{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:15:18Z","timestamp":1783124118192,"version":"3.54.6"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173356"],"award-info":[{"award-number":["62173356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62573442"],"award-info":[{"award-number":["62573442"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515011531"],"award-info":[{"award-number":["2023A1515011531"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.114943","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T09:10:27Z","timestamp":1777972227000},"page":"114943","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Tri-objective distributed assembly flow shop scheduling with batch delivery: Indicator-Driven and reinforcement learning-enhanced artificial bee colony algorithms"],"prefix":"10.1016","volume":"177","author":[{"given":"Dachao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-6928","authenticated-orcid":false,"given":"Kaizhou","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ponnuthurai N.","family":"Suganthan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6782-458X","authenticated-orcid":false,"given":"Naiqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-6604","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.engappai.2026.114943_bib1","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.1109\/TCYB.2016.2586191","article-title":"Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and many-objective optimization","volume":"47","author":"Cai","year":"2016","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"10.1016\/j.engappai.2026.114943_bib2","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/TCYB.2023.3336656","article-title":"A reinforcement-learning-based 3-D estimation of distribution algorithm for fuzzy distributed hybrid flow-shop scheduling considering on-time-delivery","volume":"54","author":"Deng","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114943_bib3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.cor.2013.12.012","article-title":"On insertion tie-breaking rules in heuristics for the permutation flowshop scheduling problem","volume":"45","author":"Fernandez-Viagas","year":"2014","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.engappai.2026.114943_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109780","article-title":"Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems","volume":"120","author":"Fu","year":"2024","journal-title":"Comput. Electr. Eng."},{"issue":"1","key":"10.1016\/j.engappai.2026.114943_bib5","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1080\/00207543.2024.2356628","article-title":"Scheduling stochastic distributed flexible job shops using an multi-objective evolutionary algorithm with simulation evaluation","volume":"63","author":"Fu","year":"2025","journal-title":"Int. J. Prod. Res."},{"issue":"2","key":"10.1016\/j.engappai.2026.114943_bib6","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s40747-019-00122-6","article-title":"A review of energy-efficient scheduling in intelligent production systems","volume":"6","author":"Gao","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.engappai.2026.114943_bib7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.simpat.2017.09.001","article-title":"A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times","volume":"79","author":"Gonzalez-Neira","year":"2017","journal-title":"Simulat. Model. Pract. Theor."},{"key":"10.1016\/j.engappai.2026.114943_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2020.107021","article-title":"An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem","volume":"152","author":"Huang","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114943_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2022.101128","article-title":"A two-phase evolutionary algorithm for multi-objective distributed assembly permutation flowshop scheduling problem","volume":"74","author":"Huang","year":"2022","journal-title":"Swarm Evol. Comput."},{"issue":"4","key":"10.1016\/j.engappai.2026.114943_bib10","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/TEVC.2013.2281534","article-title":"An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach","volume":"18","author":"Jain","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib11","series-title":"An Idea Based on Honey Bee Swarm for Numerical Optimization","author":"Karaboga","year":"2005"},{"key":"10.1016\/j.engappai.2026.114943_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.108036","article-title":"A referenced iterated greedy algorithm for the distributed assembly mixed no-idle permutation flowshop scheduling problem with the total tardiness criterion","volume":"239","author":"Li","year":"2022","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114943_bib13","article-title":"Automatic fuzzy architecture design for defect detection via classifier-assisted multiobjective optimization approach","author":"Li","year":"2025","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib14","doi-asserted-by":"crossref","DOI":"10.1109\/TSMC.2025.3613727","article-title":"Reinforcement learning assisting artificial bee colony algorithm for scheduling distributed assembly flowshops with batch delivery","author":"Li","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib15","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.cie.2016.05.005","article-title":"An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem","volume":"97","author":"Lin","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114943_bib16","article-title":"A learning-based two-stage multi-thread iterated greedy algorithm for Co-Scheduling of distributed factories and automated guided vehicles with sequence-dependent setup times","author":"Liu","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"issue":"10","key":"10.1016\/j.engappai.2026.114943_bib17","doi-asserted-by":"crossref","first-page":"6687","DOI":"10.1109\/TII.2020.3043734","article-title":"Energy-efficient scheduling of distributed flow shop with heterogeneous factories: a real-world case from automobile industry in China","volume":"17","author":"Lu","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114943_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2024.101497","article-title":"A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities","volume":"85","author":"Luo","year":"2024","journal-title":"Swarm Evol. Comput."