{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:31:51Z","timestamp":1772181111531,"version":"3.50.1"},"reference-count":114,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["52005042"],"award-info":[{"award-number":["52005042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012236","name":"Beijing Institute of Technology Research Fund Program for Young Scholars","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012236","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.aei.2022.101756","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:21:50Z","timestamp":1664148110000},"page":"101756","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"special_numbering":"C","title":["Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review"],"prefix":"10.1016","volume":"54","author":[{"given":"Haoxin","family":"Guo","sequence":"first","affiliation":[]},{"given":"Jianhua","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Cunbo","family":"Zhuang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0005","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/nav.3800010110","article-title":"Optimal two-and three-stage production schedules with setup times included","volume":"1","author":"Johnson","year":"1954","journal-title":"Naval Research Logist. Quart."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0010","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1287\/opre.6.1.35","article-title":"Determination of Feasible Shipping Schedules for a Job Shop","volume":"6","author":"Karush","year":"1958","journal-title":"Oper. Res."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0015","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1467-8640.1985.tb00058.x","article-title":"What is a heuristic?","volume":"1","author":"Romanycia","year":"1985","journal-title":"Comput. Intell."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0020","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1057\/jors.1965.8","article-title":"Sequencing jobs through a multi-stage process in the minimum total time\u2014a quick method of obtaining a near optimum","volume":"16","author":"Palmer","year":"1965","journal-title":"J. Oper. Res. Soc."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0025","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/05695557108974812","article-title":"An improved method for scheduling independent tasks","volume":"3","author":"Wilkerson","year":"1971","journal-title":"AIIE Trans."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0030","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1287\/opre.22.3.629","article-title":"Heuristic-programming solution of a flowshop-scheduling problem","volume":"22","author":"Krone","year":"1974","journal-title":"Oper. Res."},{"key":"10.1016\/j.aei.2022.101756_b0035","first-page":"293","article-title":"A Heuristic Solution Procedure to Minimize T on a Single Machine","volume":"40","author":"Fry","year":"1989","journal-title":"J. Oper. Res. Soc."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0040","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/00207549308956713","article-title":"Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling","volume":"31","author":"Maccarthy","year":"1993","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2022.101756_b0045","series-title":"Scheduling: Theory, Algorithms, and Systems","author":"Pinedo","year":"2008"},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0050","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1137\/0202009","article-title":"Genetic algorithms and the optimal allocation of trials","volume":"2","author":"Holland","year":"1973","journal-title":"SIAM J. Comput."},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0055","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.ejor.2019.01.018","article-title":"Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems","volume":"276","author":"Pagnozzi","year":"2019","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.aei.2022.101756_b0060","series-title":"Soft Computing for Problem Solving","first-page":"35","article-title":"Toward automatic scheduling algorithm with hash-based priority selection strategy","author":"Ji","year":"2020"},{"key":"10.1016\/j.aei.2022.101756_b0065","article-title":"A Classification of Hyper-heuristic Approaches","volume":"vol. 146","author":"Burke","year":"2010"},{"issue":"12","key":"10.1016\/j.aei.2022.101756_b0070","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1057\/jors.2013.71","article-title":"Hyper-heuristics: A survey of the state of the art","volume":"64","author":"Burke","year":"2013","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.aei.2022.101756_b0075","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ins.2018.01.005","article-title":"Automatic design of hyper-heuristic based on reinforcement learning","volume":"436","author":"Choong","year":"2018","journal-title":"Inf. Sci."},{"key":"10.1016\/j.aei.2022.101756_b0080","unstructured":"E. Soubeiga, Development and application of hyperheuristics to personnel scheduling, University of Nottingham, 2003."