{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:02:29Z","timestamp":1781298149852,"version":"3.54.1"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Hubei Province Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Operations Research"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.cor.2026.107526","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:22:34Z","timestamp":1778811754000},"page":"107526","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Robust scheduling policy learning for ore-flow blending in dynamic and uncertain environments"],"prefix":"10.1016","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0312-6483","authenticated-orcid":false,"given":"Hao","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9222-0702","authenticated-orcid":false,"given":"Changhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoufei","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5281-4175","authenticated-orcid":false,"given":"Michalis","family":"Mavrovouniotis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cor.2026.107526_b1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10489-005-2370-8","article-title":"Dynamic ant colony optimisation","volume":"23","author":"Angus","year":"2005","journal-title":"Appl. Intell."},{"key":"10.1016\/j.cor.2026.107526_b2","doi-asserted-by":"crossref","unstructured":"Ao, M., Li, C., Yang, S., 2023. Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model. In: 2023 42nd Chinese Control Conference. CCC, pp. 8651\u20138656.","DOI":"10.23919\/CCC58697.2023.10240705"},{"key":"10.1016\/j.cor.2026.107526_b3","series-title":"Neural combinatorial optimization with reinforcement learning","author":"Bello","year":"2016"},{"issue":"6791","key":"10.1016\/j.cor.2026.107526_b4","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/35017500","article-title":"Inspiration for optimization from social insect behaviour","volume":"406","author":"Bonabeau","year":"2000","journal-title":"Nature"},{"issue":"6","key":"10.1016\/j.cor.2026.107526_b5","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3390\/min11060587","article-title":"Integrating production planning with truck-dispatching decisions through reinforcement learning while managing uncertainty","volume":"11","author":"de Carvalho","year":"2021","journal-title":"Minerals"},{"issue":"17","key":"10.1016\/j.cor.2026.107526_b6","doi-asserted-by":"crossref","first-page":"10766","DOI":"10.3390\/su141710766","article-title":"Comprehensive utilization of mineral resources: Optimal blending of polymetallic ore using an improved NSGA-III algorithm","volume":"14","author":"Chen","year":"2022","journal-title":"Sustainability"},{"issue":"9","key":"10.1016\/j.cor.2026.107526_b7","doi-asserted-by":"crossref","first-page":"4523","DOI":"10.1007\/s00521-023-09396-x","article-title":"Improving productivity in mining operations: a deep reinforcement learning model for effective material supply and equipment management","volume":"36","author":"Chiarot Villegas","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.cor.2026.107526_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2022.107931","article-title":"A stochastic optimization framework for integrated scheduling and control under demand uncertainty","volume":"165","author":"Dering","year":"2022","journal-title":"Comput. Chem. Eng."},{"issue":"43","key":"10.1016\/j.cor.2026.107526_b9","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi","year":"2023","journal-title":"J. Mach. Learn. Res."},{"issue":"7553","key":"10.1016\/j.cor.2026.107526_b10","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1038\/nature14544","article-title":"From evolutionary computation to the evolution of things","volume":"521","author":"Eiben","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.cor.2026.107526_b11","series-title":"International Workshop on Ant Algorithms","first-page":"88","article-title":"Ant systems for a dynamic TSP: Ants caught in a traffic jam","author":"Eyckelhof","year":"2002"},{"key":"10.1016\/j.cor.2026.107526_b12","first-page":"149","article-title":"Ant colony optimization with clustering for solving the dynamic location routing problem","volume":"285","author":"Gao","year":"2016","journal-title":"Appl. Math. Comput."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b13","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ejor.2021.07.043","article-title":"A rolling-horizon approach for multi-period optimization","volume":"300","author":"Glomb","year":"2022","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.cor.2026.107526_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108058","article-title":"An efficient ant colony optimization framework for HPC environments","volume":"114","author":"Gonz\u00e1lez","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.cor.2026.107526_b15","series-title":"International Workshop on Ant Algorithms","first-page":"111","article-title":"Applying population based ACO to dynamic optimization problems","author":"Guntsch","year":"2002"},{"key":"10.1016\/j.cor.2026.107526_b16","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cie.2015.11.005","article-title":"Event driven strategy based complete rescheduling approaches for dynamic m identical parallel machines scheduling problem with a common server","volume":"91","author":"Hamzadayi","year":"2016","journal-title":"Comput. Ind. Eng."