{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:45:16Z","timestamp":1784738716454,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10725-x","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T14:26:28Z","timestamp":1733754388000},"page":"2775-2798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hybrid fuzzy genetic algorithm for the integration of process planning and scheduling for distributed flexible job shop"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0478-0628","authenticated-orcid":false,"given":"Murad","family":"Samhouri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1255-5340","authenticated-orcid":false,"given":"Sarah Z.","family":"Qareish","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"10725_CR1","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1080\/0951192X.2011.562543","volume":"24","author":"RK Phanden","year":"2011","unstructured":"Phanden RK, Jain A, Verma R (2011) Integration of process planning and scheduling: a state-of-the-art review. Int J Comput Integr Manuf 24:517\u2013534. https:\/\/doi.org\/10.1080\/0951192X.2011.562543","journal-title":"Int J Comput Integr Manuf"},{"key":"10725_CR2","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1504\/IJMR.2010.031630","volume":"5","author":"X Li","year":"2010","unstructured":"Li X, Gao L, Zhang C, Shao X (2010) A review on integrated process planning and scheduling. IJMR 5:161\u2013180. https:\/\/doi.org\/10.1504\/IJMR.2010.031630","journal-title":"IJMR"},{"key":"10725_CR3","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.eswa.2016.08.019","volume":"64","author":"M Petrovi\u0107","year":"2016","unstructured":"Petrovi\u0107 M, Vukovi\u0107 N, Miti\u0107 M, Miljkovi\u0107 Z (2016) Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. Expert Syst Appl 64:569\u2013588. https:\/\/doi.org\/10.1016\/j.eswa.2016.08.019","journal-title":"Expert Syst Appl"},{"key":"10725_CR4","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf Control 8:338\u2013353. https:\/\/doi.org\/10.1016\/S0019-9958(65)90241-X","journal-title":"Inf Control"},{"key":"10725_CR5","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/91.995115","volume":"10","author":"JM Mendel","year":"2002","unstructured":"Mendel JM, John RIB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10:117\u2013127. https:\/\/doi.org\/10.1109\/91.995115","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"10725_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2020.105053","volume":"124","author":"C-S Lin","year":"2020","unstructured":"Lin C-S, Li P-Y, Wei J-M, Wu M-C (2020) Integration of process planning and scheduling for distributed flexible job shops. Comput Oper Res 124:105053. https:\/\/doi.org\/10.1016\/j.cor.2020.105053","journal-title":"Comput Oper Res"},{"key":"10725_CR7","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.24507\/ijicic.11.05.1587","volume":"11","author":"S Zhang","year":"2015","unstructured":"Zhang S, Yu Z, Zhang W et al (2015) Distributed integration of process planning and scheduling using an enhanced genetic algorithm. Int J Innov Comput Inf Control 11:1587. https:\/\/doi.org\/10.24507\/ijicic.11.05.1587","journal-title":"Int J Innov Comput Inf Control"},{"key":"10725_CR8","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.3233\/IFS-141467","volume":"28","author":"M Vannucci","year":"2015","unstructured":"Vannucci M, Colla V (2015) Fuzzy adaptation of crossover and mutation rates in genetic algorithms based on population performance. J Intell Fuzzy Syst 28:1805\u20131818. https:\/\/doi.org\/10.3233\/IFS-141467","journal-title":"J Intell Fuzzy Syst"},{"key":"10725_CR9","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1515\/jisys-2016-0343","volume":"29","author":"M Vannucci","year":"2020","unstructured":"Vannucci M, Colla V, Dettori S, Iannino V (2020) Fuzzy adaptive genetic algorithm for improving the solution of industrial optimization problems. J Intell Syst 