},{"key":"10.1016\/j.aei.2022.101756_b0085","unstructured":"R. Bai, An investigation of novel approaches for optimising retail shelf space allocation, University of Nottingham, 2005."},{"key":"10.1016\/j.aei.2022.101756_b0090","article-title":"Exploring Hyper-heuristic Methodologies with Genetic Programming","volume":"vol. 1.","author":"Burke","year":"2009"},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0095","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TEVC.2015.2429314","article-title":"Automated Design of Production Scheduling Heuristics: A Review","volume":"20","author":"Branke","year":"2016","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0100","article-title":"A Classification of Hyper-Heuristic Approaches: Revisited","volume":"vol. 272","author":"Burke","year":"2019"},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0105","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40747-017-0036-x","article-title":"Genetic programming for production scheduling: A survey with a unified framework","volume":"3","author":"Nguyen","year":"2017","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.aei.2022.101756_b0110","article-title":"Review for Flexible Job Shop Scheduling","volume":"vol. 2","author":"Li","year":"2020"},{"key":"10.1016\/j.aei.2022.101756_b0115","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1049\/iet-cim.2018.0009","article-title":"Review on flexible job shop scheduling","volume":"1","author":"Xie","year":"2019","journal-title":"IET Collab. Intell. Manuf."},{"key":"10.1016\/j.aei.2022.101756_b0120","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."},{"issue":"5","key":"10.1016\/j.aei.2022.101756_b0125","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11740-018-0832-1","article-title":"Review and classification of hybrid shop scheduling","volume":"12","author":"Fan","year":"2018","journal-title":"Prod. Eng. Res. Devel."},{"issue":"8","key":"10.1016\/j.aei.2022.101756_b0130","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.cor.2009.11.001","article-title":"Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective","volume":"37","author":"Ribas","year":"2010","journal-title":"Comput. Oper. Res."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0135","first-page":"429","article-title":"Job shop scheduling: Classification, constraints and objective functions","volume":"11","author":"Abdolrazzagh-Nezhad","year":"2017","journal-title":"Int. J. Comput. Informat. Eng."},{"issue":"5","key":"10.1016\/j.aei.2022.101756_b0140","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1287\/opre.7.5.621","article-title":"The Schedule-Sequencing Problem","volume":"7","author":"Bowman","year":"1959","journal-title":"Oper. Res."},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0145","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1287\/opre.8.2.219","article-title":"On the Job-Shop Scheduling Problem","volume":"8","author":"Manne","year":"1960","journal-title":"Oper. Res."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0150","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/0360-8352(96)00047-2","article-title":"A tutorial survey of job-shop scheduling problems using genetic algorithms\u2014I. representation","volume":"30","author":"Cheng","year":"1996","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.aei.2022.101756_b0155","article-title":"Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming","volume":"vol. 10196","author":"Mei","year":"2017"},{"issue":"9","key":"10.1016\/j.aei.2022.101756_b0160","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1109\/TCYB.2016.2562674","article-title":"Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules","volume":"47","author":"Nguyen","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2022.101756_b0165","article-title":"Selection Schemes in Surrogate-Assisted Genetic Programming for Job Shop Scheduling","volume":"vol. 8886","author":"Nguyen","year":"2014"},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0170","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1162\/EVCO_a_00133","article-title":"J\u00fcrgen Branke; On Using Surrogates with Genetic Programming","volume":"23","author":"Hildebrandt","year":"2015","journal-title":"Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0175","doi-asserted-by":"crossref","unstructured":"T. Hildebrandt, J. Heger, B. Scholz-Reiter, Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach, in: Proceedings of the 12th annual conference on Genetic and evolutionary computation (GECCO '10). Association for Computing Machinery, New York, NY, USA, OI. 2010, pp. 257\u2013264. https:\/\/doi.org\/10.1145\/1830483.1830530.","DOI":"10.1145\/1830483.1830530"},{"key":"10.1016\/j.aei.2022.101756_b0180","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1111\/itor.12199","article-title":"A research survey: review of flexible job shop scheduling techniques","volume":"23","author":"Chaudhry","year":"2016","journal-title":"Intl. Trans. in Op. Res."