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1177\/25726668231222998","article-title":"Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems","volume":"133","author":"Hazrathosseini","year":"2024","journal-title":"Min. Technol."},{"issue":"3","key":"10.1016\/j.cor.2026.107526_b18","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10100-023-00891-z","article-title":"A rolling horizon planning approach for short-term demand supply matching","volume":"32","author":"Herding","year":"2024","journal-title":"Cent. Eur. J. Oper. Res."},{"issue":"20","key":"10.1016\/j.cor.2026.107526_b19","doi-asserted-by":"crossref","first-page":"3176","DOI":"10.3390\/math12203176","article-title":"Multi-objective production rescheduling: A systematic literature review","volume":"12","author":"Holguin Jimenez","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.cor.2026.107526_b20","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., 2022. LoRA: Low-Rank Adaptation of Large Language Models. In: International Conference on Learning Representations."},{"key":"10.1016\/j.cor.2026.107526_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.resconrec.2022.106664","article-title":"Reinforcement learning-based fleet dispatching for greenhouse gas emission reduction in open-pit mining operations","volume":"188","author":"Huo","year":"2023","journal-title":"Resour. Conserv. Recycl."},{"key":"10.1016\/j.cor.2026.107526_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.140459","article-title":"Smart dispatching for low-carbon mining fleet: A deep reinforcement learning approach","volume":"435","author":"Huo","year":"2024","journal-title":"J. Clean. Prod."},{"issue":"9","key":"10.1016\/j.cor.2026.107526_b23","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1109\/TSMC.2017.2682264","article-title":"A dynamic logistic dispatching system with set-based particle swarm optimization","volume":"48","author":"Jia","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern.: Syst."},{"key":"10.1016\/j.cor.2026.107526_b24","unstructured":"Kool, W., van Hoof, H., Welling, M., 2019. Attention, Learn to Solve Routing Problems!. In: International Conference on Learning Representations."},{"key":"10.1016\/j.cor.2026.107526_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.cor.2023.106453","article-title":"\u03b3-Robust optimization of project scheduling problems","volume":"161","author":"Koster","year":"2024","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.cor.2026.107526_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.123025","article-title":"Scheduling optimization of underground mine trackless transportation based on improved estimation of distribution algorithm","volume":"245","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.cor.2026.107526_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121254","article-title":"A fast density peak clustering based particle swarm optimizer for dynamic optimization","volume":"236","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.cor.2026.107526_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.105711","article-title":"An affinity propagation clustering based particle swarm optimizer for dynamic optimization","volume":"195","author":"Liu","year":"2020","journal-title":"Knowl.-Based Syst."},{"issue":"11","key":"10.1016\/j.cor.2026.107526_b29","doi-asserted-by":"crossref","first-page":"5092","DOI":"10.3390\/app11115092","article-title":"A method of ore blending based on the quality of beneficiation and its application in a concentrator","volume":"11","author":"Liu","year":"2021","journal-title":"Appl. Sci."},{"issue":"6","key":"10.1016\/j.cor.2026.107526_b30","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1111\/poms.13239","article-title":"A review of robust operations management under model uncertainty","volume":"30","author":"Lu","year":"2021","journal-title":"Prod. Oper. Manage."},{"key":"10.1016\/j.cor.2026.107526_b31","first-page":"8845","article-title":"Neural combinatorial optimization with heavy decoder: Toward large scale generalization","volume":"36","author":"Luo","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cor.2026.107526_b32","series-title":"Self-improved learning for scalable neural combinatorial optimization","author":"Luo","year":"2024"},{"key":"10.1016\/j.cor.2026.107526_b33","series-title":"2023 IEEE 26th International Conference on Intelligent Transportation Systems","first-page":"5468","article-title":"Real-time dispatching for autonomous vehicles in open-pit mining deployments using deep reinforcement learning","author":"Matsui","year":"2023"},{"key":"10.1016\/j.cor.2026.107526_b34","series-title":"2019 IEEE Congress on Evolutionary Computation","first-page":"2567","article-title":"Effective ACO-based memetic algorithms for symmetric and asymmetric dynamic changes","author":"Mavrovouniotis","year":"2019"},{"key":"10.1016\/j.cor.2026.107526_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.12.005","article-title":"A survey of swarm intelligence for dynamic optimization: Algorithms and applications","volume":"33","author":"Mavrovouniotis","year":"2017","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.cor.2026.107526_b36","series-title":"2024 IEEE Congress on Evolutionary Computation","first-page":"01","article-title":"Exchange strategies for multi-colony ant algorithms in dynamic