Timetable Scheduling Research Articles

Final Examination Time Table Scheduling using Integer Programming with Average Time Slot Gap Minimization

Final Examination Time Table Scheduling using Integer Programming with Average Time Slot Gap Minimization

Exploring Legal Aspects Related to Emerging Technologies in Tanzanian Distance Education

The emergence of new technologies in the ICT sector and their integration into Tanzanian distance education presents both opportunities and challenges for the educational field. Innovations like Artificial Intelligence, robotics, Mobile learning, e-learning, cloud computing, video-assisted learning, and Digital Content Platforms play a crucial role in improving the teaching and learning process. These technologies have not only transformed various aspects of universities, such as teaching methods, assessment procedures, administration, examination management, financial records, and timetable scheduling but have also expanded educational access, enhanced quality, and reduced costs. This abstract provides an overview of a comprehensive research study focusing on the legal aspects related to the use of emerging technologies in Tanzanian distance education. As distance education becomes increasingly popular as a means to enhance access and quality of education in Tanzania, it is essential to consider the legal dimensions associated with this transformation. While technology brings significant improvements to the education sector, it also introduces legal, security, privacy, and safety challenges that require attention and resolution. From a legal perspective, the technology involved in electronic learning (e-learning) presents specific challenges because many existing legal and policy standards in education are primarily designed for traditional brick-and-mortar classrooms and face-to-face interactions. This research aims to identify and analyze the legal issues surrounding the implementation of emerging technologies in distance education. The study utilizes doctrinal and comparative research methods to examine international legal instruments, policies, and laws from other jurisdictions. The findings indicate that the current policies and legal instruments governing emerging technologies in Tanzania do not adequately address the existing legal gaps.

A practical approach to college timetable scheduling

This paper formulates the college timetable scheduling (CTTS) as a constraint satisfaction problem (CSP) in a manner that is easy to implement. Timetable scheduling is a process that is revised in every academic session and requires a lot of constraint checking, so it is inefficient to check the same type of constraints repeatedly to create a proper schedule manually. CSP is a natural choice for automating this process. The prototype presented in this paper is aimed to offer such assistance in scheduling classes for different courses running in an academic session keeping given different constraint checking like the limit check of the maximum number of lectures that can be assigned to a faculty, limit check of the maximum number of lectures that can be assigned to a course, detecting conflicts between courses scheduled in the same time slots, preventing overlapping assignments for faculty members in the same time slots. Here, first, the different aspects of the timetable scheduling problem are addressed, and then a technique is devised to help map the problem as a CSP.

Auto Timetable Management Mobile Application

In this fast-paced lifestyle, we play multiple roles and bear different responsibilities. This kind of lifestyle needs proper time management skills to look after the tasks and duties that have been entrusted. In order to do time management effectively, tools such as timetables are commonly adopted. Rescheduling of a timeslot in a timetable could happen due to certain unforeseen events that require immediate action. The rearrangement of one timeslot could possibly affect another timeslot in a timetable, essentially, it needs extra time and effort to draw new planning. In this paper, we proposed to develop an auto timetable scheduling mobile application to combat the hustle of manual timetable management. By assigning priority levels and constraints such as the preferred time and date of a task, the proposed application can reschedule and generate a new timetable automatically. With the proposed application, we claim that users can reduce the hustle of creating and managing their timetables in a manual way.
 

High-speed train timetable optimization based on space–time network model and quantum simulator

Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.

THE HIGHER EDUCATIONAL INFORMATION SYSTEM: MANAGEMENT OF THE TIMETABLE SCHEDULING AND LOGISTICS OF THE EDUCATIONAL PROCESS

In modern conditions, one of the important areas of development of a higher educational institution is the automation of educational activities through the implementation of a complex information system. In order to increase the effectiveness of simultaneous management of various aspects of educational activity, the information system should combine a wide range of functionality, including the timetable scheduling module. This paper is dealing with the one of the important issues of the most significant university administrative processes - timetable scheduling. This module should support working with big data and have the appropriate software and technical infrastructure, analytical tools, reporting units and error detection units. The article discusses some aspects of the timetable module. A description of the software and technical infrastructure of the business process of forming the university schedule is given.

