Abstract
With the increasing importance and innovation of technology in recent years, smart manufacturing has been able to give significantly greater work efficiency and productivity. As the current industrial system transitions into a new era of sustainability and digitalization, it is important to note that the significance of sustainability, digitization, and smart manufacturing, should be considered in production plants and units. Sustainable development and energy efficiency are projected to be important objectives in the design and implementation of smart factories. Various multiple criteria decision-making (MCDM) methodology have been used and utilized in industry-academic works to attain sustainability and operation goals. Intelligent manufacturing technologies have increased the efficiency and sustainability of various renewable energy sources with significant market shares. However, bioenergy and low-carbon energies have a limited number of applications in this context. Due of the various time scales, production rates, and process dynamics, existing smart manufacturing technologies, the role of smart factory may vary for big production house to small production units. However, the role of research, data, and modified decision science methodologies are highly effective in this context. This study will examine various aspects of smart manufacturing applications, measuring their impact on energy efficiency and exploring several multiple criteria decision-making techniques that have been developed for evaluating and comparing the energy efficiency of smart manufacturing applications.
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References
B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, B. Yin, Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018). https://doi.org/10.1109/ACCESS.2017.2783682
I. Costa-Carrapiço, R. Raslan, J.N. González, A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency. Energ. Build., 210, 109690 (2020)
K. Gao, Y. Huang, A. Sadollah, L. Wang, A review of energy-efficient scheduling in intelligent production systems. Complex Intell. Syst. 6(2), 237–249 (2020). https://doi.org/10.1007/s40747-019-00122-6
G. Hwang, J.H. Han, T.W. Chang, An integrated key performance measurement for manufacturing operations management. Sustainability (Switzerland) 12(13), 1–15 (2020). https://doi.org/10.3390/su12135260
Y.S. Kao, K. Nawata, C.Y. Huang, Evaluating the performance of systemic innovation problems of the IoT in manufacturing industries by novel MCDM methods. Sustainability (Switzerland) 11(18), 1–33 (2019). https://doi.org/10.3390/su11184970
J. Lee, S. Jun, T.-W. Chang, J. Park, A smartness assessment framework for smart factories using analytic network process. Sustainability 9(5), 794 (2017). https://doi.org/10.3390/su9050794
Y. Meng, Y. Yang, H. Chung, P.H. Lee, C. Shao, Enhancing sustainability and energy efficiency in smart factories: A review. Sustainability (Switzerland) 10(12), 1–28 (2018). https://doi.org/10.3390/su10124779
S.M. Ordoobadi, Application of ANP methodology in evaluation of advanced technologies. J. Manuf. Technol. Manag. 23(2), 229–252 (2012). https://doi.org/10.1108/17410381211202214
C.-C. Sun, A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 37(12), 7745–7754 (2010). https://doi.org/10.1016/j.eswa.2010.04.066
S. Terry, H. Lu, I. Fidan, Y. Zhang, K. Tantawi, T. Guo, B. Asiabanpour, The influence of smart manufacturing towards energy conservation: A review. Technologies 8(2), 31 (2020). https://doi.org/10.3390/technologies8020031
S. Wang, Y. Chen, How technological innovation affect China’s pharmaceutical smart manufacturing industrial upgrading. J. Healthcare Eng. 2021, 1–10 (2021). https://doi.org/10.1155/2021/3342153
Z. Wu, K. Yang, J. Yang, Y. Cao, Y. Gan, Energy-efficiency-oriented scheduling in smart manufacturing. J. Ambient. Intell. Humaniz. Comput. 10(3), 969–978 (2019). https://doi.org/10.1007/s12652-018-1022-x
C.-L. Yang, S.-P. Chuang, R.-H. Huang, Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Syst. Appl. 36(8), 11369–11377 (2009). https://doi.org/10.1016/j.eswa.2009.03.023
Q. Zhang, R. Mu, Z. Zhang, Y. Hu, C. Liu, L. Zhang, X. Yu, Competitiveness evaluation of high-quality manufacturing development in the Yangtze River Economic Belt. Int. J. Sustain. Dev. Plann. 15(6), 875–883 (2020). https://doi.org/10.18280/ijsdp.150611
C. Zhang, W. Xu, J. Liu, Z. Liu, Z. Zhou, D.T. Pham, Digital twin-enabled reconfigurable modeling for smart manufacturing systems. Int. J. Comput. Integr. Manuf. 34(7–8), 709–733 (2021). https://doi.org/10.1080/0951192X.2019.1699256
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Jha, C.K. (2022). Evaluation of Energy Efficiency for Smart Manufacturing: Applications and Future Scopes. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_156-1
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DOI: https://doi.org/10.1007/978-3-030-72322-4_156-1
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