environments","author":"Mavrovouniotis","year":"2024"},{"issue":"7","key":"10.1016\/j.cor.2026.107526_b37","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TCYB.2016.2556742","article-title":"Ant colony optimization with local search for dynamic traveling salesman problems","volume":"47","author":"Mavrovouniotis","year":"2016","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"10.1016\/j.cor.2026.107526_b38","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TCYB.2016.2556742","article-title":"Ant colony optimization with local search for dynamic traveling salesman problems","volume":"47","author":"Mavrovouniotis","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.cor.2026.107526_b39","series-title":"2012 IEEE Congress on Evolutionary Computation","first-page":"1","article-title":"Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem","author":"Mavrovouniotis","year":"2012"},{"issue":"10","key":"10.1016\/j.cor.2026.107526_b40","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1016\/j.asoc.2013.05.022","article-title":"Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors","volume":"13","author":"Mavrovouniotis","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.cor.2026.107526_b41","series-title":"2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments","first-page":"47","article-title":"Ant colony optimization with self-adaptive evaporation rate in dynamic environments","author":"Mavrovouniotis","year":"2014"},{"key":"10.1016\/j.cor.2026.107526_b42","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.ins.2014.10.002","article-title":"Ant algorithms with immigrants schemes for the dynamic vehicle routing problem","volume":"294","author":"Mavrovouniotis","year":"2015","journal-title":"Inform. Sci."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b43","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCI.2019.2954644","article-title":"Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [research frontier]","volume":"15","author":"Mavrovouniotis","year":"2020","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b44","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s10107-017-1172-1","article-title":"Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations","volume":"171","author":"Mohajerin Esfahani","year":"2018","journal-title":"Math. Program."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b45","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.ejor.2019.01.008","article-title":"A multiple objective transportation problem approach to dynamic truck dispatching in surface mines","volume":"276","author":"Moradi Afrapoli","year":"2019","journal-title":"European J. Oper. Res."},{"issue":"9","key":"10.1016\/j.cor.2026.107526_b46","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1080\/17480930.2023.2242163","article-title":"A multiple objective hybrid algorithm for daily ore blend in oil sands mines","volume":"37","author":"Nikbin","year":"2023","journal-title":"Int. J. Min. Reclam. Environ."},{"key":"10.1016\/j.cor.2026.107526_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.cor.2024.106815","article-title":"Deep Reinforcement Learning based real-time open-pit mining truck dispatching system","volume":"173","author":"Noriega","year":"2025","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.cor.2026.107526_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.cor.2021.105359","article-title":"A computational study on ant colony optimization for the traveling salesman problem with dynamic demands","volume":"135","author":"de Oliveira","year":"2021","journal-title":"Comput. Oper. Res."},{"issue":"2","key":"10.1016\/j.cor.2026.107526_b49","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/j.ejor.2017.03.081","article-title":"Energy efficient scheduling of open-pit coal mine trucks","volume":"262","author":"Patterson","year":"2017","journal-title":"European J. Oper. Res."},{"issue":"1","key":"10.1016\/j.cor.2026.107526_b50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2012.08.015","article-title":"A review of dynamic vehicle routing problems","volume":"225","author":"Pillac","year":"2013","journal-title":"European J. Oper. Res."},{"issue":"Suppl 1","key":"10.1016\/j.cor.2026.107526_b51","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00521-018-3638-3","article-title":"Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems","volume":"31","author":"Prakasam","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.cor.2026.107526_b52","series-title":"Optimization Algorithms-Classics and Recent Advances","article-title":"An overview of ant colony optimization algorithms for dynamic optimization problems","author":"Rezvanian","year":"2023"},{"key":"10.1016\/j.cor.2026.107526_b53","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.trb.2016.01.005","article-title":"Ant colony optimization for the real-time train routing selection problem","volume":"85","author":"Sam\u00e0","year":"2016","journal-title":"Transp. Res. B"},{"key":"10.1016\/j.cor.2026.107526_b54","series-title":"Enhancing rolling horizon production planning through stochastic optimization evaluated by means of simulation","author":"Schlenkrich","year":"2024"},{"issue":"10","key":"10.1016\/j.cor.2026.107526_b55","doi-asserted-by":"crossref","first-page":"17491","DOI":"10.1109\/TITS.2022.3150471","article-title":"Memory-based ant colony system approach for