},{"issue":"11","key":"10.1016\/j.engappai.2026.114943_bib19","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1109\/TSMC.2020.2963943","article-title":"Enhancing learning efficiency of brain storm optimization via orthogonal learning design","volume":"51","author":"Ma","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"issue":"12","key":"10.1016\/j.engappai.2026.114943_bib20","doi-asserted-by":"crossref","first-page":"12698","DOI":"10.1109\/TCYB.2021.3086501","article-title":"Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system","volume":"52","author":"Ma","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"10.1016\/j.engappai.2026.114943_bib21","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1109\/TCYB.2020.3041212","article-title":"An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization","volume":"52","author":"Ma","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"10.1016\/j.engappai.2026.114943_bib22","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TASE.2022.3151648","article-title":"Improved meta-heuristics for solving distributed lot-streaming permutation flow shop scheduling problems","volume":"20","author":"Pan","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.engappai.2026.114943_bib23","doi-asserted-by":"crossref","DOI":"10.1109\/TSMC.2024.3370376","article-title":"A novel evolutionary algorithm for scheduling distributed no-wait flow shop problems","author":"Pan","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109851","article-title":"Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms","volume":"142","author":"Qiu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114943_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2021.107378","article-title":"Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain","volume":"158","author":"Rahman","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114943_bib26","doi-asserted-by":"crossref","DOI":"10.1109\/TSMC.2025.3647265","article-title":"An evolutionary algorithm with memory guidance for data transmission scheduling optimization in communication satellite network","author":"Ren","year":"2026","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib27","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.swevo.2018.12.001","article-title":"Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion","volume":"44","author":"Sang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107818","article-title":"A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time","volume":"131","author":"Shao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114943_bib29","doi-asserted-by":"crossref","DOI":"10.1109\/TSMC.2025.3572378","article-title":"Energy-efficiency oriented distributed heterogeneous hybrid flow shop scheduling with multilevelled mixed-model assembly","author":"Shao","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2020.100807","article-title":"A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times","volume":"60","author":"Song","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110022","article-title":"An effective hyper heuristic-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem","volume":"135","author":"Song","year":"2023","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.engappai.2026.114943_bib32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TSMC.2015.2416127","article-title":"An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem","volume":"46","author":"Wang","year":"2015","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"issue":"5","key":"10.1016\/j.engappai.2026.114943_bib33","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1109\/TSMC.2017.2788879","article-title":"A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop","volume":"50","author":"Wang","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108126","article-title":"A cooperative memetic algorithm with feedback for the energy-aware distributed flow-shops with flexible assembly scheduling","volume":"168","author":"Wang","year":"2022","journal-title":"Comput. Ind. Eng."},{"issue":"10","key":"10.1016\/j.engappai.2026.114943_bib35","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/TSMC.2017.2720178","article-title":"Permutation flow shop scheduling with batch delivery to multiple customers in supply chains","volume":"48","author":"Wang","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120909","article-title":"An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time","volume":"233","author":"Wang","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.engappai.2026.114943_bib37","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/TEVC.2023.3339558","article-title":"Sustainable scheduling of distributed flow shop group: a collaborative multi-objective evolutionary algorithm driven by indicators","volume":"28","author":"Wang","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib38","article-title":"Reinforcement learning-assisted memetic algorithm for sustainability-oriented multiobjective distributed flow shop group scheduling","author":"Wang","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"issue":"13","key":"10.1016\/j.engappai.2026.114943_bib39","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1080\/00207543.2020.1757174","article-title":"The distributed assembly permutation flowshop scheduling problem with flexible assembly and batch delivery","volume":"59","author":"Yang","year":"2021","journal-title":"Int. J. Prod. Res."