},{"key":"10.1016\/j.aei.2022.101756_b0185","doi-asserted-by":"crossref","unstructured":"D. Yska, Y. Mei, M. Zhang, Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling, in: M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, P. Garc\u00eda-S\u00e1nchez. (Eds.), Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science, vol. 10781, 2018.","DOI":"10.1007\/978-3-319-77553-1_19"},{"key":"10.1016\/j.aei.2022.101756_b0190","first-page":"1537","article-title":"Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem","volume":"21","author":"Zheng","year":"2017","journal-title":"SoftComputing"},{"key":"10.1016\/j.aei.2022.101756_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112915","article-title":"A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem","volume":"140","author":"Lin","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2022.101756_b0200","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.swevo.2017.06.001","article-title":"A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem","volume":"38","author":"Wang","year":"2018","journal-title":"Swarm Evol. Comput."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0205","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/TEVC.2017.2785351","article-title":"Generalized Multitasking for Evolutionary Optimization of Expensive Problems","volume":"23","author":"Ding","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0210","article-title":"Evolutionary Multitask Optimisation for Dynamic Job Shop Scheduling Using Niched Genetic Programming","volume":"vol. 11320","author":"Park","year":"2018"},{"key":"10.1016\/j.aei.2022.101756_b0215","article-title":"Surrogate-Assisted Evolutionary Framework with Adaptive Knowledge Transfer for Multi-task Optimization","author":"Huang","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0220","doi-asserted-by":"crossref","unstructured":"Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang, A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming, in: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). Association for Computing Machinery, New York, NY, USA, 2020, pp. 107\u2013108. https:\/\/doi.org\/10.1145\/3377929.3389934.","DOI":"10.1145\/3377929.3389934"},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0225","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TEVC.2021.3065707","article-title":"Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0230","article-title":"Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling","volume":"12","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Cybernet."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0235","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ijpe.2012.04.015","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. Econ."},{"key":"10.1016\/j.aei.2022.101756_b0240","article-title":"Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling","volume":"vol. 10781","author":"Park","year":"2018"},{"key":"10.1016\/j.aei.2022.101756_b0245","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0377-2217(98)00029-0","article-title":"Exploiting process plan flexibility in production scheduling: a multi-objective approach","volume":"114","author":"Brandimarte","year":"1999","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.aei.2022.101756_b0250","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1016\/j.apm.2009.09.002","article-title":"Mathematical models for job-shop scheduling problems with routing and process plan flexibility","volume":"34","author":"\u00d6zg\u00fcven","year":"2010","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.aei.2022.101756_b0255","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10845-007-0026-8","article-title":"Mathematical modeling and heuristic approaches to flexible job shop scheduling problems","volume":"18","author":"Fattahi","year":"2007","journal-title":"J. Intell. Manuf."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0260","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1007\/s11590-013-0669-7","article-title":"A MILP model for an extended version of the flexible job shop problem","volume":"8","author":"Birgin","year":"2014","journal-title":"Optim. Lett."},{"issue":"1","key":"10.1016\/j.aei.2022.101756_b0265","first-page":"61","article-title":"Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints","volume":"6","author":"Shokouhi","year":"2018","journal-title":"Prod. Manuf. Res."},{"issue":"1\u20132","key":"10.1016\/j.aei.2022.101756_b0270","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0925-5273(95)00091-7","article-title":"Fuzzy job shop scheduling","volume":"44","author":"Kuroda","year":"1996","journal-title":"Int. J. Prod. Econ."