{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T16:10:10Z","timestamp":1751731810219,"version":"3.41.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319959290"},{"type":"electronic","value":"9783319959306"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-95930-6_66","type":"book-chapter","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T06:12:40Z","timestamp":1530771160000},"page":"659-669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-objective Flexible Job Shop Scheduling Problem with Energy Consumption Constraint Using Imperialist Competitive Algorithm"],"prefix":"10.1007","author":[{"given":"Chengzhi","family":"Guo","sequence":"first","affiliation":[]},{"given":"Deming","family":"Lei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"issue":"3\u20135","key":"66_CR1","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0378-4754(02)00019-8","volume":"60","author":"I Kacem","year":"2002","unstructured":"Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Math. Comput. Simul. 60(3\u20135), 245\u2013276 (2002)","journal-title":"Math. Comput. Simul."},{"issue":"1","key":"66_CR2","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.cie.2007.04.010","volume":"53","author":"J Gao","year":"2007","unstructured":"Gao, J., Gen, M., Sun, L., Zhao, X.: A hybrid of genetic algorithm and bottleneck shifting for multi-objective flexible job shop scheduling problems. Comput. Ind. Eng. 53(1), 149\u2013162 (2007)","journal-title":"Comput. Ind. Eng."},{"issue":"1","key":"66_CR3","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1109\/TASE.2013.2274517","volume":"12","author":"Y Yuan","year":"2015","unstructured":"Yuan, Y., Xu, H.: Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Trans. Autom. Sci. Eng. 12(1), 336\u2013353 (2015)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"1","key":"66_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s00170-014-6415-1","volume":"77","author":"M Rohaninejad","year":"2015","unstructured":"Rohaninejad, M., Kheirkhah, A., Fattahi, P., Vahedi-Nouri, B.: A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 77(1), 51\u201366 (2015)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"3","key":"66_CR5","first-page":"587","volume":"50","author":"M Rohaninejad","year":"2015","unstructured":"Rohaninejad, M., Sahraeian, R., Nouri, B.V.: Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shop. PAIRO Oper. Res. 50(3), 587\u2013609 (2015)","journal-title":"PAIRO Oper. Res."},{"issue":"1","key":"66_CR6","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.cie.2016.10.012","volume":"102","author":"J Li","year":"2016","unstructured":"Li, J., Huang, Y., Niu, X.: A branch population genetic algorithm for dual-resource constrained job shop scheduling problem. Comput. Ind. Eng. 102(1), 113\u2013131 (2016)","journal-title":"Comput. Ind. Eng."},{"issue":"1","key":"66_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.cor.2016.03.009","volume":"73","author":"E Ahmadi","year":"2016","unstructured":"Ahmadi, E., Zandieh, M., Farrokh, M., Emami, S.M.: A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithm. Comput. Oper. Res. 73(1), 56\u201366 (2016)","journal-title":"Comput. Oper. Res."},{"key":"66_CR8","unstructured":"Shen, X.N., Han, Y., Fu, J.Z.: Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput. (2018, in press)"},{"issue":"1","key":"66_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ijpe.2010.08.004","volume":"129","author":"G Moslehi","year":"2011","unstructured":"Moslehi, G., Mahnam, M.: A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int. J. Prod. Econ. 129(1), 14\u201322 (2011)","journal-title":"Int. J. Prod. Econ."},{"issue":"9","key":"66_CR10","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1007\/s00170-015-8075-1","volume":"85","author":"MR Singh","year":"2016","unstructured":"Singh, M.R., Singh, M., Mahapatra, S.S., Jagadev, N.: Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 85(9), 2353\u20132366 (2016)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"66_CR11","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ins.2014.07.039","volume":"289","author":"KZ Gao","year":"2014","unstructured":"Gao, K.Z., Suganthan, P.N., Pan, Q.K., Chua, T.J., Cai, T.X., Chong, C.S.: Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Inf. Sci. 289(1), 76\u201390 (2014)","journal-title":"Inf. Sci."},{"issue":"3","key":"66_CR12","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1016\/j.apm.2013.07.038","volume":"38","author":"JQ Li","year":"2014","unstructured":"Li, J.Q., Pan, Q.K., Tasgetiren, M.F.: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance. Appl. Math. Model. 38(3), 1111\u20131132 (2014)","journal-title":"Appl. Math. Model."},{"issue":"1","key":"66_CR13","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cor.2014.01.010","volume":"47","author":"S Jia","year":"2014","unstructured":"Jia, S., Hu, Z.H.: Path-relinking tabu search for the multi-objective flexible job shop scheduling problem. Comput. Oper. Res. 47(1), 11\u201326 (2014)","journal-title":"Comput. Oper. Res."},{"issue":"1","key":"66_CR14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jmsy.2011.02.004","volume":"30","author":"A Bagheri","year":"2011","unstructured":"Bagheri, A., Zandieh, M.: Bi-criteria flexible job-shop scheduling with sequence-dependent setup times-variable neighborhood search approach. J. Manuf. Syst. 30(1), 8\u201315 (2011)","journal-title":"J. Manuf. Syst."},{"issue":"18","key":"66_CR15","first-page":"9353","volume":"218","author":"JQ Li","year":"2012","unstructured":"Li, J.Q., Pan, Q.K., Xie, S.X.: An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl. Math. Comput. 218(18), 9353\u20139371 (2012)","journal-title":"Appl. Math. Comput."