Personal Unique Time Table Generator For Students in UTP

This project is to create a new system that generate unique timetable for students in Universiti Teknologi Petronas which include function for reducing time consumption and automate student manual process during timetabling or find alternative of slot. EasyPHP is use to create dynamic web application including the algorithm. Other than that, this project also aims to improvise the current timetable in term of Human Computer Interaction where better visual design and application of colour are included. As add on, this project can provide backup timetable in various medium such as smartphone, social network and email as both of them are the most gadget and site used by students nowadays. At same time, students can do discussion from the medium aforementioned such as Facebook’s group and GoogleGroup’s thread

Personal Unique Time Table Generator For Students in UTP

This project is to create a new system that generate unique timetable for students in Universiti Teknologi Petronas which include function for reducing time consumption and automate student manual process during timetabling or find alternative of slot. EasyPHP is use to create dynamic web application including the algorithm. Other than that, this project also aims to improvise the current timetable in term of Human Computer Interaction where better visual design and application of colour are included. As add on, this project can provide backup timetable in various medium such as smartphone, social network and email as both of them are the most gadget and site used by students nowadays. At same time, students can do discussion from the medium aforementioned such as Facebook’s group and GoogleGroup’s thread

Transformation of the university examination timetabling problem space through data pre-processing

This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research

Transformation of the university examination timetabling problem space through data pre-processing

This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research