{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T04:09:22Z","timestamp":1782446962046,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51875420"],"award-info":[{"award-number":["51875420"]}]},{"name":"National Natural Science Foundation of China","award":["51875421"],"award-info":[{"award-number":["51875421"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers\u2019 expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.<\/jats:p>","DOI":"10.3390\/s21144836","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T09:32:07Z","timestamp":1626341527000},"page":"4836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty"],"prefix":"10.3390","volume":"21","author":[{"given":"Liping","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuhua","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiong","family":"Li","sequence":"additional","affiliation":[{"name":"Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"},{"name":"Faculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1080\/00207543.2015.1086037","article-title":"Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors","volume":"55","author":"Zhong","year":"2017","journal-title":"Int. J. Prod. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s10951-008-0090-8","article-title":"A survey of dynamic scheduling in manufacturing systems","volume":"12","author":"Ouelhadj","year":"2009","journal-title":"J. Sched."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.energy.2017.07.005","article-title":"Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops","volume":"138","author":"Zhang","year":"2017","journal-title":"Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1109\/TII.2019.2944247","article-title":"Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm","volume":"16","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1007\/s00170-013-4867-3","article-title":"Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency","volume":"87","author":"Zhang","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100594","DOI":"10.1016\/j.swevo.2019.100594","article-title":"An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals","volume":"51","author":"Wang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/TASE.2019.2945717","article-title":"A Knowledge-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop with Sequencing Flexibility","volume":"18","author":"Cao","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejor.2020.04.017","article-title":"A meta-heuristic to solve the just-in-time job-shop scheduling problem","volume":"288","author":"Ahmadian","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"ref_9","unstructured":"Precup, R.-E., and David, R.-C. (2019). Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems, Elsevier."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22272","DOI":"10.1109\/ACCESS.2017.2764047","article-title":"A Simple Multi-Objective Optimization Based on the Cross-Entropy Method","volume":"5","author":"Haber","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1177\/0020294020948094","article-title":"An improved memetic algorithm for the flexible job shop scheduling problem with transportation times","volume":"53","author":"Zhang","year":"2020","journal-title":"Meas. Control."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cie.2018.05.021","article-title":"A minimax linear programming model for dispatching rule selection","volume":"121","author":"Amina","year":"2018","journal-title":"Comput. Ind. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"S41","DOI":"10.1057\/jors.2009.2","article-title":"Fifty years of scheduling: A survey of milestones","volume":"60","author":"Potts","year":"2009","journal-title":"J. Oper. Res. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ragazzini, L., Negri, E., Fumagalli, L., Macchi, M., and Koz\u0142owski, J. (2020, January 10\u201312). Tolerance Scheduling for CPS. Proceedings of the 2020 IEEE Conference on Industrial Cyber Physical Systems, Tampere, Finland.","DOI":"10.1109\/ICPS48405.2020.9274762"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.arcontrol.2021.04.008","article-title":"A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins","volume":"51","author":"Villalonga","year":"2021","journal-title":"Annu. Rev. Control."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s10845-017-1350-2","article-title":"Review of job shop scheduling research and its new perspectives under industry 4.0","volume":"30","author":"Zhang","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1287\/opre.25.1.45","article-title":"A survey of scheduling rules","volume":"25","author":"Panwalkar","year":"1977","journal-title":"Oper. Res."},{"key":"ref_19","first-page":"2848","article-title":"Evolving dispatching rules for solving the flexible job-shop problem","volume":"2","author":"Ho","year":"2005","journal-title":"IEEE Congr. Evol. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0925-5273(96)00068-0","article-title":"Efficient dispatching rules for scheduling in a job shop","volume":"48","author":"Holthaus","year":"1997","journal-title":"Int. J. Prod. Econ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0377-2217(98)00023-X","article-title":"A comparative study of dispatching rules in dynamic flow shops and job shops","volume":"116","author":"Rajendran","year":"1999","journal-title":"Eur. J. Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.eswa.2018.06.053","article-title":"A survey of dispatching rules for the dynamic unrelated machines environment","volume":"113","author":"Durasevic","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/00207548208947745","article-title":"A state-of-the survey of dispatching rules for manufacturing job shop operations","volume":"20","author":"Blackstone","year":"1982","journal-title":"Int. J. Prod. Res."},{"key":"ref_24","first-page":"70","article-title":"Efficient dispatching rules for dynamic job shop scheduling","volume":"24","author":"Dominic","year":"2004","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/BF01721162","article-title":"A survey of priority rule-based scheduling","volume":"11","author":"Haupt","year":"1989","journal-title":"Oper. Res. Spektrum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.simpat.2013.03.006","article-title":"An evolutionary simulation-based optimization approach for dispatching scheduling","volume":"35","author":"Korytkowski","year":"2013","journal-title":"Simul. Model. Pract. Ther."