{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T07:10:15Z","timestamp":1733296215616,"version":"3.30.1"},"reference-count":64,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2023,5,10]]},"abstract":"Scheduling is a frequently studied combinatorial optimisation problem that often needs to be solved under dynamic conditions and to optimise multiple criteria. The most commonly used method for solving dynamic problems are dispatching rules (DRs), simple constructive heuristics that build the schedule incrementally. Since it is difficult to design DRs manually, they are often created automatically using genetic programming. Although such rules work well, their performance is still limited and various methods, especially ensemble learning, are used to improve them. So far, ensembles have only been used in the context of single-objective scheduling problems. This study aims to investigate the possibility of constructing ensembles of DRs for solving multi-objective (MO) scheduling problems. To this end, an existing ensemble construction method called SEC is adapted by extending it with non-dominated sorting to construct Pareto fronts of ensembles for a given MO problem. In addition, the algorithms NSGA-II and NSGA-III were adapted to construct ensembles and compared with the SEC method to demonstrate their effectiveness. All methods were evaluated on four MO problems with different number of criteria to be optimised. The results show that ensembles of DRs achieve better Pareto fronts compared to individual DRs. Moreover, the results show that SEC achieves equally good or even slightly better results than NSGA-II and NSGA-III when constructing ensembles, while it is simpler and slightly less computationally expensive. This shows the potential of using ensembles to increase the performance of individual DRs for MO problems.<\/jats:p>","DOI":"10.3233\/ica-230704","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T16:31:50Z","timestamp":1676997110000},"page":"275-292","source":"Crossref","is-referenced-by-count":2,"title":["Constructing ensembles of dispatching rules for multi-objective tasks in the unrelated machines environment"],"prefix":"10.1177","volume":"30","author":[{"given":"Marko","family":"\\DJurasevi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia"}]},{"given":"Francisco J.","family":"Gil-Gala","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oviedo, Oviedo, Spain"}]},{"given":"Domagoj","family":"Jakobovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia"}]}],"member":"179","reference":[{"doi-asserted-by":"crossref","unstructured":"Pinedo ML. Scheduling. Springer US; 2012.","key":"10.3233\/ICA-230704_ref1","DOI":"10.1007\/978-1-4614-2361-4"},{"issue":"2","key":"10.3233\/ICA-230704_ref2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10710-005-7580-7","article-title":"Evolutionary Scheduling: A Review","volume":"6","author":"Hart","year":"2005","journal-title":"Genetic Programming and Evolvable Machines"},{"key":"10.3233\/ICA-230704_ref3","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":"urasevi\u0107","year":"2018","journal-title":"Expert Systems with Applications"},{"unstructured":"Poli R, Langdon WB, McPhee NF. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd; 2008.","key":"10.3233\/ICA-230704_ref4"},{"key":"10.3233\/ICA-230704_ref5","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/978-3-030-19651-6_22","article-title":"Genetic Algorithm to Evolve Ensembles of Rules for On-Line Scheduling on Single Machine with Variable Capacity","author":"Gil-Gala","year":"2019","journal-title":"From Bioinspired Systems and Biomedical Applications to Machine Learning"},{"key":"10.3233\/ICA-230704_ref6","first-page":"251","article-title":"Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach","author":"Nguyen","year":"2013","journal-title":"Studies in Computational Intelligence"},{"key":"10.3233\/ICA-230704_ref7","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.asoc.2016.07.025","article-title":"Adaptive scheduling on unrelated machines with genetic programming","volume":"48","author":"urasevi\u0107","year":"2016","journal-title":"Applied Soft Computing"},{"key":"10.3233\/ICA-230704_ref8","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.future.2018.04.029","article-title":"Evolving priority rules for resource constrained project scheduling problem with genetic programming","volume":"86","author":"umi\u0107","year":"2018","journal-title":"Future Generation Computer Systems"},{"issue":"1","key":"10.3233\/ICA-230704_ref9","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 Transactions on Evolutionary Computation"},{"issue":"2","key":"10.3233\/ICA-230704_ref10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TEVC.2013.2248159","article-title":"Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming","volume":"18","author":"Nguyen","year":"2014","journal-title":"IEEE Transactions on Evolutionary Computation"},{"doi-asserted-by":"crossref","unstructured":"urasevi\u0107 M, Jakobovi\u0107 D. Heuristic and metaheuristic methods for the parallel unrelated machines scheduling problem: a survey. Artificial Intelligence Review. 