},{"issue":"12","key":"66_CR16","doi-asserted-by":"publisher","first-page":"3574","DOI":"10.1080\/00207543.2012.752588","volume":"51","author":"L Wang","year":"2013","unstructured":"Wang, L., Wang, S.Y., Liu, M.: A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem. Int. J. Prod. Res. 51(12), 3574\u20133592 (2013)","journal-title":"Int. J. Prod. Res."},{"key":"66_CR17","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.jclepro.2018.02.004","volume":"181","author":"JQ Li","year":"2018","unstructured":"Li, J.Q., Sang, H.Y., Han, Y.Y., Wang, C.G., Gao, K.Z.: Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J. Cleaner Prod. 181, 584\u2013598 (2018)","journal-title":"J. Cleaner Prod."},{"issue":"1","key":"66_CR18","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.jclepro.2014.10.006","volume":"87","author":"Y He","year":"2015","unstructured":"He, Y., Li, Y.F., Wu, T., Sutherland, J.W.: An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J. Cleaner Prod. 87(1), 245\u2013254 (2015)","journal-title":"J. Cleaner Prod."},{"key":"66_CR19","first-page":"15","volume":"13","author":"LJ Yin","year":"2017","unstructured":"Yin, L.J., Li, X.Y., Gao, L., Lu, C., Zhang, Z.: A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustain. Comput. Inf. Syst. 13, 15\u201330 (2017)","journal-title":"Sustain. Comput. Inf. Syst."},{"issue":"11","key":"66_CR20","doi-asserted-by":"publisher","first-page":"3126","DOI":"10.1080\/00207543.2016.1262082","volume":"55","author":"DM Lei","year":"2017","unstructured":"Lei, D.M., Zheng, Y.L., Guo, X.P.: A shuffled frog leaping algorithm for flexible job shop scheduling with the consideration of energy consumption. Int. J. Prod. Res. 55(11), 3126\u20133140 (2017)","journal-title":"Int. J. Prod. Res."},{"key":"66_CR21","first-page":"339","volume":"104","author":"H Mokhtari","year":"2017","unstructured":"Mokhtari, H., Hasani, A.: An energy-efficient multi-objective optimization for flexible job shop scheduling. Comput. Ind. Eng. 104, 339\u2013352 (2017)","journal-title":"Comput. Ind. Eng."},{"key":"66_CR22","doi-asserted-by":"crossref","unstructured":"Lei, D.M., Li, M., Wang, L.: A two-phase meta-heuristic for multi-objective flexible job shop scheduling problem with total energy consumption threshold. IEEE Trans. Cybern. (2018, in press)","DOI":"10.1109\/TCYB.2018.2796119"},{"key":"66_CR23","unstructured":"Lei, D.M, Yang, D.J.: Research on flexible job shop scheduling problem with total energy consumption constraint. ACTA Autom. Sinica (2018, in press). (in Chinese)"},{"issue":"8","key":"66_CR24","doi-asserted-by":"publisher","first-page":"4991","DOI":"10.1016\/j.asoc.2011.06.001","volume":"11","author":"DM Lei","year":"2011","unstructured":"Lei, D.M.: Simplified multi-objective genetic algorithm for stochastic job shop scheduling. Appl. Soft Comput. 11(8), 4991\u20134996 (2011)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"66_CR25","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(1), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"66_CR26","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gagari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialist competition. In: IEEE Congress on Evolutionary Computation, Singapore, pp. 4661\u20134667 (2007)","DOI":"10.1109\/CEC.2007.4425083"},{"issue":"23","key":"66_CR27","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1016\/j.apm.2013.05.002","volume":"37","author":"SM Goldansaz","year":"2013","unstructured":"Goldansaz, S.M., Jolai, F., Anaraki, A.H.Z.: A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl. Math. Model. 37(23), 9603\u20139616 (2013)","journal-title":"Appl. Math. Model."},{"issue":"4","key":"66_CR28","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.jmsy.2014.06.002","volume":"33","author":"B Naderi","year":"2014","unstructured":"Naderi, B., Yazdani, M.: A model and imperialist competitive algorithm for hybrid flow shops with sublots and setup times. J. Manuf. Syst. 33(4), 647\u2013653 (2014)","journal-title":"J. Manuf. Syst."},{"key":"66_CR29","first-page":"221","volume-title":"Lecture Notes in Computer Science","author":"Behrooz Ghasemishabankareh","year":"2016","unstructured":"Ghasemishabankareh, B., Shahsavari-Pour, N., Basiri, M.A., Li, X.D.: A hybrid imperialist competitive algorithm for the flexible job shop problem. Ray, T., et al. (eds.) ACALCI 2016, LNAI 9592, pp. 221\u2013233 (2016)"},{"issue":"1","key":"66_CR30","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/BF02023073","volume":"41","author":"P Brandimarte","year":"1993","unstructured":"Brandimarte, P.: Routing and scheduling in a flexible job shop by tabu search. Ann. Oper. Res. 41(1), 157\u2013183 (1993)","journal-title":"Ann. Oper. Res."},{"key":"66_CR31","doi-asserted-by":"crossref","unstructured":"Knowles, J.D., Corne, D.W.: On metrics for comparing non-dominated sets. In: Proceedings of 2002 Congress on Evolutionary Computation, Honolulu, 12\u201317 May, pp. 711\u2013716 (2002)","DOI":"10.1109\/CEC.2002.1007013"},{"issue":"4","key":"66_CR32","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257\u2013271 (1999)","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-95930-6_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T15:38:55Z","timestamp":1751729935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-95930-6_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319959290","9783319959306"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-95930-6_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2018\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"LOD","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"632","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"275","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.46","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}