Relevant bibliographies by topics / Timetable Generator

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  2. Dissertations / Theses
  3. Book chapters
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Academic literature on the topic 'Timetable Generator'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Timetable Generator"

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Ullah, Dr Mohammed Mujeer. "Examination Timetable Generator." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1118–24. https://doi.org/10.22214/ijraset.2025.66520.

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Scheduling examination timetables in educational institutions is a complex, time-consuming task prone to conflicts and inefficiencies when done manually. This paper presents the design and implementation of an automated Examination Timetable Generator that leverages algorithmic techniques to optimize scheduling. The system addresses key constraints, including course overlaps, venue availability, and student group conflicts, while providing a user-friendly interface for dynamic input and customization. By automating the process, the generator reduces administrative workload, minimizes errors, a
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Mrs. S. R. Khokale, Akshay Jadhav, Rupali Chavan, Sakshi Wani, and Parag Iwanate. "A Survey Paper on Timetable Generator Using AI Methods." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 860–64. https://doi.org/10.47392/irjaeh.2025.0122.

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This research paper discusses an innovative Automated Timetable Generator leveraging the synergistic capabilities of AI and advanced optimization algorithms. Automated Timetable Generator leveraging Decision Tree, K-Means Clustering, and Random Forest algorithms for efficient scheduling. The Decision Tree algorithm is employed to classify and allocate time slots based on predefined constraints, ensuring that scheduling conflicts are minimized. K-Means Clustering is utilized to group subjects, faculty, and students based on similarities, optimizing resource allocation. The Random Forest model f
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K.Karpagavalli, S.P.Deepika, E.Jeeva, and Mushraf A.Mohammed. "Improved webapplication for automatic timetable generator." International Journal for Science and Advance Research In Technology 9, no. 3 (2023): 169–73. https://doi.org/10.5281/zenodo.7764134.

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Most colleges have different types of courses and each course has different subjects. In an institution, there are some staff like teaching, non-teaching, and lab assistants, each faculty teaching more than one subject. So, the timetable is scheduled for the faculty at provided times such that the timings of the staff do not overlap and the timetable we would schedule that makes the best use of all faculty and the subject's demand. For these things, the manual creation of a timetable takes more time. So, we use a genetic algorithm for this purpose. In our algorithm of Timetable Generation,
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Jaiswal, Akshay. "Kalanirmata: Developing an Efficient Automatic Timetable Generator for Colleges." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2829–34. https://doi.org/10.22214/ijraset.2025.70757.

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Kalanirmata: Developing an Efficient Automatic Timetable Generator for Colleges is a web-based application designed to simplify and optimize the process of creating timetables for educational institutions. The system allows administrators to securely log in and manage key aspects of timetable creation, including adding and updating teacher profiles, assigning subjects, and generating timetables for multiple academic departments, such as Computer Science, Data Science, Artificial Intelligence, Civil Engineering, Mechanical Engineering, and Electrical Engineering. The application utilizes a gene
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DR., K. SANTHI SREE, and RAKESH REDDY CHIRRA. "AUTOMATED TIMETABLE GENERATOR SYSTEM." International Journal Of Engineering Technology Research & Management (IJETRM) 09, no. 06 (2025): 70–74. https://doi.org/10.5281/zenodo.15602225.

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Designing academic timetables manually is a complex and time-consuming process, often leading to schedulingconflicts and inefficient use of resources. To address these issues, this project introduces an Automated TimetableGenerator System, a web-based solution aimed at simplifying and optimizing the scheduling process foreducational institutions. The system leverages constraint-based scheduling algorithms to ensure that faculty,classrooms, and course timings are efficiently allocated without overlaps. With a user-friendly interface, it allowsadministrators to define rules and constraints, then
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Patil, Prof Jyothi. "Automatic Timetable Generator." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1302–9. http://dx.doi.org/10.22214/ijraset.2023.53821.

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Abstract: The goal of this project is to create a time table generator for colleges. The creation of schedules is a very common issue that affects all educational institutions. The conflict between staff members' preferences is precisely where the issue arises. Every semester, colleges are required to create time tables, which used to be an extremely time-consuming task. Once the timetables are set for a given semester, the student is allowed to access them. Once the timetables are established for a particular semester, employees are also permitted to check the class allotment schedule.
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Katkar, Atharv. "Automated Time – Table Generator Using Genetic Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34941.