2022 Aug.","key":"10.3233\/ICA-230704_ref11","DOI":"10.1007\/s10462-022-10247-9"},{"doi-asserted-by":"crossref","unstructured":"Masood A, Mei Y, Chen G, Zhang M. Many-objective genetic programming for job-shop scheduling. 2016. pp.\u00a0209-16.","key":"10.3233\/ICA-230704_ref12","DOI":"10.1109\/CEC.2016.7743797"},{"issue":"1-2","key":"10.3233\/ICA-230704_ref13","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":"urasevi\u0107","year":"2017","journal-title":"Genetic Programming and Evolvable Machines"},{"key":"10.3233\/ICA-230704_ref14","first-page":"1","article-title":"Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"10.3233\/ICA-230704_ref15","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 Transactions on Evolutionary Computation"},{"key":"10.3233\/ICA-230704_ref16","doi-asserted-by":"crossref","first-page":"101649","DOI":"10.1016\/j.jocs.2022.101649","article-title":"Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics","volume":"61","author":"urasevi\u0107","year":"2022","journal-title":"Journal of Computational Science"},{"doi-asserted-by":"crossref","unstructured":"Park J, Nguyen S, Zhang M, Johnston M. Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling. 2015. pp.\u00a092-104.","key":"10.3233\/ICA-230704_ref17","DOI":"10.1007\/978-3-319-16501-1_8"},{"issue":"6","key":"10.3233\/ICA-230704_ref18","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s10732-019-09416-x","article-title":"Creating dispatching rules by simple ensemble combination","volume":"25","author":"urasevi\u0107","year":"2019","journal-title":"Journal of Heuristics"},{"key":"10.3233\/ICA-230704_ref19","first-page":"119","article-title":"Constructing Ensembles of Dispatching Rules for Multi-objective Problems","author":"urasevi\u0107","year":"2022","journal-title":"Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence"},{"issue":"12","key":"10.3233\/ICA-230704_ref20","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":"Journal of the Operational Research Society"},{"issue":"1","key":"10.3233\/ICA-230704_ref21","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 & Intelligent Systems"},{"key":"10.3233\/ICA-230704_ref22","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1145\/3071178.3071185","article-title":"Automated Heuristic Design Using Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem","author":"Liu","year":"2017","journal-title":"Proceedings of the Genetic and Evolutionary Computation Conference. GECCO \u201917"},{"issue":"1","key":"10.3233\/ICA-230704_ref23","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TEVC.2021.3095261","article-title":"Genetic Programming With Niching for Uncertain Capacitated Arc Routing Problem","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"10.3233\/ICA-230704_ref24","first-page":"1","article-title":"Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"10.3233\/ICA-230704_ref25","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 Transactions on Cybernetics"},{"key":"10.3233\/ICA-230704_ref26","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1145\/3321707.3321790","article-title":"A Two-Stage Genetic Programming Hyper-Heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling","author":"Zhang","year":"2019","journal-title":"Proceedings of the Genetic and Evolutionary Computation Conference. GECCO \u201919"},{"key":"10.3233\/ICA-230704_ref27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-981-16-4859-5_13","article-title":"Multitask Learning in Hyper-Heuristic Domain with Dynamic Production Scheduling","author":"Zhang","year":"2021","journal-title":"Genetic Programming for Production Scheduling"},{"key":"10.3233\/ICA-230704_ref28","doi-asserted-by":"crossref","first-page":"114548","DOI":"10.1016\/j.eswa.2020.114548","article-title":"Designing dispatching rules with genetic programming for the unrelated machines environment with constraints","volume":"172","author":"Jaklinovi\u0107","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.3233\/ICA-230704_ref29","doi-asserted-by":"crossref","first-page":"141","DOI":"10.3233\/ICA-200646","article-title":"Rapid design of aircraft fuel quantity indication systems via multi-objective evolutionary algorithms","volume":"28","author":"Judt","year":"2021","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"4","key":"10.3233\/ICA-230704_ref30","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/mice.12637","article-title":"Methodology for analyzing the trade-offs associated with multi-objective optimization in transportation asset management under uncertainty","volume":"36","author":"Bai","year":"2021","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"9","key":"10.3233\/ICA-230704_ref31","doi-asserted-by":"crossref","first-page":"2150035","DOI":"10.1142\/S0129065721500350","article-title":"A multi-objective evolutionary