},{"key":"10.1016\/j.aei.2022.101756_b0275","doi-asserted-by":"crossref","unstructured":"L.A. Zadeh, Fuzzy sets, Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, 1996, pp. 394-432.","DOI":"10.1142\/9789814261302_0021"},{"key":"10.1016\/j.aei.2022.101756_b0280","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.engappai.2018.10.008","article-title":"Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time","volume":"77","author":"Lin","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"12","key":"10.1016\/j.aei.2022.101756_b0285","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1109\/TFUZZ.2020.3003506","article-title":"Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism","volume":"28","author":"Gao","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0290","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ejor.2017.08.021","article-title":"Solving the flexible job shop scheduling problem with sequence-dependent setup times","volume":"265","author":"Shen","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.aei.2022.101756_b0295","doi-asserted-by":"crossref","first-page":"100807","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.aei.2022.101756_b0300","first-page":"1","article-title":"An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time","author":"Jiang","year":"2018","journal-title":"Int. J. Prod. Res."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0305","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TEM.2017.2785774","article-title":"Flexible Assembly Job-Shop Scheduling With Sequence-Dependent Setup Times and Part Sharing in a Dynamic Environment: Constraint Programming Model, Mixed-Integer Programming Model, and Dispatching Rules","volume":"65","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Eng. Manage."},{"issue":"5","key":"10.1016\/j.aei.2022.101756_b0310","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1287\/mnsc.39.5.616","article-title":"Minimizing the Makespan in the 3-Machine Assembly-Type Flowshop Scheduling Problem","volume":"39","author":"Lee","year":"1993","journal-title":"Manage. Sci."},{"key":"10.1016\/j.aei.2022.101756_b0315","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s12293-018-00278-7","article-title":"An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem","volume":"11","author":"Wu","year":"2019","journal-title":"Memetic Comp."},{"issue":"23","key":"10.1016\/j.aei.2022.101756_b0320","doi-asserted-by":"crossref","first-page":"7216","DOI":"10.1080\/00207543.2020.1836421","article-title":"Multi-objective optimisation for energy-aware flexible job-shop scheduling problem with assembly operations","volume":"59","author":"Ren","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2022.101756_b0325","first-page":"15","article-title":"A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem","volume":"13","author":"Yin","year":"2017","journal-title":"Sustainable Comput.: Informat. Syst."},{"key":"10.1016\/j.aei.2022.101756_b0330","article-title":"Genetic Programming-based Hyper-heuristic Approach for Solving Dynamic Job Shop Scheduling Problem with Extended Technical Precedence Constraints","volume":"105401","author":"Fan","year":"2021","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.aei.2022.101756_b0335","article-title":"Reinforcement learning applications to machine scheduling problems: a comprehensive literature review","author":"Kayhan","year":"2021","journal-title":"J. Intell. Manuf."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0340","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1287\/mnsc.13.3.167","article-title":"Heuristics in job shop scheduling","volume":"13","author":"Gere","year":"1966","journal-title":"Manage. Sci."},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0345","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/095372800232379","article-title":"Efficient jobshop dispatching rules: Further developments","volume":"11","author":"Holthaus","year":"2000","journal-title":"Prod. Planning Control"},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0350","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","article-title":"Tabu search\u2014part I","volume":"1","author":"Glover","year":"1989","journal-title":"ORSA J. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0355","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1063\/1.1699114","article-title":"Simulated annealing","volume":"21","author":"Metropolis","year":"1953","journal-title":"J. Chem. Phys."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0360","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant colony optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.aei.2022.101756_b0365","unstructured":"J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems), 1992."},{"key":"10.1016\/j.aei.2022.101756_b0370","unstructured":"H.-L. Fang, P. Ross, D. Corne, A promising hybrid GA\/heuristic approach for open-shop scheduling problems, in: Proceedings of the 11th European Conference on Artificial Intelligence (ECAI'94), John Wiley & Sons, Inc., USA, 1994, pp. 590\u2013594."},{"key":"10.1016\/j.aei.2022.101756_b0375","article-title":"Hyper-Heuristics: An Emerging Direction in Modern Search Technology","volume":"vol. 57","author":"Burke","year":"2003"},{"issue":"10","key":"10.1016\/j.aei.2022.101756_b0380","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1287\/mnsc.38.10.1495","article-title":"New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling","volume":"38","author":"Storer","year":"1992","journal-title":"Manage. Sci."