REFERENCES

1. Kong L, Wang L, Li F, Wang G, Fu Y, Liu J. A new sustainable scheduling method for hybrid flow-shop subject to the characteristics of parallel machines. IEEE Access 2020;8:79998-80009.

2. Miao Y, Lv X, Zhe C, Guo D. Application of self-adaptive evolution strategy DE algorithm based on optimal objective of due date in HFSP. 2015 IEEE Int Conf Cyber Technol Autom Control Intell Syst IEEE-CYBER; 2015. pp. 1491-5.

3. Ma Y, Li F, Wang L, Wang G, Kong L. Life cycle carbon emission assessments and comparisons of cast iron and resin mineral composite machine tool bed in China. Int J Adv Manuf Technol 2021;113:1143-52.

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. Qin H, Han Y, Liu Y, Li J, Pan Q, Xue-han. A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints. Expert Syst Appl 2022;201:117256.

6. Qin H, Han Y, Wang Y, Liu Y, Li J, Pan Q. Intelligent optimization under blocking constraints: A novel iterated greedy algorithm for the hybrid flow shop group scheduling problem. Knowl Based Syst 2022;258:109962.

7. Xu Y, Wang L, Wang S, Liu M. An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines. Eng Optim 2013;45:1409-30.

8. Meng L, Zhang C, Shao X, Zhang B, Ren Y, Lin W. More MILP models for hybrid flow shop scheduling problem and its extended problems. Int J Prod Res 2020;58:3905-30.

9. Luo H, Du B, Huang GQ, Chen H, Li X. Hybrid flow shop scheduling considering machine electricity consumption cost. Int J Prod Econ 2013;146:423-39.

10. Behnamian J. Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function. Int J Adv Manuf Technol 2014;74:267-83.

11. Zhao F, Ma R, Wang L. A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system. IEEE Trans Cybern 2022;52:12675-86.

12. Qin H, Han Y, Zhang B, et al. An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem. Swarm Evol Comput 2022;69:100992.

13. Meng L, Zhang C, Shao X, Ren Y, Ren C. Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines. Int J Prod Res 2019;57:1119-45.

14. Li J, Sang H, Han Y, Wang C, Gao K. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J Clean Prod 2018;181:584-98.

15. Novak A, Sucha P, Novotny M, Stec R, Hanzalek Z. Scheduling jobs with normally distributed processing times on parallel machines. Eur J Oper Res 2022;297:422-41.

16. Yepes-borrero JC, Perea F, Ruiz R, Villa F. Bi-objective parallel machine scheduling with additional resources during setups. Eur J Oper Res 2021;292:443-55.

17. Thies C, Kieckhäfer K, Spengler TS, Sodhi MS. Operations research for sustainability assessment of products: a review. Eur J Oper Res 2019;274:1-21.

18. Drake R, Yildirim MB, Twomey J, Whitman L, Ahmad J, Lodhia P. Data collection framework on energy consumption in manufacturing. Available from: https://soar.wichita.edu/bitstream/handle/10057/3422/Yildirim_2006.pdf?sequence=3&isAllowed=y [Last accessed on 10 Jan 2023].

19. Mouzon G, Yildirim MB, Twomey J. Operational methods for minimization of energy consumption of manufacturing equipment. Int J Prod Res 2007;45:4247-71.

20. Liu X, Wang L, Kong L, Li F, Li J. A hybrid genetic algorithm for minimizing energy consumption in flow shops considering ultra-low idle state. Procedia CIRP 2019;80:192-6.

21. Kong L, Wang L, Li F, et al. Multi-layer integration framework for low carbon design based on design features. J Manuf Syst 2021;61:223-38.

22. Dimitrov D, Karachorova V, Szecsi T. Accuracy and reliability control of machining operations on machining centres. Key Eng Mater 2014;615:32-8.

23. Brecher C, Fey M, Daniels M. Modeling of position-, tool- and workpiece-dependent milling machine dynamics. Available from: http://publications.rwth-aachen.de/record/661060/files/661060.pdf [Last accessed on 10 Jan 2023].

24. Salido MA, Escamilla J, Giret A, Barber F. A genetic algorithm for energy-efficiency in job-shop scheduling. Int J Adv Manuf Technol 2016;85:1303-14.