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

Graph Coloring in University Timetable Scheduling

Addressing scheduling problems with the best graph coloring algorithm has always been very challenging. However, the university timetable scheduling problem can be formulated as a graph coloring problem where courses are represented as vertices and the presence of common students or teachers of the corresponding courses can be represented as edges. After that, the problem stands to color the vertices with lowest possible colors. In order to accomplish this task, the paper presents a comparative study of the use of graph coloring in university timetable scheduling, where five graph coloring algorithms were used: First Fit, Welsh Powell, Largest Degree Ordering, Incidence Degree Ordering, and DSATUR. We have taken the Military Institute of Science and Technology, Bangladesh as a test case. The results show that the Welsh-Powell algorithm and the DSATUR algorithm are the most effective in generating optimal schedules. The study also provides insights into the limitations and advantages of using graph coloring in timetable scheduling and suggests directions for future research with the use of these algorithms.

Examination Timetable Scheduling Using Graph Coloring for Faculty of Science

Graph coloring is an important aspect of graph theory with great application in University timetable scheduling. The examination timetable scheduling maximizes the time and minimizes resources (venue and invigilators) to give effective output of the event. The constraint based approach method is implemented in scheduling examination timetable for Faculty of Science, Federal University Lokoja. Then the results on thirteen (13) departments give a 3-timeslot within twelve (12) days examination period with no students having two (2) examinations per day. In all, there is no conflict of venues and courses thereby giving an efficient scheduling process for the entire examination.

Modeling and Efficient Passenger-Oriented Control for Urban Rail Transit Networks

Real-time timetable scheduling is an effective way to improve passenger satisfaction and to reduce operational costs in urban rail transit networks. In this paper, a novel passenger-oriented network model is developed for real-time timetable scheduling that can model time-dependent passenger origin-destination demands with consideration of a balanced trade-off between model accuracy and computation speed. Then, a model predictive control (MPC) approach is proposed for the timetable scheduling problem based on the developed model. The resulting MPC optimization problem is a nonlinear non-convex problem. In this context, the online computational complexity becomes the main issue for the real-time feasibility of MPC. To reduce the online computational complexity, the MPC optimization problem is therefore reformulated into a mixed-integer linear programming (MILP) problem. The resulting MILP problem is exactly equivalent to the original MPC optimization problem and can be solved very efficiently by existing MILP solvers, so that we can obtain the solution very fast and realize real-time timetable scheduling. Numerical experiments based on a part of Beijing subway network show the effectiveness and efficiency of the developed model and the MILP-based MPC method.

Scenario-Based MPC for Real-Time Passenger-Centric Timetable Scheduling of Urban Rail Transit Networks

Effective timetable scheduling strategies are essential for passenger satisfaction in urban rail transit networks. Most existing passenger-centric timetable scheduling approaches generate a timetable according to deterministic passenger origin-destination (OD) demands. As passenger OD demands in urban rail transit networks generally show a high level of uncertainty, an effective timetable scheduling approach should take the uncertain passenger flows into account to generate a reliable timetable. In this paper, a scenario-based model predictive control (SMPC) approach is presented to handle uncertain passenger flows based on a passenger absorption model, where uncertainties are captured by several representative scenarios according to historical data. In each SMPC step, the optimization problem for generating the timetable can be reformulated as a mixed-integer linear programming (MILP) problem, which can be efficiently solved using current MILP solvers. A probabilistic performance level can be then determined based on the performance of SMPC under the representative scenarios. Numerical experiments based on the Beijing subway network are conducted to evaluate the efficacy of the proposed approach.