},{"key":"10.1016\/j.aei.2022.101756_b0385","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.knosys.2012.04.001","article-title":"An efficient knowledge-based algorithm for the flexible job shop scheduling problem","volume":"36","author":"Karimi","year":"2012","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.aei.2022.101756_b0390","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0395","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","article-title":"An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints","volume":"18","author":"Deb","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0400","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TR.2010.2057310","article-title":"Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems","volume":"59","author":"Wang","year":"2010","journal-title":"IEEE Trans. Reliab."},{"key":"10.1016\/j.aei.2022.101756_b0405","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10710-017-9310-3","article-title":"Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment","volume":"19","author":"\u0189urasevi\u0107","year":"2018","journal-title":"Genet. Program Evolvable Mach."},{"issue":"12","key":"10.1016\/j.aei.2022.101756_b0410","first-page":"2494","article-title":"Automatic Discovery Method of Dynamic Job Shop Dispatching Rules Based on Hyper-Heuristic Genetic Programming","volume":"32","author":"Suyu","year":"2020","journal-title":"J. Syst. Simulat."},{"key":"10.1016\/j.aei.2022.101756_b0415","series-title":"2019 IEEE Congress on Evolutionary Computation (CEC)","first-page":"1366","article-title":"Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics","author":"Zhang","year":"2019"},{"key":"10.1016\/j.aei.2022.101756_b0420","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.procir.2019.02.118","article-title":"Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic","volume":"79","author":"Zhou","year":"2019","journal-title":"Proc. CIRP"},{"key":"10.1016\/j.aei.2022.101756_b0425","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106544","article-title":"An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem","volume":"95","author":"Li","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.aei.2022.101756_b0430","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/BF00175355","article-title":"Genetic programming as a means for programming computers by natural selection","volume":"4","author":"Koza","year":"1994","journal-title":"Stat Comput"},{"key":"10.1016\/j.aei.2022.101756_b0435","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/ACCESS.2018.2883802","article-title":"Hyper-heuristic coevolution of machine assignment and job sequencing rules for multi-objective dynamic flexible job shop scheduling","volume":"7","author":"Zhou","year":"2018","journal-title":"IEEE Access"},{"issue":"9","key":"10.1016\/j.aei.2022.101756_b0440","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1080\/00207543.2019.1620362","article-title":"Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming","volume":"58","author":"Zhou","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2022.101756_b0445","series-title":"Mexican International Conference on Artificial Intelligence","first-page":"284","article-title":"A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem","author":"Lara-C\u00e1rdenas","year":"2020"},{"issue":"5","key":"10.1016\/j.aei.2022.101756_b0450","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TETCI.2017.2743758","article-title":"An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming","volume":"1","author":"Mei","year":"2017","journal-title":"IEEE Trans. Emerging Top. Comput. Intell."},{"key":"10.1016\/j.aei.2022.101756_b0455","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","first-page":"149","article-title":"Feature construction in genetic programming hyper-heuristic for dynamic flexible job shop scheduling","author":"Yska","year":"2018"},{"key":"10.1016\/j.aei.2022.101756_b0460","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19)","first-page":"347","article-title":"A Two-stage Genetic programming Hyper-heuristic approach With Feature Selection for Dynamic Flexible Job Shop Scheduling","author":"Zhang","year":"2019"},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0465","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/4235.873237","article-title":"Two fast tree-creation algorithms for genetic programming","volume":"4","author":"Luke","year":"2000","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0470","series-title":"Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (GECCO'01)","first-page":"81","article-title":"A Survey and CompArison of Tree GenerAtion Algorithms","author":"Luke","year":"2001"},{"key":"10.1016\/j.aei.2022.101756_b0475","series-title":"2015 IEEE Congress on Evolutionary Computation (CEC)","first-page":"1145","article-title":"Transfer learning in genetic programming","author":"Huong Dinh","year":"2015"},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0480","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TCYB.2020.3024849","article-title":"Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling","volume":"51","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2022.101756_b0485","article-title":"A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling","volume":"vol. 11452","author":"Zhang","year":"2019"},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0490","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1162\/evco_a_00230","article-title":"A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules","volume":"27","author":"Nguyen","year":"2019","journal-title":"Evol. Comput."