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Timetable creation is a very important job in schools as it helps to make sure that all resources are used appropriately and the students get an environment conducive for learning. In this paper, we introduce a new way of making them automatically using genetic algorithms for optimization. The method takes into account several restrictions such as availability of classes or subjects, preference of teachers about rooms they would like to teach in, size of rooms among many others which are necessary when generating optimized timetables for given sets of classes or resources. The structure of our
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Kumar, Sanjeev, and Rakesh Pandey. "Automated University Course Timetable Generator." International Journal of Industrial and Systems Engineering 36, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijise.2020.10028374.

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Kumar, Sanjeev, and Rakesh Pandey. "Automated university course timetable generator." International Journal of Industrial and Systems Engineering 36, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijise.2020.109133.

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K, Marimuthu, Moram Ranga Upendra Reddy, Dilip D, and Sathwik B. S. "Examination Timetable Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem40166.

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The creation of academic timetables is a complex and time-consuming task for educational institutions, requiring adherence to various constraints such as university regulations, faculty workloads, and resource availability. Traditional manual methods are often inefficient and error-prone, making it essential to develop automated solutions. This paper proposes an intelligent timetable generation system that leverages advanced optimization algorithms, including Evolutionary Algorithms, Tabu Search, Simulated Annealing, and Scatter Search, to address the challenges associated with manual scheduli
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Dissertations / Theses on the topic "Timetable Generator"

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Sandhu, Kuldeep Singh, and n/a. "Automating Class Schedule Generation in the Context of a University Timetabling Information System." Griffith University. School of Management, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030825.121338.

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This thesis examines the university timetable generation problem. It begins with a discussion on the conflicting terminology used and differentiates between the terms scheduling and timetabling. This discussion integrates with an overview of the problem itself both from practical and academic perspectives. This is followed by a summary of the apparently effective timetable solution generation algorithms. The literature is then examined in detail from that perspective. This literature review is then summarised in a form to highlight the use of these different solution algorithms. A classificat
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Sandhu, Kuldeep Singh. "Automating Class Schedule Generation in the Context of a University Timetabling Information System." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366189.

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This thesis examines the university timetable generation problem. It begins with a discussion on the conflicting terminology used and differentiates between the terms scheduling and timetabling. This discussion integrates with an overview of the problem itself both from practical and academic perspectives. This is followed by a summary of the apparently effective timetable solution generation algorithms. The literature is then examined in detail from that perspective. This literature review is then summarised in a form to highlight the use of these different solution algorithms. A classificat
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Antkowiak, Łukasz. "Parallel algorithms of timetable generation." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6083.

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Context: Most of the problem of generating timetable for a school belongs to the class of NP-hard problems. Complexity and practical value makes this kind of problem interesting for parallel computing. Objectives: This paper focuses on Class-Teacher problem with weighted time slots and proofs that it is NP-complete problem. Branch and bound scheme and two approaches to distribute the simulated annealing are proposed. Empirical evaluation of described methods is conducted in an elementary school computer laboratory. Methods: Simulated annealing implementation described in literature are adapted
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Söderberg, Anna, and Johan Wieslander. "Evaluating the Effects of using a Fuzzy Controller in Timetable Generation for Commuter Rail Services." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259204.

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Scheduling trains is a hard problem where current solutions typically create timetables that create undesirable amounts of delay for the trains that use them. This study considers the fact that conventional timetables, which only use fixed intervals to schedule trains, might not be optimal for minimising the delay and travel time of passengers. In this study, we compare this ”simple” timetable with timetables generated via a fuzzy controller which has access to information about the flow of passengers throughout the day. The hypothesis is that this fuzzy controller therefore becomes more ”inte
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Putra, Iponsyah, and 朴亦明. "Review of Traditional Train Timetables Generation and Incorporation into High-Speed Railway Timetable Model Considering Disturbances." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/31173790591998200817.