multi-source data associated dynamic electric vehicle dispatch optimization","volume":"23","author":"Shi","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.cor.2026.107526_b56","series-title":"HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization Algorithm","author":"Skackauskas","year":"2022"},{"issue":"5","key":"10.1016\/j.cor.2026.107526_b57","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1007\/s40747-021-00433-7","article-title":"A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement","volume":"7","author":"Su","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.cor.2026.107526_b58","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":"\u00d0urasevi\u0107","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.cor.2026.107526_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2024.110122","article-title":"An adaptive large neighborhood search for the multi-depot dynamic vehicle routing problem with time windows","volume":"191","author":"Wang","year":"2024","journal-title":"Comput. Ind. Eng."},{"issue":"3","key":"10.1016\/j.cor.2026.107526_b60","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1023\/A:1022672621406","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach. Learn."},{"issue":"9","key":"10.1016\/j.cor.2026.107526_b61","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.1109\/TNNLS.2021.3068828","article-title":"Learning improvement heuristics for solving routing problems","volume":"33","author":"Wu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"10.1016\/j.cor.2026.107526_b62","doi-asserted-by":"crossref","first-page":"5275","DOI":"10.1109\/TITS.2021.3052834","article-title":"A pairwise proximity learning-based ant colony algorithm for dynamic vehicle routing problems","volume":"23","author":"Xiang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.cor.2026.107526_b63","doi-asserted-by":"crossref","first-page":"3399","DOI":"10.3390\/su15043399","article-title":"Open-pit mine truck dispatching system based on dynamic ore blending decisions","volume":"15","author":"Yao","year":"2023","journal-title":"Sustainability"},{"issue":"7","key":"10.1016\/j.cor.2026.107526_b64","doi-asserted-by":"crossref","first-page":"6476","DOI":"10.1109\/TCYB.2020.3036100","article-title":"Adaptive control of subpopulations in evolutionary dynamic optimization","volume":"52","author":"Yazdani","year":"2020","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"10.1016\/j.cor.2026.107526_b65","doi-asserted-by":"crossref","first-page":"6476","DOI":"10.1109\/TCYB.2020.3036100","article-title":"Adaptive control of subpopulations in evolutionary dynamic optimization","volume":"52","author":"Yazdani","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"10.1016\/j.cor.2026.107526_b66","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TEVC.2021.3060014","article-title":"A survey of evolutionary continuous dynamic optimization over two decades\u2014Part A","volume":"25","author":"Yazdani","year":"2021","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.cor.2026.107526_b67","doi-asserted-by":"crossref","DOI":"10.1109\/TEVC.2022.3180590","article-title":"Robust optimization over time: a critical review","author":"Yazdani","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"14","key":"10.1016\/j.cor.2026.107526_b68","doi-asserted-by":"crossref","first-page":"4836","DOI":"10.3390\/s21144836","article-title":"Data-driven dispatching rules mining and real-time decision-making methodology in intelligent manufacturing shop floor with uncertainty","volume":"21","author":"Zhang","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.cor.2026.107526_b69","series-title":"2020 IEEE International Conference on Big Data","first-page":"1436","article-title":"Dynamic dispatching for large-scale heterogeneous fleet via multi-agent deep reinforcement learning","author":"Zhang","year":"2020"},{"issue":"5","key":"10.1016\/j.cor.2026.107526_b70","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/TEVC.2022.3179256","article-title":"Surrogate-assisted evolutionary Q-learning for black-box dynamic time-linkage optimization problems","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"11","key":"10.1016\/j.cor.2026.107526_b71","doi-asserted-by":"crossref","first-page":"2903","DOI":"10.1080\/00207540701644219","article-title":"Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems","volume":"47","author":"Zhou","year":"2009","journal-title":"Int. J. Prod. Res."}],"container-title":["Computers & Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305054826001449?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305054826001449?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T20:42:21Z","timestamp":1781296941000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0305054826001449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":71,"alternative-id":["S0305054826001449"],"URL":"https:\/\/doi.org\/10.1016\/j.cor.2026.107526","relation":{},"ISSN":["0305-0548"],"issn-type":[{"value":"0305-0548","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Robust scheduling policy learning for ore-flow blending in dynamic and uncertain environments","name":"articletitle","label":"Article Title"},{"value":"Computers & Operations Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cor.2026.107526","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":"107526"}}