{"citation":{"bibtex":"@article{Tavana_Khalili-Damghani_Di Caprio_Oveisi_2018, title={An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems}, volume={30}, DOI={10.1007/s00521-016-2676-y}, number={1}, journal={Neural Computing and Applications}, publisher={Springer Science and Business Media LLC}, author={Tavana, Madjid and Khalili-Damghani, Kaveh and Di Caprio, Debora and Oveisi, Zeynab}, year={2018}, pages={127–139} }","ieee":"M. Tavana, K. Khalili-Damghani, D. Di Caprio, and Z. Oveisi, “An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems,” Neural Computing and Applications, vol. 30, no. 1, pp. 127–139, 2018, doi: 10.1007/s00521-016-2676-y.","mla":"Tavana, Madjid, et al. “An Evolutionary Computation Approach to Solving Repairable Multi-State Multi-Objective Redundancy Allocation Problems.” Neural Computing and Applications, vol. 30, no. 1, Springer Science and Business Media LLC, 2018, pp. 127–39, doi:10.1007/s00521-016-2676-y.","chicago":"Tavana, Madjid, Kaveh Khalili-Damghani, Debora Di Caprio, and Zeynab Oveisi. “An Evolutionary Computation Approach to Solving Repairable Multi-State Multi-Objective Redundancy Allocation Problems.” Neural Computing and Applications 30, no. 1 (2018): 127–39. https://doi.org/10.1007/s00521-016-2676-y.","ama":"Tavana M, Khalili-Damghani K, Di Caprio D, Oveisi Z. An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems. Neural Computing and Applications. 2018;30(1):127-139. doi:10.1007/s00521-016-2676-y","short":"M. Tavana, K. Khalili-Damghani, D. Di Caprio, Z. Oveisi, Neural Computing and Applications 30 (2018) 127–139.","apa":"Tavana, M., Khalili-Damghani, K., Di Caprio, D., & Oveisi, Z. (2018). An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems. Neural Computing and Applications, 30(1), 127–139. https://doi.org/10.1007/s00521-016-2676-y"},"author":[{"full_name":"Tavana, Madjid","first_name":"Madjid","id":"31858","last_name":"Tavana"},{"last_name":"Khalili-Damghani","full_name":"Khalili-Damghani, Kaveh","first_name":"Kaveh"},{"last_name":"Di Caprio","full_name":"Di Caprio, Debora","first_name":"Debora"},{"last_name":"Oveisi","full_name":"Oveisi, Zeynab","first_name":"Zeynab"}],"title":"An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems","user_id":"51811","volume":30,"_id":"54000","publication_identifier":{"issn":["0941-0643","1433-3058"]},"intvolume":" 30","language":[{"iso":"eng"}],"doi":"10.1007/s00521-016-2676-y","department":[{"_id":"277"}],"publisher":"Springer Science and Business Media LLC","date_updated":"2024-05-06T16:58:52Z","publication_status":"published","status":"public","year":"2018","issue":"1","page":"127-139","publication":"Neural Computing and Applications","type":"journal_article","date_created":"2024-05-06T16:49:59Z"}