Timetable Scheduling Research Articles

Genetic algorithm to solve the problems of lectures and practicums scheduling

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

Paperless Master Timetable Scheduling System

Paperless Master Timetable Scheduling System

Use Historic Information as Self Organizing Resources for an Intelligent Timetable Scheduling System

An application of office automation is proposed in this paper. We use past historic timetable data as templates to intelligently adjust a better evolution of a set of more usable timetables. We develop a two staged system: First of all, a feasible set of timetables were generated via blank-filled technique. These timetables merely satisfied users’ basic constraints. Then, historic tables and teachers’ preferences were feed to the system in order to fine tune these tables. The intelligent system has the capability to self-organize these set of timetables. We conduct a questionnaire for volunteers including students and instructors. Two different timetable sets (before/after the tuning process) were given to these testers. Fixed sized sample of candidates were randomly picked regularly. And the chosen testers have to grade the tables depending on their satisfactoriness toward the timetable they received and presumably they are going to use that timetable later on. A two sample Welch t-test on the average of testers’ score was given. Under 90% of confidence level, there is a significant difference between the satisfactory mean of these tables. Auto-tuned timetable bears comparison with the manual work of the current assistant.

Strategic timetable scheduling for last trains in urban railway transit networks

Well-designed timetables, with appropriate coordination between the last feeder trains and the last connecting trains so that passengers can enjoy “smooth” transfers, are desired by passengers and are a major service goal of metro corporations. This paper addresses the strategically last train scheduling problem in urban railway transit networks. First, two practical optimization models for last trains are proposed to minimize the standard deviation of transfer redundant times and to balance the last train transfers in subway networks. Second, we design a new heuristic algorithm to solve the developed models. Finally, the models and the heuristic are applied to the Beijing subway network. The results show that the last train timetables for the Beijing subway network have been improved by increasing the average travelling speed and the average technical speed by 9.9% and 8.42%, respectively. This means that the last train passengers can save 97.4min in total in the Beijing subway network when riding last trains.

An Experimental On Time Table Generator

This project introduces a practical timetabling algorithm capable of taking care of both strong and weak constraints effectively, used in an automated timetabling system. So that each teacher and student can view their timetable once they are finalized for a given semester but they can‘t edit them. Timetable Generation System generates timetable for each class and teacher, in keeping with the availability calendar of teachers, availability and capacity of physical resources (such as classrooms, laboratories and computer room) and rules applicable at different classes, semesters, teachers and subjects level. Even though most college administrative work has been computerized, the lecture timetable scheduling is still mostly done manually due to its inherent difficulties. The manual lecture-timetable scheduling demands considerable time and efforts. The lecture-timetable scheduling is a Constraint satisfaction problem in which we find a solution that satisfies the given set of constraints. A college timetable is a temporal arrangement of a set of lectures and classrooms in which all given constraints are satisfied. Creating such timetables manually is complex and time-consuming process. By automating this process with computer assisted timetable generator can save a lot of precious time of administrators who are involved in creating and managing course timetables.

Applications of Ecological Interface Design for Engineering Course Design

Instructional course design in engineering poses a challenge for many practitioners given the lack of defined rules and complicated interactions between the goals of maximizing student learning and satisfying administrative requirements. The aim of this work was to create a proof-of-concept for the application of Ecological Interface Design (EID) to a complex intentional system, specifically the development of a theoretical interface for university-level engineering course design. This work reviews the establishment and application of an abstraction hierarchy for considering pedagogical principles (Bloom’s Taxonomy and Active Learning Pyramid) in conjunction with organizational rules for the University of Toronto. Presented are also selections from a preliminary interface mock-up which addresses learning outcomes, activity and content selection, and timetable scheduling.

Genetically Evolved Solution to Timetable Scheduling Problem

The simultaneous advancement in genetic modeling and data computational capabilities has prompted profound interest of scientists across the globe in the field of timetable scheduling. The wider usage of timetable scheduling in complex data manipulation and computation has attracted many researchers to put forward their theory regarding the use of genetic algorithms. The progression on this field has increased the efficiency of the timetable to use the limited resources in the given time to get productive results. This paper describes various genetic algorithmic methods.