Hybrid Vehicle Timetable Scheduling Under Fuzzy Decision Making

Hybrid Vehicle Timetable Scheduling Under Fuzzy Decision Making

University Examination Timetable Scheduling Using Constructive Heuristic Compared to Genetic Algorithm

University Examination Timetable Scheduling Using Constructive Heuristic Compared to Genetic Algorithm

Timetable Scheduling using Bipartite Graph

Abstract: In any educational institution, the two most common academic scheduling problems are course timetabling and exam timetabling. A schedule is desirablewhich combines resources like teachers, subjects, students, classrooms in a way to avoid conflicts satisfying various essential and preferential constraints. The timetable scheduling problem is known to be NP Complete but the corresponding optimization problem is NP Hard. This problem is solved using bipartite graph. Hence a heuristic approach ispreferred to find a nearest optimal solution within reasonable running time

Robust metro train scheduling integrated with skip-stop pattern and passenger flow control strategy under uncertain passenger demands

As the passenger demands, especially commuters, in the megacities increase dramatically, the congestion problem of metro lines becomes severe and affects passengers’ travel experience. More and more metro stations have adopted passenger flow control measures to ensure operational security or implemented skip-stop patterns to ease transportation pressure. This paper investigates the collaborative optimization problem of timetable scheduling, passenger flow control, and skip-stop pattern on a metro line. A mixed integer nonlinear programming (MINLP) model is proposed to require a performance balance between service level and operation costs. The collaborative optimization model is constructed to optimize the train timetable by determining arrival and departure times with bounded dwell time and running time at intervals. Specifically, time-dependent passenger arrival rates are regarded as uncertain parameters here. The scenario-based robust optimization (SBRO) model with nonlinear constraints is further transformed into a model with linear constraints, which can be directly solved by commercial solvers. Finally, several sets of numerical experiments according to the actual data about the Beijing Changping metro line are performed to demonstrate the validity of the proposed model and method. The optimized train timetable integrated with the skip-stop pattern and passenger flow control strategy can effectively ease the congestion of stations and improve the safety and service quality of the metro system. The tradeoff between solution robustness and model robustness is obtained with critical practical insights.

A Systematic Literature Review: Optimization Timetable in Education to Support Work-Life Balance (WLB)

Scheduling academic staff timetables is crucial and necessary to avoid redundancy and clash of class between teacher and student timetables. A good timetable allows students and teachers to manage their time and support a good and healthy lifestyle. However, with the scheduling, academic staff timetable may use many procedures to get efficient results. Therefore, this paper provides a gap of study for existing work on Optimization Timetable to support Work-Life Balance (WLB) regarding their market commercial and research purposes. The methodology of this study was conducted using a Systematic Literature Review (SLR). Result: two findings investigate 1) relevant optimization timetable scheduling used and 2) the method for timetable optimization to support WLB. The strengths and weaknesses of the features and utilities behind each study are also presented to provide a further understanding of the gaps and weaknesses of each body of research. We conclude that these studies are still insufficient and require further evaluation and improvement.

An Efficient Train Timetable Scheduling Approach With Regenerative-Energy Supplementation Strategy Responding to Potential Power Interruptions

The timetable of a metro system is essential for trains running safely and efficiently. For the design of a timetable, the potential power interruptions of energy supplies from the substations to the trains should be borne in mind. In case potential power interruptions occur, the backup running plan should be completed responsively to accommodate the passenger and energy demands In this study, we propose an energy supplementation strategy utilizing regenerative energy from decelerating trains in the event of power interruptions and develop a tri-objective optimization model incorporating passenger travel time, potential power interruption, and energy consumption. Particularly, for calculating regenerative energy utilization, we suggest a many-to-many energy allocation mechanism between decelerating and accelerating trains based on the real-time energy demands and supplies. A heuristic algorithm is developed to obtain a regular and cyclic timetable for minimizing passenger travel time, the potential power interruptions, and energy consumption. The suggested model and algorithm are tested based on the smart-card data collected from a bidirectional metro line in Beijing (China). The results show that the suggested approach significantly improves energy efficiency, reduces passenger waiting time, and decreases power interruption risks, compared with the currently used scheduling method.