29:409\u2013422. https:\/\/doi.org\/10.1515\/jisys-2016-0343","journal-title":"J Intell Syst"},{"key":"10725_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/BF01471363","volume":"2","author":"B Khoshnevis","year":"1991","unstructured":"Khoshnevis B, Chen QM (1991) Integration of process planning and scheduling functions. J Intell Manuf 2:165\u2013175. https:\/\/doi.org\/10.1007\/BF01471363","journal-title":"J Intell Manuf"},{"key":"10725_CR11","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1023\/A:1008952024606","volume":"11","author":"W Tan","year":"2000","unstructured":"Tan W, Khoshnevis B (2000) Integration of process planning and scheduling\u2014a review. J Intell Manuf 11:51\u201363. https:\/\/doi.org\/10.1023\/A:1008952024606","journal-title":"J Intell Manuf"},{"key":"10725_CR12","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.jmsy.2022.01.006","volume":"63","author":"W Weng","year":"2022","unstructured":"Weng W, Chen J, Zheng M, Fujimura S (2022) Realtime scheduling heuristics for just-in-time production in large-scale flexible job shops. J Manuf Syst 63:64\u201377. https:\/\/doi.org\/10.1016\/j.jmsy.2022.01.006","journal-title":"J Manuf Syst"},{"key":"10725_CR13","doi-asserted-by":"crossref","unstructured":"Zhang X (2022) A scheduling model of intelligent manufacturing system based on GA optimization. In: 2022 14th international conference on measuring technology and mechatronics automation (ICMTMA). IEEE, Changsha, China, pp 454\u2013457","DOI":"10.1109\/ICMTMA54903.2022.00095"},{"key":"10725_CR14","doi-asserted-by":"crossref","unstructured":"Awad MA, Abd-Elaziz HM (2021) An efficient modified genetic algorithm for integrated process planning-job scheduling. In: 2021 international mobile, intelligent, and ubiquitous computing conference (MIUCC). IEEE, Cairo, Egypt, pp 319\u2013323","DOI":"10.1109\/MIUCC52538.2021.9447610"},{"key":"10725_CR15","doi-asserted-by":"publisher","first-page":"3775","DOI":"10.1080\/00207540701827905","volume":"47","author":"YW Guo","year":"2009","unstructured":"Guo YW, Li WD, Mileham AR, Owen GW (2009) Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach. Int J Prod Res 47:3775\u20133796. https:\/\/doi.org\/10.1080\/00207540701827905","journal-title":"Int J Prod Res"},{"key":"10725_CR16","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.cie.2009.09.003","volume":"59","author":"CW Leung","year":"2010","unstructured":"Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Comput Ind Eng 59:166\u2013180. https:\/\/doi.org\/10.1016\/j.cie.2009.09.003","journal-title":"Comput Ind Eng"},{"key":"10725_CR17","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3390\/a17040142","volume":"17","author":"SK Fuladi","year":"2024","unstructured":"Fuladi SK, Kim C-S (2024) Dynamic events in the flexible job-shop scheduling problem: rescheduling with a hybrid metaheuristic algorithm. Algorithms 17:142. https:\/\/doi.org\/10.3390\/a17040142","journal-title":"Algorithms"},{"key":"10725_CR18","doi-asserted-by":"publisher","DOI":"10.1177\/09544089231209145","author":"A Barua","year":"2023","unstructured":"Barua A, Jeet S, Bagal DK, Pattanaik AK (2023) A hybrid GRA-FIS based genetic algorithm and simulated annealing approach for camshaft material optimization using finite element analysis. Proc Inst Mech Eng Part E J Process Mech Eng. https:\/\/doi.org\/10.1177\/09544089231209145","journal-title":"Proc Inst Mech Eng Part E J Process Mech Eng"},{"key":"10725_CR19","doi-asserted-by":"crossref","unstructured":"Savage J, Mu\u00f1oz S, Contreras L et al (2021) Generating reactive robots\u2019 behaviors using genetic algorithms. pp 698\u2013707","DOI":"10.5220\/0010229306980707"},{"key":"10725_CR20","doi-asserted-by":"publisher","first-page":"451","DOI":"10.3233\/JIFS-219203","volume":"42","author":"O Engin","year":"2022","unstructured":"Engin O, Y\u0131lmaz MK (2022) A fuzzy logic based methodology for multi-objective hybrid flow shop scheduling with multi-processor tasks problems and