Cognitive rhythms and evolutionary algorithms in university timetables scheduling

The main purpose of this research is to design a methodology based on evolutionary algorithms to university timetable scheduling. This methodology will consider the students’ cognitive rhythms, which establish that teaching certain subjects in specific time intervals is much better than other techniques. This project takes place in three phases. First of all, there is a description of the different techniques used to solve this problem. Then, a new methodology based on cognitive rhythms and evolutionary algorithms is proposed, and finally, different methodologies are compared to determine the best. It is concluded that evolutionary algorithms are more efficient than other techniques in the university timetable scheduling. Future lines of research will determine the impact of these techniques within the students’ learning process.

COMPARISON USING PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR TIMETABLE SCHEDULING

Lecturer timetable scheduling is an important part in the resource allocation planning. Due to the large amount of transactions and various related constraints have to be taken into account in timetable scheduling process, resource manager team shall need a lot of time to the solve the problem. This research is aimed to discuss the application of Particle Swarm Optimization (PSO) that can be used to automatically generate optimal lecturer timetable scheduling. Using Software Laboratory Center (SLC) data, some hard constraints are taken into account such as the assistant should teach according to their qualifications, teaching in their work shift and doesn’t teach any course that are being taken. Some soft constraints are also considered and the associated cost function is built based on these hard and soft constraints. Based on the computational results, the amount of penalty obtained by the PSO is much smaller than the GA on 500th iteration. The calculation is performed by comparing the amount of penalty that earned each time a hard constraint or soft constraint is violated by the implementation of PSO or GA to the total penalty obtained when all constraints are violated.

Scheduling Additional Train Unit Services on Rail Transit Lines

This paper deals with the problem of scheduling additional train unit (TU) services in a double parallel rail transit line, and a mixed integer programming (MIP) model is formulated for integration strategies of new trains connected by TUs with the objective of obtaining higher frequencies in some special sections and special time periods due to mass passenger volumes. We took timetable scheduling and TUs scheduling as an integrated optimization model with two objectives: minimizing travel times of additional trains and minimizing shifts of initial trains. We illustrated our model using computational experiments drawn from the real rail transit line 16 in Shanghai and reached results which show that rail transit agencies can obtain a reasonable new timetable for different managerial goals in a matter of seconds, so the model is well suited to be used in daily operations.

A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem

Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on the quality of service provided by the transport system. This paper explores the periodic railway timetable scheduling (PRTS) problem, with the objective to minimize the average waiting time of the transfer passengers. Unlike traditional PRTS models that only involve service lines with fixed cycles, this paper presents a more flexible model by allowing the cycle of service lines and the number of transfer passengers to vary with the time period. An enhanced differential evolution (DE) algorithm with dual populations, termed “dual-population DE” (DP-DE), was developed to solve the PRTS problem, yielding high-quality solutions. In the DP-DE, two populations cooperate during the evolution; the first focuses on global search by adopting parameter settings and operators that help maintain population diversity, while the second one focuses on speeding up convergence by adopting parameter settings and operators that are good for local fine tuning. A novel bidirectional migration operator is proposed to share the search experience between the two populations. The proposed DP-DE has been applied to optimize the timetable of the Guangzhou Metro system in Mainland China and six artificial periodic railway systems. Two conventional deterministic algorithms and seven highly regarded evolutionary algorithms are used for comparison. The comparison results reveal that the performance of DP-PE is very promising.

Integration of a Course Enrolment and Class Timetable Scheduling in a Student Information System

