{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:40:38Z","timestamp":1775760038220,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["FCOMP-01-0124-FEDER- 775 PEst-OE\/EEI\/UI0760\/2014"],"award-info":[{"award-number":["FCOMP-01-0124-FEDER- 775 PEst-OE\/EEI\/UI0760\/2014"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>In real manufacturing environments, scheduling can be defined as the problem of effectively and efficiently assigning tasks to specific resources. Metaheuristics are often used to obtain near-optimal solutions in an efficient way. The parameter tuning of metaheuristics allows flexibility and leads to robust results, but requires careful specifications. The a priori definition of parameter values is complex, depending on the problem instances and resources. This paper implements a novel approach to the automatic specification of metaheuristic parameters, for solving the scheduling problem. This novel approach incorporates two learning techniques, namely, racing and case-based reasoning (CBR), to provide the system with the ability to learn from previous cases. In order to evaluate the contributions of the proposed approach, a computational study was performed, focusing on comparing our results previous published results. All results were validated by analyzing the statistical significance, allowing us to conclude the statistically significant advantage of the use of the novel proposed approach.<\/jats:p>","DOI":"10.3390\/app11083325","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T11:31:59Z","timestamp":1617795119000},"page":"3325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Hybrid Metaheuristics Parameter Tuning Approach for Scheduling through Racing and Case-Based Reasoning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-3225","authenticated-orcid":false,"given":"Ivo","family":"Pereira","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Fernando Pessoa University, 4249-004 Porto, Portugal"},{"name":"ISRC\u2014Interdisciplinary Studies Research Center, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0264-4710","authenticated-orcid":false,"given":"Ana","family":"Madureira","sequence":"additional","affiliation":[{"name":"ISRC\u2014Interdisciplinary Studies Research Center, 4200-072 Porto, Portugal"},{"name":"INOV\u2014Instituto de Engenharia de Sistemas e Computadores Inova\u00e7\u00e3o, 1000-029 Lisboa, Portugal"},{"name":"Institute of Engineering-Polytechnique of Porto (ISEP\/IPP), 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9757-6687","authenticated-orcid":false,"given":"Eliana","family":"Costa e Silva","sequence":"additional","affiliation":[{"name":"CIICESI-ESTG-Polytechnic of Porto, 4610-156 Felgueiras, Portugal"},{"name":"Centro ALGORITMI, School of Engineering, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA 98071-2259, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105094","DOI":"10.1016\/j.knosys.2019.105094","article-title":"Parameter tuning for meta-heuristics","volume":"189","author":"Joshi","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_2","first-page":"201","article-title":"A statistical learning based approach for parameter fine-tuning of metaheuristics","volume":"1","author":"Calvet","year":"2016","journal-title":"Stat. Oper. Res. Trans."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TEVC.2019.2921598","article-title":"A survey of automatic parameter tuning methods for metaheuristics","volume":"24","author":"Huang","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Birattari, M. (2009). Tuning Metaheuristics: A Machine Learning Perspective, Springer.","DOI":"10.1007\/978-3-642-00483-4"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cotta, C., Sevaux, M., and S\u00f6rensen, K. (2008). Adaptive and Multilevel Metaheuristics, Springer.","DOI":"10.1007\/978-3-540-79438-7"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Madureira, A., Santos, F., and Pereira, I. (2008, January 12\u201316). Self-Managing Agents for Dynamic Scheduling in Manufacturing. Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), Atlanta, GA, USA.","DOI":"10.1145\/1388969.1389045"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pereira, I., and Madureira, A. (2010, January 18\u201323). Self-optimizing through CBR learning. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586081"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Madureira, A., and Pereira, I. (2010). Self-Optimization for Dynamic Scheduling in Manufacturing Systems. Technological Developments in Networking, Education and Automation, Springer.","DOI":"10.1007\/978-90-481-9151-2_74"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.asoc.2012.02.009","article-title":"Self-optimization module for scheduling using case-based reasoning","volume":"13","author":"Pereira","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pereira, I., and Madureira, A. (2016). Self-Optimizing A Multi-Agent Scheduling System: A Racing Based Approach. Intelligent Distributed Computing IX, Springer.","DOI":"10.1007\/978-3-319-25017-5_26"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pinedo, M.L. (2012). Scheduling: Theory, Algorithms, and Systems, Springer.","DOI":"10.1007\/978-1-4614-2361-4"},{"key":"ref_12","unstructured":"Madureira, A. (2003). Meta-Heuristics Application to Scheduling in Dynamic Environments of Discrete Manufacturing. [Ph.D. Thesis, University of Minho]. (In Portuguese)."