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0495","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/TEVC.2021.3056143","article-title":"Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.aei.2022.101756_b0500","article-title":"Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling","volume":"vol. 12102","author":"Zhang","year":"2020"},{"key":"10.1016\/j.aei.2022.101756_b0505","first-page":"2117","article-title":"Genetic Programming with Archive for Dynamic Flexible Job Shop Scheduling","author":"Xu","year":"2021"},{"key":"10.1016\/j.aei.2022.101756_b0510","series-title":"2019 IEEE Congress on Evolutionary Computation (CEC)","first-page":"41","article-title":"Can Stochastic Dispatching Rules Evolved by Genetic Programming Hyper-heuristics Help in Dynamic Flexible Job Shop Scheduling?","author":"Zhang","year":"2019"},{"key":"10.1016\/j.aei.2022.101756_b0515","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1002\/(SICI)1099-1425(200005\/06)3:3<125::AID-JOS40>3.0.CO;2-C","article-title":"A large step random walk for minimizing total weighted tardiness in a job shop","volume":"3","author":"Kreipl","year":"2000","journal-title":"J. Sched."},{"issue":"4","key":"10.1016\/j.aei.2022.101756_b0520","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MCI.2018.2866731","article-title":"Visualizing the Evolution of Computer Programs for Genetic Programming [Research Frontier]","volume":"13","author":"Nguyen","year":"2018","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"3","key":"10.1016\/j.aei.2022.101756_b0525","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/TCYB.2019.2936001","article-title":"People-Centric Evolutionary System for Dynamic Production Scheduling","volume":"51","author":"Nguyen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2022.101756_b0530","article-title":"Surrogate-Assisted Genetic Programming for Dynamic Flexible Job Shop Scheduling","volume":"vol. 11320","author":"Zhang","year":"2018","journal-title":"Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science"},{"key":"10.1016\/j.aei.2022.101756_b0535","doi-asserted-by":"crossref","unstructured":"F. Zhang, Y. Mei, S. Nguyen, et al., Guided subtree selection for genetic operators in genetic programming for dynamic flexible job shop scheduling. 2020.","DOI":"10.26686\/wgtn.13158314.v1"},{"key":"10.1016\/j.aei.2022.101756_b0540","article-title":"Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment","author":"Ameer","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2022.101756_b0545","unstructured":"People + AI Research, Google AI, London, U.K., Sep. 2017. [Online]. Available: https:\/\/ai.google\/research\/teams\/brain\/pair."},{"key":"10.1016\/j.aei.2022.101756_b0550","unstructured":"F. Doshi-Velez, B. Kim, A roadmap for a rigorous science of interpretability. arXiv preprint arXiv:1702.08608, 2017, 2: 1."},{"issue":"1\u20133","key":"10.1016\/j.aei.2022.101756_b0555","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.tcs.2006.07.011","article-title":"Multi-agent scheduling on a single machine to minimize total weighted number of tardy jobs","volume":"362","author":"Cheng","year":"2006","journal-title":"Theoret. Comput. Sci."},{"key":"10.1016\/j.aei.2022.101756_b0560","doi-asserted-by":"crossref","first-page":"107","DOI":"10.3390\/technologies6040107","article-title":"Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era","volume":"6","author":"Leusin","year":"2018","journal-title":"Technologies"},{"key":"10.1016\/j.aei.2022.101756_b0565","unstructured":"J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A.H. Byers, \u201cBig data: The next frontier for innovation, competition, and productivity\u201d, McKinsey Global Institute, 2011, http:\/\/www.mckinsey.com\/\u223c\/media\/McKinsey\/dotcom\/Insights%20and%20pubs\/MGI\/Research\/Technology%20and%20Innovation\/Big%20Data\/MGI_big_data_full_report.ashx."},{"key":"10.1016\/j.aei.2022.101756_b0570","series-title":"2013 International Conference on Collaboration Technologies and Systems (CTS)","first-page":"42","article-title":"Big data: A review","author":"Sagiroglu","year":"2013"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034622002142?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034622002142?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T10:03:39Z","timestamp":1761473019000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034622002142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":114,"alternative-id":["S1474034622002142"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2022.101756","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2022.101756","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101756"}}