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碩士<br>國立中央大學<br>土木工程研究所<br>99<br>The Taiwan High-Speed Railway (THSR) system plays an important role in maintaining efficient transportation of passengers around Taiwan. Right now, more and more people depend on its stable operations and reliable services. However, the control mechanism between THSR and traditional railway systems is quite different. Drivers on THSR trains cannot control the cars by themselves; only the control center of THSR can give the commands, which are based on the train timetables and should be followed by the drivers to operate the cars. Moreover, when a disaster o
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Chen, Te-Che, and 陳德哲. "Study on Train Timetables Generation for Taiwan High-Speed Rail System Using Optimization and Agent Technologies." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/59660994614985070804.

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博士<br>國立中央大學<br>土木工程學系<br>102<br>As more and more governments are planning or constructing high-speed rail (HSR) systems, the central control mechanism of such systems, i.e., train timetables, should be investigated more in order to cope with various disturbances due to a disaster. Several optimization-based approaches have been successfully utilized for generating stable and reliable train timetables; however, few researchers have considered train circulation issues, especially for HSR systems, even though it could be such a way to reschedule the timetable against disturbances. This researc
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Book chapters on the topic "Timetable Generator"

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Prabhakaran, Bhavadharani Swaminadhan, Maheswari Shanmugam, Chaaru Bhala Karunakaran, Bhumika Murugesan, and Deepalakshmi Manimaran. "Automatic Timetable Generator Using Genetic Algorithm." In Advances in Computer Science Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-250-7_16.

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Kalshetti, Urmila, Deepika Nahar, Ketan Deshpande, Sanket Gawas, and Sujay Sudeep. "Dynamic Timetable Generation Using Constraint Satisfaction Algorithm." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2517-1_73.

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Brauner, Nadia, Rachid Echahed, Gerd Finke, Hanns Gregor, and Frederic Prost. "Specializing Narrowing for Timetable Generation: A Case Study." In Practical Aspects of Declarative Languages. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30557-6_4.

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Bajpai, Shruti, and Amrita Chaturvedi. "Ontology-Driven Framework to Automatically Generate University Timetable." In Communication and Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3325-9_6.

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Shah, Virti, Aastha Gadhvi, Neel Oza, Monali Sankhe, and Monika Mangla. "Optimizing Timetable Generation for Educational Institute Using Genetic Algorithm." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-716-8_37.

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Mistry, Paavan, and Raymond S. K. Kwan. "Generation and Optimization of Train Timetables Using Coevolution." In Genetic and Evolutionary Computation — GECCO 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_84.

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Wang, Yuqiang, Bo Li, Zhengduo Zhou, et al. "Research on Precision-Oriented Train Timetable Generation of High-Speed Railway." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9640-3_56.

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Bathla, Ravitashaw, Shubham Jain, and Rajeev Singh. "Introducing a Novel Parameter in Generation of Course Timetable with Genetic Algorithm." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1771-8_28.

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Yalçınkaya, Özgür. "A General Simulation Modelling Framework for Train Timetabling Problem." In Advances in Civil and Industrial Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0084-1.ch015.

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One of the most important problems encountered and needed to be solved in railway systems is train timetabling (scheduling) problem. This is the problem of determining a feasible timetable for sets of trains which does not violate track capacities and additionally satisfies some operational constraints of the railway system. In this chapter, a feasible timetable generator framework for stochastic simulation modelling is introduced. The objective is to obtain a feasible train timetable for all trains in the railway system, which includes train arrival and departure times at all visited stations
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Hu, Hui-Ling, and Ya-Ting Liu. "Constructing a User-Centric Visual Real-Time Surgical Dashboard." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240316.

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The existing surgery timetable is very simple, not user-friendly, and has very limited information. This article outlines the creation of a surgical dynamic dashboard for users, using Microsoft Power BI. The dashboard was developed based on in-depth interviews with the surgical team members to determine their requirements. The outcome of dashboard was evaluated by 5-likert scale questionnaire to measure satisfaction of all surgical team members. All team members were very satisfied with the dashboard compared to the existing surgery timetable. In addition, with the function of auto-generated a
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Conference papers on the topic "Timetable Generator"

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Abbas, Shaheer, Ahmad Maaz, Raja Hashim Ali, Talha Ali Khan, and Iftikhar Ahmed. "Automatic Timetable Generation using Neural Networks Trained by Genetic Algorithms." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871163.

