{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:24Z","timestamp":1760145864019,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China University Industry University Research Innovation Fund","award":["2020ITA04008"],"award-info":[{"award-number":["2020ITA04008"]}]},{"name":"Ou Tang of Link\u00f6ping University, Swede","award":["2020ITA04008"],"award-info":[{"award-number":["2020ITA04008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task\u2013logistics\u2013resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks\u2019 priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.<\/jats:p>","DOI":"10.3390\/systems12090339","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Flow Shop Scheduling Method Based on Dual BP Neural Networks with Multi-Layer Topology Feature Parameters"],"prefix":"10.3390","volume":"12","author":[{"given":"Hui","family":"Mu","sequence":"first","affiliation":[{"name":"Jinan Vocational College, Jinan 250002, China"}]},{"given":"Zinuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Jinan Vocational College, Jinan 250002, China"}]},{"given":"Jiaqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road Construction Technology and Equipment of MOE, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Guoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Electronic Engineering Research Institute, Xi\u2019an 710100, China"}]},{"given":"Shaocun","family":"Wang","sequence":"additional","affiliation":[{"name":"Jinan Vocational College, Jinan 250002, China"}]},{"given":"Fuqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road Construction Technology and Equipment of MOE, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1080\/00207543.2020.1824085","article-title":"The applications of industry 4.0 technologies in manufacturing context: A systematic literature review","volume":"59","author":"Zheng","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_2","first-page":"25","article-title":"Smart logistics transformation collaboration between manufacturers and logistics service providers: A supply chain contracting perspective","volume":"6","author":"Liu","year":"2021","journal-title":"J. Manag. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/00207543.2019.1612964","article-title":"Logistics 4.0: A systematic review towards a new logistics system","volume":"58","author":"Winkelhaus","year":"2020","journal-title":"Int. J. Prod. Res."},{"doi-asserted-by":"crossref","unstructured":"Hu, Y., Wu, X., Zhai, J.J., Lou, P.H., Qian, X.M., and Xiao, H.N. (2022). Hybrid task allocation of an AGV system for task groups of an assembly line. Appl. Sci., 12.","key":"ref_4","DOI":"10.3390\/app122110956"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209452","DOI":"10.1109\/ACCESS.2020.3039515","article-title":"A collision and deadlock prevention method with traffic sequence optimization strategy for UGN-based AGVS","volume":"8","author":"Xiao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.jmsy.2016.12.001","article-title":"Complex networks in advanced manufacturing systems","volume":"43","author":"Li","year":"2017","journal-title":"J. Manuf. Syst."},{"doi-asserted-by":"crossref","unstructured":"Nagurney, A., and Dong, J. (2002). Supernetworks: Decision-Making for the Information Age, Edward Elgar Publishers.","key":"ref_7","DOI":"10.4337\/9781035352425"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5772","DOI":"10.1080\/00207543.2022.2104180","article-title":"Reinforcement learning applied to production planning and control","volume":"61","author":"Esteso","year":"2023","journal-title":"Int. J. Prod. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"130541","DOI":"10.1016\/j.jclepro.2022.130541","article-title":"An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance","volume":"337","author":"Zhou","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4382","DOI":"10.1109\/TASE.2023.3296087","article-title":"A branch and bound algorithm for scheduling of flexible manufacturing systems","volume":"21","author":"Ahn","year":"2023","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s10878-023-01046-1","article-title":"Fuzzy cleaner production in assembly flexible job-shop scheduling with machine breakdown and batch transportation: Lagrangian relaxation","volume":"45","author":"Hajibabaei","year":"2023","journal-title":"J. Comb. Optim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1109\/TSC.2013.30","article-title":"Improving heterogeneous SOA-based IoT message stability by shortest processing time scheduling","volume":"7","author":"Leu","year":"2014","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1214\/10-AAP681","article-title":"Heavy traffic analysis for EDF queues with reneging","volume":"21","author":"Kruk","year":"2011","journal-title":"Ann. Appl. Probab."