2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 8 , Issue 1 , PP: 16-23, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud Ibrahim 1 * , Shereen Zaki 2 , Mahmoud M. Ismail 3
Doi: https://doi.org/10.54216/AJBOR.080102
This paper introduce a multi- criteria decision making (MCDM) perfect to assess business risk in electricity retail company to decrease risk loss and mange risks of business. The evaluation of business risk in electricity company included many conflicting criteria such as risk of political, risk of economic, and risk of market. So, this paper presented an Evaluation based on distance from average solution (EDAS) MCDM method to compute the weights of these criteria and rank the alternatives. Distances between each option and the mean answer on each criteria form the basis of EDAS. It expedites the decision-making process by streamlining the computation of distances to the deal solution. But in this evaluation, there are many imperfect and unclear data. So, the neutrosophic sets is presented to overcome this vague information. The interval valued neutrosophic sets (IVNSs) is a type of neutrosophic sets is presented in this work.
Business Risk , Electricity Retail , EDAS , MCDM ,
[1] B. Sun, F. Wang, J. Xie, and X. Sun, “Electricity Retailer trading portfolio optimization considering risk assessment in Chinese electricity market,” Electric Power Systems Research, vol. 190, p. 106833, 2021.
[2] J. Liu, X. Chen, Y. Xiang, D. Huo, and J. Liu, “Optimal planning and investment benefit analysis of shared energy storage for electricity retailers,” International Journal of Electrical Power & Energy Systems, vol. 126, p. 106561, 2021.
[3] L. Jin, C. Chen, X. Wang, J. Yu, and H. Long, “Research on information disclosure strategies of electricity retailers under new electricity reform in China,” Science of The Total Environment, vol. 710, p. 136382, 2020.
[4] K. Li et al., “A business model incorporating harmonic control as a value-added service for utility-owned electricity retailers,” IEEE Transactions on Industry Applications, vol. 55, no. 5, pp. 4441–4450, 2019.
[5] Y. Chai, Y. Xiang, J. Liu, C. Gu, W. Zhang, and W. Xu, “Incentive-based demand response model for maximizing benefits of electricity retailers,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 6, pp. 1644–1650, 2019.
[6] S. Yang et al., “A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response,” Journal of Cleaner Production, vol. 277, p. 124017, 2020.
[7] S. Guo, W. Zhang, and X. Gao, “Business risk evaluation of electricity retail company in China using a hybrid MCDM method,” Sustainability, vol. 12, no. 5, p. 2040, 2020.
[8] M. Karami and R. Madlener, “Business model innovation for the energy market: Joint value creation for electricity retailers and their customers,” Energy Research & Social Science, vol. 73, p. 101878, 2021.
[9] J. Yang, J. Zhao, F. Wen, and Z. Dong, “A model of customizing electricity retail prices based on load profile clustering analysis,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3374–3386, 2018.
[10] C. S. Dhanalakshmi, P. Madhu, A. Karthick, M. Mathew, and R. Vignesh Kumar, “A comprehensive MCDM-based approach using TOPSIS and EDAS as an auxiliary tool for pyrolysis material selection and its application,” Biomass conversion and biorefinery, pp. 1–16, 2020.
[11] N. Yalçin and U. Nuşin, “Applying EDAS as an applicable MCDM method for industrial robot selection,” Sigma Journal of Engineering and Natural Sciences, vol. 37, no. 3, pp. 779–796, 2019.
[12] D. K. Bagal, A. Giri, A. K. Pattanaik, S. Jeet, A. Barua, and S. N. Panda, “MCDM optimization of characteristics in resistance spot welding for dissimilar materials utilizing advanced hybrid Taguchi method-coupled CoCoSo, EDAS and WASPAS method,” in Next Generation Materials and Processing Technologies, Springer, 2021, pp. 475–490.
[13] D. Zindani, S. R. Maity, and S. Bhowmik, “Fuzzy-EDAS (evaluation based on distance from average solution) for material selection problems,” in Advances in Computational Methods in Manufacturing, Springer, 2019, pp. 755–771.
[14] T. Rashid, A. Ali, and Y.-M. Chu, “Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection,” Plos one, vol. 16, no. 2, p. e0246738, 2021.
[15] E. Bolturk and C. Kahraman, “A novel interval-valued neutrosophic AHP with cosine similarity measure,” Soft Computing, vol. 22, no. 15, pp. 4941–4958, 2018.
[16] S. Broumi, D. Nagarajan, A. Bakali, M. Talea, F. Smarandache, and M. Lathamaheswari, “The shortest path problem in interval valued trapezoidal and triangular neutrosophic environment,” Complex & Intelligent Systems, vol. 5, no. 4, pp. 391–402, 2019.
[17] M. Yazdani, A. E. Torkayesh, Ž. Stević, P. Chatterjee, S. A. Ahari, and V. D. Hernandez, “An interval valued neutrosophic decision-making structure for sustainable supplier selection,” Expert Systems with Applications, vol. 183, p. 115354, 2021.
[18] S. Gayen, F. Smarandache, S. Jha, and R. Kumar, “Interval-valued neutrosophic subgroup based on interval-valued triple t-norm,” in Neutrosophic sets in decision analysis and operations research, IGI Global, 2020, pp. 215–243.
[19] E. K. Zavadskas, R. Bausys, I. Lescauskiene, and A. Usovaite, “MULTIMOORA under interval-valued neutrosophic sets as the basis for the quantitative heuristic evaluation methodology HEBIN,” Mathematics, vol. 9, no. 1, p. 66, 2020.
[20] S. Broumi, M. Talea, A. Bakali, P. K. Singh, and F. Smarandache, Energy and spectrum analysis of interval valued neutrosophic graph using MATLAB. Infinite Study, 2019.
[21] N. T. Thong, L. Q. Dat, N. D. Hoa, M. Ali, and F. Smarandache, “Dynamic interval valued neutrosophic set: Modeling decision making in dynamic environments,” Computers in Industry, vol. 108, pp. 45–52, 2019.
[22] S. Gayen, F. Smarandache, S. Jha, M. K. Singh, S. Broumi, and R. Kumar, Soft subring theory under interval-valued neutrosophic environment, vol. 36. Infinite Study, 2020.