approach based on graph-in-graph for neural architecture search of convolutional neural networks","volume":"31","author":"Xue","year":"2021","journal-title":"International Journal of Neural Systems"},{"issue":"Preprint","key":"10.3233\/ICA-230704_ref32","first-page":"1","article-title":"An improved loop subdivision to coordinate the smoothness and the number of faces via multi-objective optimization","author":"Liang","year":"2022","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"6","key":"10.3233\/ICA-230704_ref33","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1007\/s00158-017-1835-9","article-title":"Many-objective control optimization of high-rise building structures using replicator dynamics and neural dynamics model","volume":"56","author":"Gutierrez Soto","year":"2017","journal-title":"Structural and Multidisciplinary Optimization"},{"issue":"9","key":"10.3233\/ICA-230704_ref34","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1111\/mice.12646","article-title":"A multi-objective genetic algorithm strategy for robust optimal sensor placement","volume":"36","author":"Civera","year":"2021","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"6","key":"10.3233\/ICA-230704_ref35","doi-asserted-by":"crossref","first-page":"e12255","DOI":"10.1111\/exsy.12255","article-title":"Meta-heuristic multi-and many-objective optimization techniques for solution of machine learning problems","volume":"34","author":"Rodrigues","year":"2017","journal-title":"Expert Systems"},{"issue":"Preprint","key":"10.3233\/ICA-230704_ref36","first-page":"1","article-title":"A self-adaptive multi-objective feature selection approach for classification problems","author":"Xue","year":"2022","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"3","key":"10.3233\/ICA-230704_ref37","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.cie.2007.08.008","article-title":"Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems","volume":"54","author":"Tay","year":"2008","journal-title":"Computers & Industrial Engineering"},{"key":"10.3233\/ICA-230704_ref38","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/CEC.2019.8790112","article-title":"Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics","author":"Zhang","year":"2019","journal-title":"2019 IEEE Congress on Evolutionary Computation (CEC)"},{"key":"10.3233\/ICA-230704_ref39","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/978-981-16-4859-5_12","article-title":"Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling","author":"Zhang","year":"2021","journal-title":"Genetic Programming for Production Scheduling"},{"key":"10.3233\/ICA-230704_ref40","first-page":"536","article-title":"Genetic Programming with Pareto Local Search for Many-Objective Job Shop Scheduling","author":"Masood","year":"2019","journal-title":"AI 2019: Advances in Artificial Intelligence"},{"key":"10.3233\/ICA-230704_ref41","first-page":"1","article-title":"A Fitness-based Selection Method for Pareto Local Search for Many-Objective Job Shop Scheduling","author":"Masood","year":"2020","journal-title":"2020 IEEE Congress on Evolutionary Computation (CEC)"},{"issue":"1","key":"10.3233\/ICA-230704_ref42","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1162\/evco_a_00273","article-title":"Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling","volume":"29","author":"Xu","year":"2021","journal-title":"Evolutionary Computation"},{"doi-asserted-by":"crossref","unstructured":"Sagi O, Rokach L. Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery. 2018 Feb; 8(4).","key":"10.3233\/ICA-230704_ref43","DOI":"10.1002\/widm.1249"},{"key":"10.3233\/ICA-230704_ref44","doi-asserted-by":"crossref","first-page":"2250049","DOI":"10.1142\/S0129065722500496","article-title":"Reward-Penalty Weighted Ensemble for Emotion State Classification from Multi-Modal Data Streams","author":"Nandi","year":"2022","journal-title":"International Journal of Neural Systems"},{"issue":"02","key":"10.3233\/ICA-230704_ref45","doi-asserted-by":"crossref","first-page":"2050068","DOI":"10.1142\/S0129065720500689","article-title":"LieToMe: An ensemble approach for deception detection from facial cues","volume":"31","author":"Avola","year":"2021","journal-title":"International Journal of Neural Systems"},{"key":"10.3233\/ICA-230704_ref46","doi-asserted-by":"crossref","first-page":"105151","DOI":"10.1016\/j.engappai.2022.105151","article-title":"Ensemble deep learning: A review","volume":"115","author":"Ganaie","year":"2022","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"12","key":"10.3233\/ICA-230704_ref47","doi-asserted-by":"crossref","first-page":"8675","DOI":"10.1007\/s00521-019-04359-7","article-title":"A dynamic ensemble learning algorithm for neural networks","volume":"32","author":"Alam","year":"2020","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"10.3233\/ICA-230704_ref48","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3233\/ICA-200643","article-title":"Real-time facial expression