Intelligent university timetable scheduling system using sudoku grid with magic square

Examination timetabling is problem faced by many academic educational institutions. The situation formulated as artificial intelligence problems as Sudoku grid with Magic square. Problem assigning a set of examinations to a fixed number of examination periods so that no student is required to take more than one examination at a time. The research describes two proposed heuristic algorithms to create university examination timetabling and avoiding the overlapping of exams. We proposed two algorithms, the first algorithm is based on Sudoku grid, a number of hard constraints as well as a number of soft constraints are proposed. We assigned exams as 9 to period as 27 slot times and to 3 rooms, with 3 classes. Each student in each class takes 9 exams While, the second algorithm simulates the classic manual methods while observing the hard and soft constraints of the problem. The heuristic approach used to further optimize the result to include hard constraints with soft. The proposed methodology for creating the Exam timetabling problem has been implemented in Matlab and closing the gap between classical methods and intelligent methods. The results obtained with very less computational effort through two algorithms.

Numerical Simulation Design of Improved Meta Heuristic Charging Scheduling for Electrical Vehicle Applications

The driving range of an electric car is restricted. There aren't many charging stations in India, so those that exist must be efficient. This study's purpose is to develop a set of criteria for selecting successful algorithms for scheduling EV charging in photovoltaic micro grids Due to the similarities between EV charging scheduling and timetabling scheduling, research works on other fields' scheduling were thoroughly analyzed. This involves scheduling issues. The paper also reviews scheduling constraints, particularly for solar-powered electric vehicles. Charging electric vehicles on smart micro grids using PV is encouraged. Electric automobiles (EVs) are gaining worldwide favour over conventional cars. However, the higher purchase price of an EV may still be the main market obstacle. Customers choose EVs for various reasons, including lower carbon emissions and higher performance. Consumers with environmental awareness and a renewable energy perspective are needed. A recent study found that a 1% increase in renewable energy sources increases EV demand by 2-6%. It is acknowledged that EVs provide new potential for control and consumption flexibility by adjusting the charging power at a given moment. Particle swarm optimization for grid charging electric vehicles Electric vehicles must be recharged after a distance. Because electric vehicles are a viable alternative to internal combustion engines, the technology has grown rapidly. Electric vehicles have fewer emissions, better energy efficiency, less noise pollution, and cheaper operating and maintenance expenses. We plan to use this research to identify efficient algorithms for charging electric vehicles (EVs) in photovoltaic microgrids. The similarities between scheduling timetabling and EV charging prompted a review of literature on scheduling in many domains, particularly timetabling.

Integrated Framework for Bus Timetabling and Scheduling in Multi-Line Operation Mode

Bus service is of great significance to urban residents. With the convergence of bus lines and the formation of bus hubs, a multi-line operation mode which can realize the centralized management of vehicles is applied to the daily bus service planning. To solve the bus service planning problem systematically in the multi-line operation mode, we propose an integrated framework for bus timetabling (TT) and vehicle scheduling (VS), which are the two fundamental processes of bus service planning. Firstly, the determination processes of TT and VS are correlated by constructing the multiple vehicles’ trip-link chains with departure time information to facilitate simultaneous optimization. Secondly, a multi-objective optimization model is constructed, which considers higher service quality and lower operating costs as objectives. Logic and operational rules are also considered as constraints to ensure the implementation of the solutions. Thirdly, we propose and implement a heuristic solution algorithm based on neighborhood search to achieve high-performance solutions. Finally, we validate the efficiency and effectiveness of our framework under the actual bus operation scenario in Chongqing, China. The Pareto frontier solutions are provided to bus operators as alternative operation schemes.