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1057\/s41273-016-0006-0","article-title":"Emulation of control strategies through machine learning in manufacturing simulations","volume":"11","author":"Bergmann","year":"2017","journal-title":"J. Simul."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1007\/s00357-018-9299-1","article-title":"Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine","volume":"36","author":"Tharwat","year":"2019","journal-title":"J. Classif."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1016\/j.asoc.2012.03.065","article-title":"Evolving priority scheduling heuristics with genetic programming","volume":"12","author":"Jakobovic","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TEVC.2012.2227326","article-title":"A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem","volume":"17","author":"Nguyen","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118289","DOI":"10.1016\/j.jclepro.2019.118289","article-title":"Mathematical modeling and multi-attribute rule mining for energy efficient job-shop scheduling","volume":"241","author":"Zhang","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_32","first-page":"1","article-title":"Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction","volume":"11","author":"Jun","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cie.2009.03.008","article-title":"Training a neural network to select dispatching rules in real time","volume":"58","author":"Pierreval","year":"2010","journal-title":"Comput. Ind. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s10100-013-0322-7","article-title":"Dispatching rule selection with Gaussian processes","volume":"23","author":"Jens","year":"2015","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1007\/s10845-018-1421-z","article-title":"A semantics-based dispatching rule selection approach for job shop scheduling","volume":"30","author":"Zhang","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2020.106208","article-title":"Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning","volume":"91","author":"Luo","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1080\/00207543.2019.1602744","article-title":"Big data driven jobs remaining time prediction in discrete manufacturing system: A deep learning based approach","volume":"58","author":"Fang","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1007\/s001700200087","article-title":"Reactive recovery of job shop schedules\u2014A review","volume":"19","author":"Raheja","year":"2002","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ejor.2011.07.022","article-title":"Real-Time production planning and control system for job-shop manufacturing: A system dynamics analysis","volume":"216","author":"Georgiadis","year":"2012","journal-title":"Eur. J. Oper. Res."},{"key":"ref_40","first-page":"112","article-title":"Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns","volume":"141","author":"Xiong","year":"2013","journal-title":"Int. J. Prod. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1080\/00207543.2012.751509","article-title":"A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem","volume":"51","author":"Zhang","year":"2013","journal-title":"Int. J. Prod. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1080\/00207540110057909","article-title":"Routing-based reactive scheduling policies for machine failures in dynamic job shops","volume":"39","author":"Kutanoglu","year":"2001","journal-title":"Int. J. Prod. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cie.2003.09.007","article-title":"Dynamic rescheduling that simultaneously considers efficiency and stability","volume":"46","author":"Rangsaritratsamee","year":"2004","journal-title":"Comput. Ind. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/00207540701486082","article-title":"Simulation-Based metamodels for scheduling a dynamic job shop with sequence-dependent setup times","volume":"47","author":"Vinod","year":"2009","journal-title":"Int. J. Prod. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1287\/mnsc.42.6.797","article-title":"A Fast Taboo Search Algorithm for the Job Shop Problem","volume":"42","author":"Nowicki","year":"1996","journal-title":"Manag. Sci."},{"key":"ref_46","first-page":"75","article-title":"Clouds, big data, and smart assets: Ten tech-enabled business trends to watch","volume":"56","author":"Bughin","year":"2010","journal-title":"McKinsey Q."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TII.2018.2873186","article-title":"Digital Twin in Industry: State-of-the-Art","volume":"15","author":"Tao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1007\/s00170-017-0459-y","article-title":"Using autonomous intelligence to build a smart shop floor","volume":"94","author":"Tang","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","article-title":"Data-Driven smart manufacturing","volume":"48","author":"Tao","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s00170-013-5017-7","article-title":"Modeling and impact factors analyzing of energy consumption in CNC face milling using GRASP gene expression programming","volume":"87","author":"Yang","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ferreira, C. (2002). Gene expression programming in problem solving. Soft Computing and Industry, Springer.","DOI":"10.1007\/978-1-4471-0123-9_54"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s10845-012-0626-9","article-title":"A GEP-Based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates","volume":"24","author":"Nie","year":"2013","journal-title":"J. Intell. Manuf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TEVC.2015.2424410","article-title":"Self-Learning gene expression programming","volume":"20","author":"Zhong","year":"2016","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:31:13Z","timestamp":1760164273000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,15]]},"references-count":53,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144836"],"URL":"https:\/\/doi.org\/10.3390\/s21144836","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,15]]}}}