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neucom.2013.10.032","article-title":"Negotiation Mechanism for Self-organized Scheduling System with Collective Intelligence","volume":"132","author":"Madureira","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gonzalez, T.F. (2007). Handbook of Approximation Algorithms and Metaheuristics, CRC Press.","DOI":"10.1201\/9781420010749"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Talbi, E.G. (2009). Metaheuristics: From Design to Implementation, John Wiley & Sons.","DOI":"10.1002\/9780470496916"},{"key":"ref_16","unstructured":"Pereira, I. (2014). Intelligent System for Scheduling Assisted by Learning. [Ph.D. Thesis, UTAD]. (In Portuguese)."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Glover, F., and Kochenberger, G.A. (2003). Handbook of Metaheuristics, Springer Science & Business Media.","DOI":"10.1007\/b101874"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compind.2018.03.001","article-title":"A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms","volume":"98","author":"Sabzi","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.compind.2016.04.007","article-title":"A rail-road PI-hub allocation problem: Active and reactive approaches","volume":"81","author":"Walha","year":"2016","journal-title":"Comput. Ind."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hamadi, Y., Hoos, H.H., and Leyton-Brown, K. (2006). Performance prediction and automated tuning of randomized and parametric algorithms. Principles and Practice of Constraint Programming, Springer.","DOI":"10.1007\/11889205_17"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Smith, J.E. (2008). Self-adaptation in evolutionary algorithms for combinatorial optimisation. Adaptive and Multilevel Metaheuristics, Springer.","DOI":"10.1007\/978-3-540-79438-7_2"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Smit, S.K., and Eiben, A.E. (2009, January 18\u201321). Comparing parameter tuning methods for evolutionary algorithms. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Trondheim, Norway.","DOI":"10.1109\/CEC.2009.4982974"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hamadi, Y., Monfroy, E., and Saubion, F. (2012). Automated Algorithm Configuration and Parameter Tuning. Autonomous Search, Springer.","DOI":"10.1007\/978-3-642-21434-9"},{"key":"ref_24","unstructured":"Bartz-Beielstein, T., Parsopoulos, K.E., and Vrahatis, M.N. (2004, January 10\u201314). Analysis of particle swarm optimization using computational statistics. Proceedings of the International Conference of Numerical Analysis and Applied Mathematics, Chalkis, Greece."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1287\/opre.1050.0243","article-title":"Fine-tuning of algorithms using fractional experimental designs and local search","volume":"54","author":"Laguna","year":"2006","journal-title":"Oper. Res."},{"key":"ref_27","unstructured":"Birattari, M., St\u00fctzle, T., Paquete, L., and Varrentrapp, K. (2002, January 9\u201313). A Racing Algorithm for Configuring Metaheuristics. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), New York, NY, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Akay, B., and Karaboga, D. (2009, January 5\u20137). Parameter tuning for the artificial bee colony algorithm. Proceedings of the International Conference on Computational Collective Intelligence, Wroc\u0142aw, Poland.","DOI":"10.1007\/978-3-642-04441-0_53"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1002\/tee.20078","article-title":"Dynamic parameter tuning of particle swarm optimization","volume":"1","author":"Iwasaki","year":"2006","journal-title":"IEEE Trans. Electr. Electron. Eng."},{"key":"ref_30","unstructured":"Bartz-Beielstein, T., and Markon, S. (2004, January 19\u201323). Tuning search algorithms for real-world applications: A regression tree based approach. Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Amoozegar, M., and Rashedi, E. (2014, January 29\u201330). Parameter tuning of GSA using DOE. Proceedings of the 4th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE.2014.6993390"},{"key":"ref_32","unstructured":"Vafadarnikjoo, A., Firouzabadi, S., Mobin, M., and Roshani, A. (2015, January 4\u20139). A meta-heuristic approach to locate optimal switch locations in cellular mobile networks. Proceedings of the 2015 American Society of Engineering Management Conference (ASEM2015), Vienna, Austria."