recognition using smoothed deep neural network ensemble","volume":"28","author":"Benamara","year":"2021","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"3","key":"10.3233\/ICA-230704_ref49","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3233\/ICA-210649","article-title":"An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance","volume":"28","author":"Gasienica-J\u00f3zkowy","year":"2021","journal-title":"Integrated Computer-Aided Engineering"},{"key":"10.3233\/ICA-230704_ref50","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-319-30668-1_8","article-title":"Genetic Programming Based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches","author":"Park","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"issue":"4","key":"10.3233\/ICA-230704_ref51","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1162\/EVCO_a_00183","article-title":"A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling","volume":"24","author":"Hart","year":"2016","journal-title":"Evolutionary Computation"},{"key":"10.3233\/ICA-230704_ref52","first-page":"63","article-title":"An Investigation of Ensemble Combination Schemes for Genetic Programming based Hyper-heuristic Approaches to Dynamic Job Shop Scheduling","volume":"11","author":"Park","year":"2017","journal-title":"Applied Soft Computing"},{"issue":"1-2","key":"10.3233\/ICA-230704_ref53","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10710-017-9302-3","article-title":"Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment","volume":"19","author":"urasevi\u0107","year":"2017","journal-title":"Genetic Programming and Evolvable Machines"},{"key":"10.3233\/ICA-230704_ref54","doi-asserted-by":"crossref","first-page":"107606","DOI":"10.1016\/j.asoc.2021.107606","article-title":"Ensembles of priority rules for resource constrained project scheduling problem","volume":"110","author":"umi\u0107","year":"2021","journal-title":"Applied Soft Computing"},{"doi-asserted-by":"crossref","unstructured":"Gil-Gala FJ, Sierra MR, Menc\u00eda C, Varela R. Combining hyper-heuristics to evolve ensembles of priority rules for on-line scheduling. Natural Computing. 2020 Jun.","key":"10.3233\/ICA-230704_ref55","DOI":"10.1007\/s11047-020-09793-4"},{"issue":"1","key":"10.3233\/ICA-230704_ref56","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3233\/ICA-200634","article-title":"Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms","volume":"28","author":"Gil-Gala","year":"2021","journal-title":"Integrated Computer-Aided Engineering"},{"key":"10.3233\/ICA-230704_ref57","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1145\/3512290.3528807","article-title":"Novel Ensemble Collaboration Method for Dynamic Scheduling Problems","author":"urasevi\u0107","year":"2022","journal-title":"Proceedings of the Genetic and Evolutionary Computation Conference. GECCO \u201922"},{"key":"10.3233\/ICA-230704_ref58","doi-asserted-by":"crossref","first-page":"106637","DOI":"10.1016\/j.asoc.2020.106637","article-title":"Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment","volume":"96","author":"urasevi\u0107","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.3233\/ICA-230704_ref59","doi-asserted-by":"crossref","first-page":"22886","DOI":"10.1109\/ACCESS.2022.3151346","article-title":"A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment","volume":"10","author":"Planini\u0107","year":"2022","journal-title":"IEEE Access"},{"issue":"2","key":"10.3233\/ICA-230704_ref60","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 Transactions on Evolutionary Computation"},{"issue":"4","key":"10.3233\/ICA-230704_ref61","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 Transactions on Evolutionary Computation"},{"issue":"4","key":"10.3233\/ICA-230704_ref62","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"IEEE Transactions on Evolutionary Computation"},{"doi-asserted-by":"crossref","unstructured":"Riquelme N, Lucken CV, Baran B. Performance metrics in multi-objective optimization. In: 2015 Latin American Computing Conference. IEEE; 2015.","key":"10.3233\/ICA-230704_ref63","DOI":"10.1109\/CLEI.2015.7360024"},{"issue":"2","key":"10.3233\/ICA-230704_ref64","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.ejor.2020.11.016","article-title":"Performance indicators in multiobjective optimization","volume":"292","author":"Audet","year":"2021","journal-title":"European Journal of Operational Research"}],"container-title":["Integrated Computer-Aided Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/ICA-230704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T06:15:12Z","timestamp":1733292912000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/ICA-230704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":64,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/ica-230704","relation":{},"ISSN":["1069-2509","1875-8835"],"issn-type":[{"type":"print","value":"1069-2509"},{"type":"electronic","value":"1875-8835"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}