This paper is dealing with the most important issues of two of the most significant university administrative processes – the process of term enrolment with course enrolment and the process of organization of classes and scheduling a conflict-free class timetable. In the first part of the paper an introduction to the problem is set and the impact and interrelation of the two processes is explained in detail. In the second part of the paper, the focus is on the process of class timetable scheduling, with an analysis of how severe is the risk of having conflicts in the class schedule. Conflicts in the class schedule are rarely introduced due to the lack of resources, in the usual understanding of the term, but are mostly related to the availability human resources. The analysis gives more details on the probability that the students will have a conflict in the timetables among different courses they attend. The third part of the paper discusses some of the formal complexities of the course enrolment process and gives short overview of an earlier effort to overcome such complexities with an introduction of a virtual academic adviser. The virtual adviser components, are then integrated with a recommender system that gives each student personalized course recommendations based on historical data of courses and success. Finally, a solution is given in the form of a model that is currently under development in which a single unifying process is envisioned that covers both earlier processes. The integrated process defines a continuous dialogue between the student groups, enrolment administration and scheduling administration. It is expected from the solution to streamline both processes into a fully integrated system that will improve the student satisfaction from the organization and transparency of the administration.

A Improved Bus Timetable Scheduling Model Using Quantum Genetic Algorithm Based on Penalty Strategy

Considering the influence that the cycles of signal lamp have on the waiting time, a bus scheduling model is presented in this paper based on the trade-off between the cost of bus operator and benefits of passengers. In order to handle with the low efficiency brought about by the refused strategy, a new fitness function is designed according to penalty strategy, and then traditional genetic algorithm is replaced by quantum genetic algorithm to accelerate the search of optimal parameters further. The results of experiment show that the presented method is effective.

Medical and health care professional education in the 21st century: institutional, national and global perspectives

Medical and health care professional education in the 21st century: institutional, national and global perspectives

Modeling Circulation of Train-Set with Multiple Routing

Planning on utilization of train-set is one of the key tasks of transport organization for passenger dedicated railway in China. It also has strong relationships with timetable scheduling and operation plans at a station. To execute such a task in a railway hub pooling multiple railway lines, the characteristics of multiple routing for train-set is discussed in term of semicircle of train-sets' turnover. In programming the described problem, the minimum dwell time is selected as the objectives with special derive constraints of the train-set's dispatch, the connecting conditions, the principle of uniqueness for train-sets, and the first plus for connection in the same direction based on time tolerance σ. A compact connection algorithm based on time tolerance is then designed. The feasibility of the model and the algorithm is proved by the case study. The result indicates that the circulation model and algorithm about multiple routing can deal with the connections between the train-sets of multiple directions, and reduce the train's pulling in or leaving impact on the station's throat.

MAS_UP-UCT: A Multi-Agent System for University Course Timetable Scheduling

Many real-world applications are mapped into combinatorial problems. An example of such problem is timetable scheduling. In this case, the two basic characteristics can be defined by its distributed and dynamic environment. One efficient solution to solve this problem could be provided by an agent-based approach. A timetable scheduling problem can be modelled as a multi-agent system that provides the final schedule by taken into account all the restrictions. In this paper it is presented a preliminary research work that involves the development of a multi-agent system for university course timetable scheduling, named MAS_UP-UCT. We focus on the architecture of the multi-agent system, and on the evaluation of the communication process by using the interaction diagrams.

Educational Management: Timetable Scheduling Algorithms

Educational Management: Timetable Scheduling Algorithms

School time table scheduling in Prolog

The school Time-Table Scheduling task is a very hard operations research and engineering problem, especially when-implemented in a conventional language. That is due to the imperative, deterministic nature of most conventional languages, such as BASIC and PASCAL and to the long series of constraints and goals inside the problem. The descriptive, logic-based and nondeterministic nature of Prolog language, and its ability to backtrack allows one to easily obtain a deductive data base, mixing the facts, rules, and constraints of the Time-table. Two systems, one nonmonotonic, and one monotonic with a non-monotonic reasoning structure are compared and their performances in a significant test are discussed. The approach may be easily generalized to other analogous engineering, scheduling and operations research problems.

Operational Research in London Transport

London Transport is a mature public transport system with many serious operational problems. Some have been around a long time, such as labour availability and methods of controlling services. Others have more recently become important; problems of vehicle availability—levels of spares, management of engineering supply. Yet others are periodic, such as debate over types of operation—crew versus OMO, ticketing systems and network structure. Operational research has been assisting management in understanding and searching for solutions in each of these. The paper describes and relates their key features. In addition, although it is not seen as a major problem area, the London Transport experience of timetabling and duty scheduling by computer will be summarised.