25. Feng Y, Zhou M, Tian G, et al. Target disassembly sequencing and scheme evaluation for cnc machine tools using improved multiobjective ant colony algorithm and fuzzy integral. IEEE Trans Syst Man Cybern Syst 2019;49:2438-51.

26. Lu C, Gao L, Pan Q, Li X, Zheng J. A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Appl Soft Comput 2019;75:728-49.

27. Kang Y, Tang D. An approach to product solution generation and evaluation based on the similarity theory and ant colony optimisation. Int J Comput Integr Manuf 2014;27:1090-104.

28. Fang R, Popole Z. Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm. Environ Sci Pollut Res Int 2020;27:12842-50.

29. Tian G, Ren Y, Zhou M. Dual-objective scheduling of rescue vehicles to distinguish forest fires via differential evolution and particle swarm optimization combined algorithm. IEEE Trans Intell Transp Syst 2016;17:3009-21.

30. Wang L, Xu Y, Zhou G, Wang S, Liu M. A novel decoding method for the hybrid flow-shop scheduling problem with multiprocessor tasks. Int J Adv Manuf Technol 2012;59:1113-25.

31. Tian G, Ren Y, Feng Y, Zhou M, Zhang H, Tan J. Modeling and planning for dual-objective selective disassembly using and/or graph and discrete artificial bee colony. IEEE Trans Ind Inf 2019;15:2456-68.

32. Ding J, Song S, Wu C. Carbon-efficient scheduling of flow shops by multi-objective optimization. Eur J Oper Res 2016;248:758-71.

33. Tliba K, Diallo TML, Penas O, Ben Khalifa R, Ben Yahia N, Choley J. Digital twin-driven dynamic scheduling of a hybrid flow shop. J Intell Manuf ; doi: 10.1007/s10845-022-01922-3.

34. He Y, Li Y, Wu T, Sutherland JW. An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J Clean Prod 2015;87:245-54.

35. Lu C, Gao L, Li X, Pan Q, Wang Q. Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm. J Clean Prod 2017;144:228-38.

36. Strantzali E, Aravossis K, Livanos GA. Evaluation of future sustainable electricity generation alternatives: the case of a Greek island. Renew Sust Energ Rev 2017;76:775-87.

37. Sahabuddin M, Khan I. Multi-criteria decision analysis methods for energy sector’s sustainability assessment: robustness analysis through criteria weight change. Sustain Energy Technol Assess 2021;47:101380.

38. He B, Luo T, Huang S. Product sustainability assessment for product life cycle. J Clean Prod 2019;206:238-50.

39. Tian G, Zhang H, Zhou M, Li Z. AHP, gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives. IEEE Trans Syst Man Cybern Syst 2018;48:1093-105.

40. 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.

41. Shih H, Shyur H, Lee ES. An extension of TOPSIS for group decision making. Math Comput Model 2007;45:801-13.

42. Liu X, Yang X, Lei M. Optimisation of mixed-model assembly line balancing problem under uncertain demand. J Manuf Syst 2021;59:214-27.

43. Tiwari V, Jain PK, Tandon P. An integrated Shannon entropy and TOPSIS for product design concept evaluation based on bijective soft set. J Intell Manuf 2019;30:1645-58.

44. Wang X, Chan HK, Li D. A case study of an integrated fuzzy methodology for green product development. Eur J Oper Res 2015;241:212-23.

45. Zhao F, Zhao L, Wang L, Song H. An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. Expert Syst Appl 2020;160:113678.

46. Liu W, Gong Y, Chen W, Liu Z, Wang H, Zhang J. Coordinated charging scheduling of electric vehicles: a mixed-variable differential evolution approach. IEEE Trans Intell Transp Syst 2020;21:5094-109.

47. Li L, Wang Y, Xu Y, Lin K. Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems. J Manuf Syst 2022;62:767-76.

48. Luo S, Zhang L, Fan Y. Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J Clean Prod 2019;234:1365-84.

49. Li X, Lu C, Gao L, Xiao S, Wen L. An effective multiobjective algorithm for energy-efficient scheduling in a real-life welding shop. IEEE Trans Ind Inf 2018;14:5400-9.

50. Wang W, Zhou X, Tian G, et al. Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm. Sci Iran 2022;0:0-0.

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/