{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:36:20Z","timestamp":1772087780759,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu industry university research project","award":["BY20240828"],"award-info":[{"award-number":["BY20240828"]}]},{"name":"Jiangsu Provincial Basic Research Program Natural Science Foundation - Youth Fund Project","award":["BK20230173"],"award-info":[{"award-number":["BK20230173"]}]},{"name":"Basic Research Program of Jiangsu","award":["BK20240316"],"award-info":[{"award-number":["BK20240316"]}]},{"name":"General Project of Basic Science (NATURAL SCIENCE) Research in Colleges and Universities of Jiangsu Province","award":["23kjb460031"],"award-info":[{"award-number":["23kjb460031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the increasing demand for personalized services in the market, manufacturing enterprises are facing frequent emergency order insertion and equipment resource shortages, and traditional scheduling methods lack flexibility. This article focuses on the workshop scheduling problem under emergency insertion disturbance, and constructs a dynamic scheduling optimization method considering equipment occupancy status. Firstly, a dynamic scheduling framework is proposed, and a real-time status model is established to monitor emergency insertion and equipment occupancy status in real time. An event-driven dynamic scheduling mechanism is also constructed. Secondly, with the optimization objective of minimizing the maximum completion time, a mixed integer programming model is established, and an improved genetic simulated annealing algorithm is proposed to solve the proposed model. Finally, the proposed method was validated using a standard case set and real production scenarios. The experimental results showed that the solution of the proposed method was better than similar algorithms under three different problem scales. In three emergency insertion scenarios, the proposed method can reduce the disturbance of insertion on the original plan while ensuring equipment utilization, verifying the practicality and effectiveness of the proposed dynamic scheduling method.<\/jats:p>","DOI":"10.3390\/a19020156","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:14:32Z","timestamp":1771236872000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Workshop Dynamic Scheduling Method Considering Equipment Occupation Under Emergency Insertion Order"],"prefix":"10.3390","volume":"19","author":[{"given":"Xuan","family":"Su","sequence":"first","affiliation":[{"name":"School of Automation, Wuxi University, Wuxi 214105, China"},{"name":"Wuxi Key Laboratory of Intelligent Manufacturing Technology for Core Components of High-End Equipment, Wuxi 214105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jitai","family":"Han","sequence":"additional","affiliation":[{"name":"School of Automation, Wuxi University, Wuxi 214105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Gu","sequence":"additional","affiliation":[{"name":"Wuxi Weiming Intelligent Technology Co., Ltd., Wuxi 214104, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Automation, Wuxi University, Wuxi 214105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115339","DOI":"10.1016\/j.eswa.2021.115339","article-title":"Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time","volume":"183","author":"Rifai","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1080\/17517575.2018.1545160","article-title":"Integrated scheduling for a distributed manufacturing system: A stochastic multi-objective model","volume":"13","author":"Fu","year":"2019","journal-title":"Enterp. Inf. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2396","DOI":"10.1016\/j.ifacol.2019.11.565","article-title":"An hybrid SA-DATC approach for JIT open-shop scheduling problem with earliness and tardiness penalties","volume":"52","author":"Meddourene","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Ji, W., and Zhang, C. (2019). A hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness of multi-variety products. Appl. Sci., 9.","DOI":"10.3390\/app9245286"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1108\/IJICC-10-2018-0136","article-title":"A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time","volume":"12","author":"Huang","year":"2019","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/TASE.2024.3369019","article-title":"A two-stage individual feedback NSGA-III for dynamic many-objective flexible job shop scheduling problem","volume":"22","author":"Feng","year":"2024","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, H., Qin, C., Zhang, W., Zhuang, C., and Liu, J. (2023). Energy-saving scheduling for flexible job shop problem with AGV transportation considering emergencies. Systems, 11.","DOI":"10.3390\/systems11020103"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110688","DOI":"10.1016\/j.cie.2024.110688","article-title":"A novel method for solving dynamic flexible job-shop scheduling problem via DIFFormer and deep reinforcement learning","volume":"198","author":"Wan","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s10696-022-09454-x","article-title":"Improving coordination in assembly job shops: Redesigning order release and dispatching","volume":"35","author":"Liu","year":"2023","journal-title":"Flex. Serv. Manuf. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y., Zhang, Z., and Xu, J. (2024). Dynamic integrated scheduling of production equipment and automated guided vehicles in a flexible job shop based on deep reinforcement learning. Processes, 12.","DOI":"10.3390\/pr12112423"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cie.2019.01.036","article-title":"Using real-time information to reschedule jobs in a flowshop with variable processing times","volume":"129","author":"Framinan","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/0951192X.2017.1407455","article-title":"Towards flexible RFID event-driven integrated manufacturing for make-to-order production","volume":"31","author":"Yao","year":"2018","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1080\/00207543.2019.1581954","article-title":"Learning dispatching rules using random forest in flexible job shop scheduling problems","volume":"57","author":"Jun","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1080\/0951192X.2019.1571241","article-title":"A proactive task dispatching method based on future bottleneck prediction for the smart factory","volume":"32","author":"Huang","year":"2019","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112461","DOI":"10.1016\/j.asoc.2024.112461","article-title":"A reinforcement learning hyper-heuristic algorithm for the distributed flowshops scheduling problem under consideration of emergency order insertion","volume":"167","author":"Zhao","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3645","DOI":"10.1007\/s12190-024-02364-1","article-title":"A Q-learning grey wolf optimizer for a distributed hybrid flowshop rescheduling problem with urgent job insertion","volume":"71","author":"Chen","year":"2025","journal-title":"J. Appl. Math. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cie.2017.09.005","article-title":"Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems","volume":"113","author":"Li","year":"2017","journal-title":"Comput. Ind. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1080\/00207543.2019.1613581","article-title":"Research on rush order insertion rescheduling problem under hybrid flow shop based on NSGA-III","volume":"58","author":"He","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106505","DOI":"10.1016\/j.cie.2020.106505","article-title":"Meta-heuristics for unrelated parallel machines scheduling with random rework to minimize expected total weighted tardiness","volume":"145","author":"Wang","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106060","DOI":"10.1016\/j.cie.2019.106060","article-title":"Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival","volume":"137","author":"Chen","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/00207543.2018.1543964","article-title":"Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming","volume":"57","author":"Ozturk","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5999","DOI":"10.1109\/TCYB.2020.3041494","article-title":"An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems","volume":"52","author":"Pan","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100800","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":"ref_24","first-page":"1497","article-title":"Distributed Heterogeneous Co-Evolutionary Algorithm for Scheduling a Multistage Fine-Manufacturing System With Setup Constraints","volume":"52","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"117324","DOI":"10.1016\/j.eswa.2022.117269","article-title":"A knowledge-driven constructive heuristic algorithm for the distributed assembly blocking flow shop scheduling problem","volume":"202","author":"Yang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108066","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."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125168","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."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107392","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":"ref_29","doi-asserted-by":"crossref","first-page":"116515","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":"ref_30","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cie.2015.08.002","article-title":"A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem","volume":"88","author":"Gao","year":"2015","journal-title":"Comput. Ind. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100664","DOI":"10.1016\/j.swevo.2020.100664","article-title":"An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints","volume":"54","author":"Zhang","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10951-017-0547-8","article-title":"The current state of bounds on benchmark instances of the job-shop scheduling problem","volume":"21","year":"2018","journal-title":"J. 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