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.measurement.2016.09.007","article-title":"An artificial immune algorithm for ergonomic product classification using anthropometric measurements","volume":"94","author":"Tavana","year":"2016","journal-title":"Measurement"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cie.2016.03.024","article-title":"Minimizing tardiness and maintenance costs in flow shop scheduling by a lower-bound-based GA","volume":"97","author":"Yu","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.ins.2016.08.066","article-title":"Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms","volume":"372","author":"Mernik","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s40314-015-0218-3","article-title":"An intelligent water drop algorithm to identical parallel machine scheduling with controllable processing times: A just-in-time approach","volume":"36","author":"Kayvanfar","year":"2017","journal-title":"Comput. Appl. Math."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.measurement.2017.10.009","article-title":"A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms","volume":"114","author":"Mobin","year":"2018","journal-title":"Measurement"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Eiben, A., and Smit, S. (2012). Evolutionary algorithm parameters and methods to tune them. Autonomous Search, Springer.","DOI":"10.1007\/978-3-642-21434-9_2"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hamadi, Y., Monfroy, E., and Saubion, F. (2012). An introduction to autonomous search. Autonomous Search, Springer.","DOI":"10.1007\/978-3-642-21434-9"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"012063","DOI":"10.1088\/1742-6596\/973\/1\/012063","article-title":"Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem","volume":"973","author":"Skakov","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TEVC.2014.2308294","article-title":"Parameter control in evolutionary algorithms: Trends and challenges","volume":"19","author":"Karafotias","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Eiben, A.E., and Smith, J.E. (2015). Introduction to Evolutionary Computing, Springer.","DOI":"10.1007\/978-3-662-44874-8"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bartz-Beielstein, T. (2006). Experimental Research in Evolutionary Computation, Springer.","DOI":"10.1145\/1274000.1274102"},{"key":"ref_44","unstructured":"Box, G.E., Hunter, W.G., and Hunter, J.S. (2005). Statistics for Experimenters: Design, Innovation, and Discovery, John Wiley & Sons."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1026569813391","article-title":"Using experimental design to find effective parameter settings for heuristics","volume":"7","author":"Coy","year":"2001","journal-title":"J. Heuristics"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Johnson, D.S. (2002). A theoretician\u2019s guide to the experimental analysis of algorithms. Data Structures, near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, American Mathematical Society.","DOI":"10.1090\/dimacs\/059\/11"},{"key":"ref_47","unstructured":"Schaffer, J.D., Caruana, R.A., Eshelman, L.J., and Das, R. (1989, January 4\u20137). A study of control parameters affecting online performance of genetic algorithms for function optimization. Proceedings of the International Conference on Genetic Algorithms, Fairfax, VA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yuan, B., and Gallagher, M. (2007). Combining Meta-EAs and racing for difficult EA parameter tuning tasks. Parameter Setting in Evolutionary Algorithms, Springer.","DOI":"10.1007\/978-3-540-69432-8_6"},{"key":"ref_49","unstructured":"Dobslaw, F. (2010, January 2). A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. Proceedings of the International Conference on Computer Mathematics and Natural Computing, Rome, Italy."},{"key":"ref_50","first-page":"1","article-title":"Cross-disciplinary perspectives on meta-learning for algorithm selection","volume":"41","year":"2008","journal-title":"ACM Comput. Surv."},{"key":"ref_51","unstructured":"Stoean, R., Bartz-Beielstein, T., Preuss, M., and Stoean, C. (2021, February 07). A Support Vector Machine-Inspired Evolutionary Approach for Parameter Tuning in Metaheuristics, Available online: https:\/\/www.semanticscholar.org\/paper\/A-Support-Vector-Machine-Inspired-Evolutionary-for-Stoean-Bartz-Beielstein\/e84aa2111ab61e3b000691368fedbfc19b5e01e1."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.3923\/jas.2010.1991.2000","article-title":"A New Machine Learning based Approach for Tuning Metaheuristics for the Solution of Hard Combinatorial Optimization Problems","volume":"10","author":"Zennaki","year":"2010","journal-title":"J. Appl. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12826","DOI":"10.1016\/j.eswa.2011.04.075","article-title":"Tuning metaheuristics: A data mining based approach for particle swarm optimization","volume":"38","author":"Lessmann","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_54","unstructured":"Maron, O., and Moore, A.W. (1993). Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. Advances in Neural Information Processing Systems, Morgan-Kaufmann."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/01621459.1963.10500830","article-title":"Probability inequalities for sums of bounded random variables","volume":"58","author":"Hoeffding","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_56","unstructured":"Lee, M.S., and Moore, A. (1994, January 10\u201313). Efficient algorithms for minimizing cross validation error. Proceedings of the Machine Learning, Eighth International Conference, New Brunswick, NJ, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dean, A., and Voss, D. (1999). Design and Analysis of Experiments, Springer.","DOI":"10.1007\/b97673"},{"key":"ref_58","unstructured":"Montgomery, D.C. (2008). Design and Analysis of Experiments, John Wiley & Sons."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sheskin, D.J. (2003). Handbook of Parametric and Nonparametric Statistical Procedures, Chapman and Hall\/CRC.","DOI":"10.1201\/9781420036268"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3233\/AIC-1994-7104","article-title":"Case-based reasoning: Foundational issues, methodological variations, and system approaches","volume":"7","author":"Aamodt","year":"1994","journal-title":"AI Commun."},{"key":"ref_61","unstructured":"Kolodner, J. (2014). Case-Based Reasoning, Morgan-Kaufmann."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compind.2015.10.007","article-title":"Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore","volume":"78","author":"Chang","year":"2016","journal-title":"Comput. Ind."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compind.2018.06.001","article-title":"Experience capitalization to support decision making in inventive problem solving","volume":"101","author":"Zhang","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.asoc.2018.11.017","article-title":"Application of case-based reasoning in a fault detection system on production of drippers","volume":"75","author":"Khosravani","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10951-008-0082-8","article-title":"A hybrid metaheuristic case-based reasoning system for nurse rostering","volume":"12","author":"Beddoe","year":"2009","journal-title":"J. Sched."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Burke, E.K., MacCarthy, B.L., Petrovic, S., and Qu, R. (2003). Knowledge discovery in a hyper-heuristic for course timetabling using case-based reasoning. Practice and Theory of Automated Timetabling IV, Springer.","DOI":"10.1007\/978-3-540-45157-0_18"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.eswa.2006.06.017","article-title":"Case-based selection of initialisation heuristics for metaheuristic examination timetabling","volume":"33","author":"Petrovic","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ejor.2004.12.028","article-title":"Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering","volume":"175","author":"Beddoe","year":"2006","journal-title":"Eur. J. Oper. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Madureira, A., Pereira, I., and Sousa, N. (2011, January 1\u20132). Self-organization for scheduling in agile manufacturing. Proceedings of the 10th International Conference on Cybernetic Intelligent Systems, London, UK.","DOI":"10.1109\/CIS.2011.6169132"},{"key":"ref_70","unstructured":"Madureira, A., Pereira, I., and Falc\u00e3o, D. (2013, January 26\u201329). Dynamic Adaptation for Scheduling Under Rush Manufacturing Orders With Case-Based Reasoning. Proceedings of the International Conference on Algebraic and Symbolic Computation, Boston, MA, USA."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Madureira, A., Pereira, I., and Falc\u00e3o, D. (2013). Cooperative Scheduling System with Emergent Swarm Based Behavior. Advances in Information Systems and Technologies, Springer.","DOI":"10.1007\/978-3-642-36981-0_61"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Madureira, A., Cunha, B., and Pereira, I. (2014, January 6\u201311). Cooperation Mechanism for Distributed resource scheduling through artificial bee colony based self-organized scheduling system. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Beijing, China.","DOI":"10.1109\/CEC.2014.6900574"},{"key":"ref_73","unstructured":"R Core Team (2014). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/11\/8\/3325\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:34:18Z","timestamp":1760362458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/11\/8\/3325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":73,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["app11083325"],"URL":"https:\/\/doi.org\/10.3390\/app11083325","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]}}}