REFERENCES

1. Lubing X, Xiaoming R, Shuai L, Xin H. An opportunistic maintenance strategy for offshore wind turbine based on accessibility evaluation. Wind Eng 2020;44:455-68.

2. Mohammad Shafinejad M, Abedi M. Selection of suitable sites for offshore wind farms in the Caspian Sea and choosing the most suitable wind turbine in each area. Wind Eng 2021;45:294-313.

3. Yang J, Chang Y, Zhang L, Hao Y, Yan Q, Wang C. The life-cycle energy and environmental emissions of a typical offshore wind farm in China. J Clean Prod 2018;180:316-24.

4. Kong L, Wang L, Li F, et al. A life-cycle integrated model for product eco-design in the conceptual design phase. J Clean Prod 2022;363:132516.

5. Zhang X, Zhang L, Fung KY, Bakshi BR, Ng KM. Sustainable product design: a life-cycle approach. Chem Eng Sci 2020;217:115508.

6. Kuo TC, Wang CJ. Integrating robust design criteria and axiomatic design principles to support sustainable product development. Int J Pr Eng Man GT 2019;6:549-57.

7. Gero JS, Kannengiesser U. The situated function-behaviour-structure framework. Design Stud 2004;25:373-91.

8. Deng YM, Zhu YW. Function to structure/material mappings for conceptual design synthesis and their supportive strategies. Int J Adv Manuf Technol 2009;44:1063-72.

9. Yuan L, Liu Y, Sun Z, Cao Y, Qamar A. A hybrid approach for the automation of functional decomposition in conceptual design. J Eng Design 2016;27:333-60.

10. Christophe F, Bernard A, Coatanéa É. RFBS: a model for knowledge representation of conceptual design. CIRP Ann 2010;59:155-8.

11. Habib T, Komoto H. Comparative analysis of design concepts of mechatronics systems with a CAD tool for system architecting. Mechatronics 2014;24:788-804.

12. Chen Y, Zhao M, Xie Y, Zhang Z. A new model of conceptual design based on Scientific Ontology and intentionality theory. Part II: the process model. Design Stud 2015;38:139-60.

13. Deng YM. Function and behavior representation in conceptual mechanical design. AI EDAM 2002;16:343-62.

14. Li L, Yu S, Tao J, Li L. A FBS-based energy modelling method for energy efficiency-oriented design. J Clean Prod 2018;172:1-13.

15. Umeda Y, Kondoh S, Shimomura Y, Tomiyama T. Development of design methodology for upgradable products based on function-behavior-state modeling. AI EDAM 2005;19:161-82.

16. Wang Y, Qin Y, Wang K, et al. Where is the most feasible, economical, and green wind energy? Evidence from high-resolution potential mapping in China. J Clean Prod 2022;376:134287.

17. Supciller AA, Toprak F. Selection of wind turbines with multi-criteria decision making techniques involving neutrosophic numbers: a case from Turkey. Energy 2020;207:118237.

18. Deveci M, Cali U, Kucuksari S, Erdogan N. Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland. Energy 2020;198:117317.

19. Peri E, Tal A. A sustainable way forward for wind power: assessing turbines’ environmental impacts using a holistic GIS analysis. Appl Energy 2020;279:115829.

20. Dhiman HS, Deb D. Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms. Energy 2020;202:117755.

21. Abdel-basset M, Gamal A, Chakrabortty RK, Ryan M. A new hybrid multi-criteria decision-making approach for location selection of sustainable offshore wind energy stations: a case study. J Clean Prod 2021;280:124462.

22. Yu Y, Wu S, Yu J, Xu Y, Song L, Xu W. A hybrid multi-criteria decision-making framework for offshore wind turbine selection: a case study in China. Appl Energy 2022;328:120173.

23. Ma Y, Xu L, Cai J, Cao J, Zhao F, Zhang J. A novel hybrid multi-criteria decision-making approach for offshore wind turbine selection. Wind Eng 2021;45:1273-95.

24. Wang J, Xu L, Cai J, Fu Y, Bian X. Offshore wind turbine selection with a novel multi-criteria decision-making method based on Dempster-Shafer evidence theory. Sustain Energy Techn 2022;51:101951.

25. Gao J, Guo F, Ma Z, Huang X, Li X. Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators. Energy 2020;204:117899.

26. Huang R, Zhang M, Guo M, et al. Selection of offshore wind turbine based on analytic hierarchy process. In: 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE); 2020 Jun 04-07; Chengdu, China. IEEE; 2020. pp. 341-5.

27. Güner F, Başer V, Zenk H. Evaluation of offshore wind power plant sustainability: a case study of Sinop/Gerze, Turkey. IJGW 2021;23:370.

28. Lozano-minguez E, Kolios AJ, Brennan FP. Multi-criteria assessment of offshore wind turbine support structures. Renew Energy 2011;36:2831-7.