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10951-018-0597-6","article-title":"The longest processing time rule for identical parallel machines revisited","volume":"23","author":"Scatamacchia","year":"2020","journal-title":"J. Sched."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"122594","DOI":"10.1016\/j.eswa.2023.122594","article-title":"MIP modeling of energy-conscious FJSP and its extended problems: From simplicity to complexity","volume":"241","author":"Meng","year":"2024","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Meng, L.L., Cheng, W.Y., Zhang, B., Zou, W.Q., Fang, W.K., and Duan, P. (2023). An improved genetic algorithm for solving the multi-AGV flexible job shop scheduling problem. Sensors, 23.","key":"ref_16","DOI":"10.3390\/s23083815"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1016\/j.ejor.2022.09.006","article-title":"A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources","volume":"306","author":"Fontes","year":"2023","journal-title":"Eur. J. Oper. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1177\/0954405415616785","article-title":"Energy-aware integration of process planning and scheduling of advanced machining workshop","volume":"231","author":"Zhang","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101374","DOI":"10.1016\/j.swevo.2023.101374","article-title":"MILP modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times","volume":"82","author":"Meng","year":"2023","journal-title":"Swarm Evol. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10951-022-00763-5","article-title":"Single machine scheduling with step-learning","volume":"27","author":"Atsmony","year":"2022","journal-title":"J. Sched."},{"doi-asserted-by":"crossref","unstructured":"Yang, Y.Q., Chen, X., Yang, M.L., Guo, W., and Jiang, P.Y. (2024). Designing an industrial product service system for robot-driven sanding processing line: A reinforcement learning based approach. Machines, 12.","key":"ref_21","DOI":"10.3390\/machines12020136"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5142","DOI":"10.1080\/00207543.2013.793476","article-title":"A neural network decision-making model for job-shop scheduling","volume":"51","author":"Golmohammadi","year":"2013","journal-title":"Int. J. Prod. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117460","DOI":"10.1016\/j.eswa.2022.117460","article-title":"An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown","volume":"203","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1504\/IJCSM.2020.106389","article-title":"Optimisation of makespan of a flow shop problem using multi layer neural network","volume":"11","author":"Kumar","year":"2020","journal-title":"Int. J. Comput. Sci. Math."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1142\/S0217595913500140","article-title":"Flow shop scheduling with reinforcement learning","volume":"30","author":"Zhang","year":"2013","journal-title":"Asia-Pac. J. Oper. Res."},{"doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Li, J.X., Jiao, Q.S., and Ma, F. (2024). Design patterns of deep reinforcement learning models for job shop scheduling problems. J. Intell. Manuf., preprint.","key":"ref_26","DOI":"10.1007\/s10845-024-02454-8"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10845-011-0538-0","article-title":"Complexity analysis of distributed measuring and sensing network in multistage machining processes","volume":"24","author":"Zhang","year":"2013","journal-title":"J. Intell. Manuf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cor.2018.11.019","article-title":"A flexible job shop scheduling approach with operators for coal export terminals","volume":"104","author":"Burdett","year":"2019","journal-title":"Comput. Oper. Res."},{"key":"ref_29","first-page":"13","article-title":"Determining the number of bp neural network hidden layer units","volume":"5","author":"Shen","year":"2008","journal-title":"J. Tianjin Univ. Technol."},{"key":"ref_30","first-page":"204","article-title":"Job-shop scheduling using artificial neural network","volume":"12","author":"Cao","year":"2016","journal-title":"Comput. Knowl. Technol."},{"unstructured":"Zou, M. (2019). Research on Complex Network Features Based Neural Network Scheduler for Job Shop Scheduling Problem. [Master\u2019s Thesis, Huazhong University of Science and Technology].","key":"ref_31"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/9\/339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:38Z","timestamp":1760111198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/9\/339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["systems12090339"],"URL":"https:\/\/doi.org\/10.3390\/systems12090339","relation":{},"ISSN":["2079-8954"],"issn-type":[{"type":"electronic","value":"2079-8954"}],"subject":[],"published":{"date-parts":[[2024,9,1]]}}}