},{"issue":"5","key":"10.1016\/j.engappai.2026.114943_bib40","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1080\/00207543.2024.2383781","article-title":"Minimising makespan in distributed assembly hybrid flowshop scheduling problems","volume":"63","author":"Ying","year":"2025","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.engappai.2026.114943_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2023.101335","article-title":"Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems","volume":"80","author":"Yu","year":"2023","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib42","article-title":"Double-learning-strategy-based evolutionary algorithm for scheduling multiobjective distributed assembly permutation flowshops with setup time","author":"Yu","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2020.100785","article-title":"A matrix-cube-based estimation of distribution algorithm for the distributed assembly permutation flow-shop scheduling problem","volume":"60","author":"Zhang","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116484","article-title":"A matrix cube-based estimation of distribution algorithm for the energy-efficient distributed assembly permutation flow-shop scheduling problem","volume":"194","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114943_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111295","article-title":"A self-learning knowledge-based MOEA\/D for distributed heterogeneous assembly permutation flowshop scheduling with batch delivery","volume":"284","author":"Zhang","year":"2024","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114943_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125690","article-title":"A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery","volume":"263","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"10.1016\/j.engappai.2026.114943_bib47","doi-asserted-by":"crossref","first-page":"12675","DOI":"10.1109\/TCYB.2021.3086181","article-title":"A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system","volume":"52","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"10.1016\/j.engappai.2026.114943_bib48","doi-asserted-by":"crossref","first-page":"6692","DOI":"10.1109\/TII.2022.3192881","article-title":"A population-based iterated greedy algorithm for distributed assembly no-wait flow-shop scheduling problem","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"5","key":"10.1016\/j.engappai.2026.114943_bib49","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1109\/TCYB.2022.3192112","article-title":"A hyperheuristic with Q-learning for the multiobjective energy-efficient distributed blocking flow shop scheduling problem","volume":"53","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"8","key":"10.1016\/j.engappai.2026.114943_bib50","doi-asserted-by":"crossref","first-page":"8588","DOI":"10.1109\/TII.2022.3220860","article-title":"A pareto-based discrete jaya algorithm for multiobjective carbon-efficient distributed blocking flow shop scheduling problem","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"4","key":"10.1016\/j.engappai.2026.114943_bib51","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TASE.2022.3212786","article-title":"A reinforcement learning driven artificial bee colony algorithm for distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup times","volume":"20","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"9","key":"10.1016\/j.engappai.2026.114943_bib52","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1080\/00207543.2022.2070786","article-title":"A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem","volume":"61","author":"Zhao","year":"2023","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.engappai.2026.114943_bib53","article-title":"A policy-based meta-heuristic algorithm for energy-aware distributed no-wait flow-shop scheduling in heterogeneous factory systems","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib54","article-title":"A Co-Evolution algorithm with dueling reinforcement learning mechanism for the energy-aware distributed heterogeneous flexible flow-shop scheduling problem","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114943_bib55","doi-asserted-by":"crossref","DOI":"10.1109\/TCYB.2025.3644904","article-title":"A hierarchical optimization algorithm with dual-cache synced tuning mechanism for distributed flexible job shop scheduling problem","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114943_bib56","article-title":"An iterative greedy algorithm for solving a multiobjective distributed assembly flexible job shop scheduling problem with fuzzy processing time","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114943_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111952","article-title":"Adaptive multi-population artificial bee colony algorithm based on fitness landscape analysis","volume":"164","author":"Zhou","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114943_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125283","article-title":"Artificial bee colony algorithm based on multi-neighbor guidance","volume":"259","author":"Zhou","year":"2025","journal-title":"Expert Syst. Appl."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095219762601225X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095219762601225X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:15:56Z","timestamp":1783120556000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095219762601225X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":58,"alternative-id":["S095219762601225X"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114943","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tri-objective distributed assembly flow shop scheduling with batch delivery: Indicator-Driven and reinforcement learning-enhanced artificial bee colony algorithms","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114943","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114943"}}