An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning

Wouda, Niels A.; Aslan, Ayşe; Vis, Iris F. A.

doi:10.1016/j.cor.2022.106089

Cited by 3 publications

(3 citation statements)

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“…In recent years, other strategies have been used that have allowed good quality solutions to be achieved in less time to large problems; however, these do not guarantee the global optimum of the problem. The use of local search methods stands out (Demirović & Musliu, 2017;Goh, Kendall & Sabar, 2017;Rezaeipanah, Matoori & Ahmadi, 2021;Saviniec, Santos & Costa, 2017Song, Liu, Tang, Peng & Chen, 2018), metaheuristic tabu search techniques (Goh et al, 2017;Lü & Hao, 2010;Saviniec et al, 2018) and genetic algorithms (Arias-Osorio & Mora-Esquivel, 2020;Beligiannis, Moschopoulos & Likothanassis, 2009;Feng, Lee & Moon, 2017;Junn, Obit & Alfred, 2018;Khonggamnerd & Innet, 2009;Lin, Chin, Tsui & Wong, 2016;Niknamian, 2021;Raghavjee & Pillay, 2010;Rezaeipanah et al, 2021;Yigit, 2007), solutions based on minimal disturbance (Barták, Müller & Rudová, 2003;Lindahl, Stidsen & Sørensen, 2019;Phillips, Walker, Ehrgott & Ryan 2017), in addition to hyper-heuristics (Ahmed, Özcan & Kheiri, 2015;Junn, Obit, Alfred & Bolongkikit, 2019;Kheiri, Özcan & Parkes, 2016) among others (Cruz-Rosales et al, 2022;Esmaeilbeigi, Mak-Hau, Yearwood, & Nguyen, 2022;Mirghaderi, Alimohammadlo & Fotovvati, 2023;Wouda, Aslan & Vis, 2023).…”

Section: Introductionmentioning

confidence: 99%

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Hernández-Vázquez,

Hernández-Vázquez

2024

JER

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The preparation of school schedules is a problem that occurs in many educational institutions around the world. From the mathematical approach, school schedules are considered NP-hard, since the computational time in searching for the solution can increase exponentially by increasing the number of variables, or by the complexity of the restrictions. Different strategies are reported in the literature to solve this problem; however, these do not guarantee finding the best solution or global optimum of the problem. This document establishes a validation of the three-stage assignment strategy that has been used in the solution of school schedules, whose results are characterized by obtaining good solutions in short times through the exact branching and bounding technique. Validation consists of demonstrating that the strategy reaches the global optimum in a school schedule problem.

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Section: Introductionmentioning

confidence: 99%

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Hernández-Vázquez,

Hernández-Vázquez

2024

JER

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“…Pembelajaran yang dipersonalisasi menawarkan metode alternatif untuk pendidikan sesuai ukuran untuk semua di sekolah, dan telah mengalami peningkatan penyesuaian selama beberapa tahun terakhir. Fokus pembelajaran yang dipersonalisasi pada pendidikan yang digerakkan oleh siswa membutuhkan metode penjadwalan yang baru (Wouda et al, 2023).…”

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Implementasi Personalized Learning Menggunakan Litercy Ict Digital : Sebuah Systematic Literatur Review

Winarno

2024

hinef

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Abstract. The different learning needs of students are met by accommodating and personalizing their education. Technology is often used to facilitate the personalization of student learning environments. However, the educational world is currently struggling to implement such an approach. The change in digital literacy practices in the context of education requires a continuous re-examination of the conceptualization of digital literature education. The research uses the method of systematic literature review (SLR) to trace selected literature from 35 Scopus indexed journals on the implementation of personalized learning using digital ICT literacy. The results of the research show that the urgent need of students for personalized learning becomes more evident as educational information progresses. Personalized Recommendation Technology (PR) can help personalized learning and improve student learning efficiency. The development of personalized learning platforms can be done based on CF (collaborative filtering) algorithms, and perform system needs analysis. But it still requires an evaluation of personalized learning features in order to be able to understand the behavioral features of learning intelligently. Abstrak. Kebutuhan pembelajaran yang berbeda dari siswa dipenuhi dengan mengakomodasi dan mempersonalisasi pendidikan mereka. Teknologi sering digunakan untuk memfasilitasi personalisasi lingkungan belajar siswa. Namun, saat ini dunia pendidikan mengalami kesulitan untuk menerapkan pendekatan tersebut. Perubahan praktik literasi digital dalam konteks pendidikan mengharuskan terus meninjau kembali konseptualisasi pendidikan literasi digital. Penelitian ini menggunakan metode systematic literature review (SLR) untuk menelusuri literatur-literatur terpilih dari 35 jurnal terindeks Scopus tentang implementasi pembelajaran dipersonalisasi menggunakan litercy ICT digital. Hasil penelitian menunjukan kebutuhan mendesak pelajar akan pembelajaran yang dipersonalisasi menjadi lebih jelas seiring dengan kemajuan informasi pendidikan. Teknologi rekomendasi yang dipersonalisasi (PR) dapat membantu pembelajaran yang dipersonalisasi dan meningkatkan efisiensi pembelajaran peserta didik. Pengembangan platform pembelajaran yang dipersonalisasi dapat dilakukan berdasarkan algoritma CF (collaborative filtering), dan melakukan analisis kebutuhan sistem. Tetapi tetap perlu adanya evaluasi fitur pembelajaran yang dipersonalisasi agar dapat memahami fitur perilaku belajar secara cerdas.

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“…Furthermore, it supports many other single-trajectory neighbourhood search algorithms as special cases, including iterated local search (ILS), variable neighbourhood search (VNS), and the greedy randomised adaptive search procedure (GRASP). The package has already been successfully used for research into methodological improvements of ALNS itself (Reijnen et al, 2022), and for the development of a high-quality ALNS metaheuristic solving an industry problem (Wouda et al, 2023). Because of this success, we expect the package to be useful to the wider operations research community.…”

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confidence: 99%

ALNS: a Python implementation of the adaptive large neighbourhood search metaheuristic

Wouda¹,

Leon²

2023

JOSS

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An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning

Cited by 3 publications

References 16 publications

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Implementasi Personalized Learning Menggunakan Litercy Ict Digital : Sebuah Systematic Literatur Review

ALNS: a Python implementation of the adaptive large neighbourhood search metaheuristic

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