solving with an efficient genetic algorithm. J Intell Fuzzy Syst 42:451\u2013463. https:\/\/doi.org\/10.3233\/JIFS-219203","journal-title":"J Intell Fuzzy Syst"},{"key":"10725_CR21","doi-asserted-by":"crossref","unstructured":"Ausaf MF, Li X, Gao L (2014) Optimization algorithms for integrated process planning and scheduling problem\u2014a survey. In: Proceeding of the 11th world congress on intelligent control and automation. IEEE, Shenyang, China, pp 5287\u20135292","DOI":"10.1109\/WCICA.2014.7053616"},{"key":"10725_CR22","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1008976720878","volume":"10","author":"N Morad","year":"1999","unstructured":"Morad N, Zalzala A (1999) Genetic algorithm in integrated process planning and scheduling. J Intell Manuf 10:169\u2013179. https:\/\/doi.org\/10.1023\/A:1008976720878","journal-title":"J Intell Manuf"},{"key":"10725_CR23","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/S0305-0548(02)00063-1","volume":"30","author":"YK Kim","year":"2003","unstructured":"Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151\u20131171. https:\/\/doi.org\/10.1016\/S0305-0548(02)00063-1","journal-title":"Comput Oper Res"},{"key":"10725_CR24","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1016\/j.jclepro.2016.03.101","volume":"137","author":"Z Zhang","year":"2016","unstructured":"Zhang Z, Tang R, Peng T et al (2016) A method for minimizing the energy consumption of machining system: integration of process planning and scheduling. J Clean Prod 137:1647\u20131662. https:\/\/doi.org\/10.1016\/j.jclepro.2016.03.101","journal-title":"J Clean Prod"},{"key":"10725_CR25","doi-asserted-by":"publisher","first-page":"390","DOI":"10.3390\/info10120390","volume":"10","author":"A Hassanat","year":"2019","unstructured":"Hassanat A, Almohammadi K, Alkafaween E et al (2019) Choosing mutation and crossover ratios for genetic algorithms\u2014a review with a new dynamic approach. Information 10:390. https:\/\/doi.org\/10.3390\/info10120390","journal-title":"Information"},{"key":"10725_CR26","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1108\/03684921011063510","volume":"39","author":"Z Peng","year":"2010","unstructured":"Peng Z, Song B (2010) Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network. Kybernetes 39:1235\u20131244. https:\/\/doi.org\/10.1108\/03684921011063510","journal-title":"Kybernetes"},{"key":"10725_CR27","doi-asserted-by":"publisher","first-page":"334","DOI":"10.3390\/a15100334","volume":"15","author":"EA Campo","year":"2022","unstructured":"Campo EA, Cano JA, G\u00f3mez-Montoya R et al (2022) Flexible job shop scheduling problem with fuzzy times and due-windows: minimizing weighted tardiness and earliness using genetic algorithms. Algorithms 15:334. https:\/\/doi.org\/10.3390\/a15100334","journal-title":"Algorithms"},{"key":"10725_CR28","doi-asserted-by":"publisher","DOI":"10.38007\/IJMC.2022.030404","author":"Y Han","year":"2022","unstructured":"Han Y (2022) Super heuristic genetic algorithm for fuzzy flexible job shop scheduling. IJMC. https:\/\/doi.org\/10.38007\/IJMC.2022.030404","journal-title":"IJMC"},{"key":"10725_CR29","doi-asserted-by":"crossref","unstructured":"Wang X, Liu C (2022) A hybrid genetic algorithm for flexible job shop scheduling problem. In: 2022 5th world conference on mechanical engineering and intelligent manufacturing (WCMEIM). IEEE, Ma\u2019anshan, China, pp 1144\u20131148","DOI":"10.1109\/WCMEIM56910.2022.10021523"},{"key":"10725_CR30","doi-asserted-by":"publisher","first-page":"4700","DOI":"10.3390\/math11224700","volume":"11","author":"M Jiang","year":"2023","unstructured":"Jiang M, Yu H, Chen J (2023) Improved self-learning genetic algorithm for solving flexible job shop scheduling. Mathematics 11:4700. https:\/\/doi.org\/10.3390\/math11224700","journal-title":"Mathematics"},{"key":"10725_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119077","volume":"213","author":"A Goli","year":"2023","unstructured":"Goli