 14(1):62–66
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Wang, F., Wu, S. Multi-objective flow shop scheduling system based on wireless network genetic algorithm from perspective of artificial intelligence. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08364-w
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DOI: https://doi.org/10.1007/s00500-023-08364-w