29. Bagočius V, Zavadskas EK, Turskis Z. Multi-person selection of the best wind turbine based on the multi-criteria integrated additive-multiplicative utility function. J Civ Eng Manag 2014;20:590-9. Available from: https://www.tandfonline.com/doi/abs/10.3846/13923730.2014.932836. [Last accessed on 25 Oct 2023]

30. Xue J, Yip TL, Wu B, Wu C, van Gelder PHAJM. A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: an application to a case in China. Renew Energy 2021;172:897-917.

31. Deveci M, Özcan E, John R, Pamucar D, Karaman H. Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Appl Soft Comput 2021;109:107532.

32. Spreafico C, Landi D, Russo D. A new method of patent analysis to support prospective life cycle assessment of eco-design solutions. Sustain Prod Consump 2023;38:241-51.

33. Zhu GN, Hu J, Ren H. A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Appl Soft Comput 2020;91:106228.

34. Wu X, Zhu Z, Chen C, Chen G, Liu P. A monotonous intuitionistic fuzzy TOPSIS method under general linear orders via admissible distance measures. IEEE Trans Fuzzy Syst 2023;31:1552-65.

35. Bringas EN, Bowles G, Walker GH. Supporting complex decision making in learning space design: WDA-ANP, a novel sociotechnical systems approach. Facilities 2022;40:435-51.

36. Wang S, Wang S, Liu J. Life-cycle green-house gas emissions of onshore and offshore wind turbines. J Clean Prod 2019;210:804-10.

37. Schreiber A, Marx J, Zapp P. Comparative life cycle assessment of electricity generation by different wind turbine types. J Clean Prod 2019;233:561-72.

38. Gao C, Na H, Song K, et al. Environmental impact analysis of power generation from biomass and wind farms in different locations. Renew Sustain Energy Rev 2019;102:307-17.

39. Yang W, Zhang J. Assessing the performance of gray and green strategies for sustainable urban drainage system development: a multi-criteria decision-making analysis. J Clean Prod 2021;293:126191.

40. Yang Z, Shang WL, Zhang H, Garg H, Han C. Assessing the green distribution transformer manufacturing process using a cloud-based q-rung orthopair fuzzy multi-criteria framework. Appl Energy 2022;311:118687.

41. Cui Y, Yang L, Shi L, Liu G, Wang Y. Cleaner production indicator system of petroleum refining industry: from life cycle perspective. J Clean Prod 2022;355:131392.

42. Yadegaridehkordi E, Hourmand M, Nilashi M, et al. Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach. J Clean Prod 2020;277:122905.

43. van Hagen L, Petrick K, Wilhelm S, Schmehl R. Life-cycle assessment of a multi-megawatt airborne wind energy system. Energies 2023;16:1750.

44. Wu Y, Tao Y, Zhang B, Wang S, Xu C, Zhou J. A decision framework of offshore wind power station site selection using a PROMETHEE method under intuitionistic fuzzy environment: a case in China. Ocean Coast Manage 2020;184:105016.

45. Chunhua F, Shi H, Guozhen B. A group decision making method for sustainable design using intuitionistic fuzzy preference relations in the conceptual design stage. J Clean Prod 2020;243:118640.

46. Bilgili F, Zarali F, Ilgün MF, Dumrul C, Dumrul Y. The evaluation of renewable energy alternatives for sustainable development in Turkey using ‌intuitionistic‌ ‌fuzzy‌-TOPSIS method. Renew Energy 2022;189:1443-58.

47. Chen CH. A hybrid multi-criteria decision-making approach based on ANP-entropy TOPSIS for building materials supplier selection. Entropy 2021;23:1597.

48. Saaty TL. Decision making - the analytic hierarchy and network processes (AHP/ANP). J Syst Sci Syst Eng 2004;13:1-35.

49. Li TC, Zhang HQ, Du JG, Qian J. Evaluating the ability of new ship maintenance based on super decision software. In: 2020 International Conference on Wireless Communications and Smart Grid (ICWCSG); 2020 Jun 12-14; Qingdao, China. IEEE; 2020. pp. 484-7.

50. Memari A, Dargi A, Akbari Jokar MR, Ahmad R, Abdul Rahim AR. Sustainable supplier selection: a multi-criteria intuitionistic fuzzy TOPSIS method. J Manuf Syst 2019;50:9-24.

51. Chen L, Yu H. Emergency alternative selection based on an E-IFWA approach. IEEE Access 2019;7:44431-40.

52. Khan MSA, Anjum F, Ullah I, Senapati T, Moslem S. Priority degrees and distance measures of complex hesitant fuzzy sets with application to multi-criteria decision making. IEEE Access 2023;11:13647-66.

Green Manufacturing Open
ISSN 2835-7590 (Online)
Follow Us

Portico

All published articles will preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles will preserved here permanently:

https://www.portico.org/publishers/oae/