A, Ala A, Hajiaghaei-Keshteli M (2023) Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem. Expert Syst Appl 213:119077. https:\/\/doi.org\/10.1016\/j.eswa.2022.119077","journal-title":"Expert Syst Appl"},{"key":"10725_CR32","doi-asserted-by":"publisher","first-page":"367","DOI":"10.3233\/ICA-220685","volume":"29","author":"V Iannino","year":"2022","unstructured":"Iannino V, Colla V, Maddaloni A et al (2022) A hybrid approach for improving the flexibility of production scheduling in flat steel industry. Integr Comput Aid Eng 29:367\u2013387. https:\/\/doi.org\/10.3233\/ICA-220685","journal-title":"Integr Comput Aid Eng"},{"key":"10725_CR33","doi-asserted-by":"publisher","first-page":"209","DOI":"10.12700\/APH.19.4.2022.4.11","volume":"19","author":"A Stankovi\u0107","year":"2022","unstructured":"Stankovi\u0107 A, Petrovi\u0107 G, Markovi\u0107 D, \u0106ojba\u0161i\u0107 \u017d (2022) Solving Flexible job shop scheduling problem with transportation time based on neuro -fuzzy suggested metaheuristics. Acta Polytech Hung 19:209\u2013227. https:\/\/doi.org\/10.12700\/APH.19.4.2022.4.11","journal-title":"Acta Polytech Hung"},{"key":"10725_CR34","doi-asserted-by":"publisher","first-page":"505","DOI":"10.4301\/S1807-17752015000300001","volume":"12","author":"HCW Lau","year":"2015","unstructured":"Lau HCW, Nakandala D, Zhao L (2015) Development of a hybrid fuzzy genetic algorithm model for solving transportation scheduling problem. J Inf Syst Technol Manag 12:505\u2013524. https:\/\/doi.org\/10.4301\/S1807-17752015000300001","journal-title":"J Inf Syst Technol Manag"},{"key":"10725_CR35","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10845-020-01659-x","volume":"33","author":"X Zhang","year":"2022","unstructured":"Zhang X, Liao Z, Ma L, Yao J (2022) Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling. J Intell Manuf 33:223\u2013246. https:\/\/doi.org\/10.1007\/s10845-020-01659-x","journal-title":"J Intell Manuf"},{"key":"10725_CR36","doi-asserted-by":"crossref","unstructured":"Shukla A, Pandey HM, Mehrotra D (2015) Comparative review of selection techniques in genetic algorithm. In: 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE). IEEE, Greater Noida, India, pp 515\u2013519","DOI":"10.1109\/ABLAZE.2015.7154916"},{"key":"10725_CR37","volume-title":"Genetic algorithms in search, optimization, and machine learning","author":"DE Goldberg","year":"1989","unstructured":"Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co. Inc, Boston"},{"key":"10725_CR38","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/B0-12-227055-X\/00103-6","volume-title":"Encyclopedia of food sciences and nutrition","author":"S Zydenbos","year":"2003","unstructured":"Zydenbos S, Humphrey-Taylor V (2003) Biscuits, cookies, and crackers|nature of the products. In: Caballero B (ed) Encyclopedia of food sciences and nutrition, 2nd edn. Academic Press, Oxford, pp 524\u2013528","edition":"2"},{"key":"10725_CR39","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/309\/1\/012042","volume":"309","author":"T Marwati","year":"2019","unstructured":"Marwati T, Hendrawanto PP, Widodo S et al (2019) GMP Implementation and CCP Determination on Chocolate Candy Processing in ATP Nglanggeran Yogyakarta. IOP Conf Ser: Earth Environ Sci 309:012042. https:\/\/doi.org\/10.1088\/1755-1315\/309\/1\/012042","journal-title":"IOP Conf Ser: Earth Environ Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10725-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10725-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10725-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T07:41:17Z","timestamp":1738827677000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10725-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10725"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10725-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]},"assertion":[{"value":"20 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}