Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Penjadwalan mata kuliah merupakan hal penting yang dilakukan pada awal semester akademik. Proses penyusunan jadwal kuliah secara manual seringkali mengalami kesulitan karena terdapat beberapa konstrain sehingga membutuhkan waktu yang lama. Penelitian ini bertujuan mengkaji algoritma-algoritma yang sesuai dengan masalah penjadwalan mata kuliah. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan optimasi penjadwalan. Proses pencarian literatur dilakukan pada Google Scholar dan Science Direct dengan memasukkan kata kunci utama “course timetable”, “university timetable problem”, “school scheduling”, dan “algoritma penjadwalan”. Hasil analisis literatur meliputi sebaran domain, analisis algoritma serta gap dari penelitian sebelumnya. Pada penelitian sebelumnya terdapat kekurangan seperti algoritma yang tidak dapat menghasilkan solusi optimal. Hasil sebaran domain yang diperoleh ialah universitas dan sekolah dengan persentase 88% dan 12% dari keseluruhan makalah. Adanya temuan 14 sebaran algoritma dapat diklasifikasikan menjadi 3 metode, yaitu heuristic, metaheuristic, dan hyper-heuristic. Berdasarkan hasil analisis, dapat diberikan beberapa rekomendasi. Untuk optimasi yang cepat, Simulated Annealing (SA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.481-10.102s. Untuk solusi waktu dan nilai fitness terbaik, Genetic Algorithm (GA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.964-73.461s dan nilai fitness 1.

Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures

Background: School closures around the world contributed to reducing the transmission of COVID-19. In the face of significant uncertainty around the epidemic impact of in-person schooling, policymakers, parents, and teachers are weighing the risks and benefits of returning to in-person education. In this context, we examined the impact of different school reopening scenarios on transmission within and outside of schools and on the share of school days that would need to be spent learning at a distance. Methods: We used an agent-based mathematical model of COVID-19 transmission and interventions to quantify the impact of school reopening on disease transmission and the extent to which school-based interventions could mitigate epidemic spread within and outside schools. We compared seven school reopening strategies that vary the degree of countermeasures within schools to mitigate COVID-19 transmission, including the use of face masks, physical distancing, classroom cohorting, screening, testing, and contact tracing, as well as schedule changes to reduce the number of students in school. We considered three scenarios for the size of the epidemic in the two weeks prior to school reopening: 20, 50, or 110 detected cases per 100,000 individuals and assumed the epidemic was slowly declining with full school closures. For each scenario, we calculated the percentage of schools that would have at least one person arriving at school with an active COVID-19 infection on the first day of school; the percentage of in-person school days that would be lost due to scheduled distance learning, symptomatic screening or quarantine; the cumulative infection rate for students, staff and teachers over the first three months of school; and the effective reproduction number averaged over the first three months of school within the community. Findings: In-person schooling poses significant risks to students, teachers, and staff. On the first day of school, 5-42% of schools would have at least one person arrive at school with active COVID-19, depending on the incidence of COVID in the community and the school type. However, reducing class sizes via A/B school scheduling, combined with an incremental approach that returns elementary schools in person and keeps all other students remote, can mitigate COVID transmission. In the absence of any countermeasures in schools, we expect 6-25% of teaching and non-teaching staff and 4-20% of students to be infected with COVID in the first three months of school, depending upon the case detection rate. Schools can lower this risk to as low as 0.2% for staff and 0.1% for students by returning elementary schools with a hybrid schedule while all other grades continue learning remotely. However, this approach would require 60-85% of all school days to be spent at home. Despite the significant risks to the school population, reopening schools would not significantly increase community-wide transmission, provided sufficient countermeasures are implemented in schools. Interpretation: Without extensive countermeasures, school reopening may lead to an increase in infections and a significant number of re-closures as cases are identified among staff and students. Returning elementary schools only with A/B scheduling is the lowest risk school reopening strategy that includes some in-person learning.

Scheduling as a part of School Management System

Learning Management Systems are very popular nowadays. They have many functions to support the learning process, classes home assignments, communications, and progress tracking. However, it lacks the functionality for the management staff like scheduling (planning) and reporting. Here we propose the software solution, which solves this issue and provides the scheduling for the classes in a school or university, considering requirements, limitations, and wishes. An innovative approach was applied to the scheduling problem. This solution is based on the workforce management techniques known previously. The first positive feedbacks from Iraq schools, where we implemented this solution, support us for the next development and improvements of the system. The focus of the paper is the scheduling module of the system developed, the context of the task (the scope), and arguing why it is important. The method from the area of workforce management systems was taken, adopted, and applied to the new task of school scheduling construction. This is the novelty of the presented work.

Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah

AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

Optimalaus mokyklos tvarkaraščio sudarymo problema

Straipsnyje aprašomas profi linių klasių tvarkaraščio sudarymas. Šio tipo uždaviniams spręsti nėra sukurta polinominio sudėtingumo algoritmų, todėl naudojami euristiniai optimizavimo metodai. Šiame straipsnyje aprašomi rezultatai, gauti naudojant lokalios paieškos metodus – lokalų determinuotą, lokalų atsitiktinį, atkaitinimo modeliavimo ir Bayeso – siekiant palengvinti profi linių klasių uždavinio sprendimą. Straipsnyje aprašoma nauja metodika tokio tipo uždaviniams spręsti. Tai Atkaitinimo modeliavimo parametrų optimizavimas naudojant Bayeso metodus. Kitas naujumas, aprašomas šiame straipsnyje, yra vektorinis optimizavimas naudojant tokius Pareto optimalius tvarkaraščius, kurie tenkintų individualius euristinius mokyklos kriterijus. Sukurtoje programoje „Optima“ yra įdiegti keturi optimizavimo metodai. Analizuojami rezultatai gauti taikant šiuos metodus.Optimal School Scheduling ProblemLina Pupeikienė, Eugenijus Kurilovas SummaryThe main problem investigated in the paper is optimization of the profi led school schedule. This kind of task does not have any algorithms of polynomial complexity therefore the principal attention is paid to heuristic methods. The paper reports on the results of experimentation with the local search techniques – such as Local, Local Random, Simulated Annealing and Bayes – for optimization of the profi led school schedule. A new element of this work is optimization of Simulated Annealing (SA) parameters using special Bayes (BA) methods. Another new element is application of vectorial optimization theory by fi xing Pareto optimal schedules such that would satisfy the subjective criteria of a concrete school. The created “Optima” program has four optimization algorithms for making the best school schedule. The results of application of each technique are analyzed.