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Khan, Ali Hasan, and Talha Imtiaz. "A Novel Genetic Algorithm Based Timetable Generator for Optimized University Timetable Solution." In 2024 International Conference on Engineering & Computing Technologies (ICECT). IEEE, 2024. http://dx.doi.org/10.1109/icect61618.2024.10581296.

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Bhatt, Chandradeep, Divyanshu Chaurasiya, Rahul Chauhan, Teekam Singh, Sanjay Sharma, and Dev Baloni. "Timetable Generator for Educational Institute Using Genetic Algorithm." In 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2023. http://dx.doi.org/10.1109/ictacs59847.2023.10389824.

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Sárkány, Marcell Csaba, and András Kovács. "Timetable generator and optimizer for Hungarian university students." In 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2023. http://dx.doi.org/10.1109/saci58269.2023.10158587.

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Popescu, Doru anastasiu, and Dragos Nicolae. "GENERATING A CLASS SCHEDULE WITH A REDUCED NUMBER OF CONSTRAINTS." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-156.

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In this paper we will present an application developed in Java which generates a timetable for both teachers and students. The application is intended for schools with a reduced number of students and which cannot afford a professional timetable generator. The innovation is brought by the intuitive user interface which makes the program very easy to use. It is structured in three sections: in the first one the user can introduce name of the teachers, in the second one the classes and the third one is designed for the introduction of the courses. Each input form from the courses sections is com
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Khadem-Sameni, Melody, Alex Landex, and John Preston. "Revising the UIC 406 Method: Revenue Generating Capacity." In 2010 Joint Rail Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/jrc2010-36281.

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The UIC 406 method has been published by the International Union of Railways (UIC) in 2004 and quantifies capacity utilisation for a given infrastructure. It is generally suitable for capacity analysis in European railways that are focused mainly on passenger operations and run according to exact timetables. This method defines railway capacity as “the total number of possible paths in a defined time window, considering the actual path mix or known developments respectively”. To measure the railway capacity consumption, timetable graphs can be used where by the given infrastructure and the typ
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Fulmański, Piotr, Theodore E. Simos, George Maroulis, George Psihoyios, and Ch Tsitouras. "Boid Based Timetable Generation." In SELECTED PAPERS FROM ICNAAM-2007 AND ICCMSE-2007: Special Presentations at the International Conference on Numerical Analysis and Applied Mathematics 2007 (ICNAAM-2007), held in Corfu, Greece, 16–20 September 2007 and of the International Conference on Computational Methods in Sciences and Engineering 2007 (ICCMSE-2007), held in Corfu, Greece, 25–30 September 2007. AIP, 2008. http://dx.doi.org/10.1063/1.2997324.

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"TRAIN TIMETABLE GENERATION USING GENETIC ALGORITHMS." In International Conference on Evolutionary Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003074801700175.

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Singh, Divanshu, Avish Khandelwal, Aniket Kumar, Harsh Bawaskar, and Prajakta Ugale. "Timelog: Genetic AI Enabled Timetable Generation." In 2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE). IEEE, 2024. http://dx.doi.org/10.1109/icwite59797.2024.10502752.

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Devi, M. Uma, J. Jayapradha, Bhaavya Naharas, and Saniya Sharma. "Automated timetable generation for academic institutions." In 4TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS 2023: ICIoT2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0217280.

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Reports on the topic "Timetable Generator"

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Navarro, Adoracion. The Implications of Developing a Philippine Nuclear Energy Program. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.61.

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Nuclear energy remains a promising option for the Philippines. But as the International Atomic Energy Agency (IAEA) has reiterated, deciding to use nuclear energy is a long-term commitment and one that should consider a time frame of 100 years from construction to decommissioning and waste disposal or storage. Thus, it would do well for the Philippines to follow the IAEA's Milestones Approach and for every government administration to be consistent in seeking progress through this approach. As a demonstration of understanding the commitment involved in nuclear energy development, the positive
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Mohammadian, Abolfazl, Amir Bahador Parsa, Homa Taghipour, Amir Davatgari, and Motahare Mohammadi. Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-033.

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Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway
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