Search Results - ((scheduling algorithm) OR (((matching algorithm) OR (learning algorithm))))

Sports tournament scheduling using genetic algorithm / Hafeezur Syakir Abdul Motok@Mohd Ridzuan by Abdul Motok@Mohd Ridzuan, Hafeezur Syakir

“…The organizer of sports events often fronting problems such as the incorrect allocation of matches as well as tough to create a good and reliable schedule. …”
Get full text
Get full text

Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling by Ullah, Wasif, Mohd Fadzil Faisae, Ab Rashid, Muhammad Ammar, Nik Mu’tasim

“…However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO) algorithm for CHFS. …”
Get full text
Get full text
Get full text

Automated Student Timetable Scheduling System based on Genetic Algorithm by Amirul Azuani, Romlee, Noor Ain, Rosly, Meng, Chuan Haw

“…These factors lead to several major problems identified during the enrollment period such as timetable clashing between students, continuous hours of lecture and difficulty to find a matching slot for the clashed courses. In order to solve these problems, automated timetable scheduling system based on genetic algorithm is proposed which is estimated to reduce the chances of class clashing and prevent continuous lecture time. …”
Get full text
Get full text
Get full text

Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm by Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

“…The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.…”
Get full text
Get full text
Get full text

Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

Subjects:

Hybrid dynamic scheduling model for flexible manufacturing system with machine availability and new job arrivals by Paslar, Shahla

“…The performance of the schedules as produced by the scheduling/rescheduling algorithms were investigated and compared. …”
Get full text
Get full text

Comparative study on job scheduling using priority rule and machine learning by Murad, Saydul Akbar, Zafril Rizal, M Azmi, Abu Jafar, Md Muzahid, Al-Imran, Md.

“…We’ve achieved better for SJF and a decent machine learning algorithm outcome as well.…”
Get full text
Get full text
Get full text
Get full text

Artificial Intelligence Matching Algorithm with UTP Final Year Project Students and Advisors Matching as Test Case by ABDULLAH, NUR LYDIA OH

Get full text
Get full text

Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
Get full text
Get full text
Get full text

A heuristic room matching algorithm in generating enhanced initial seed for the university course timetabling problem by Teoh, Chong Keat, Haron, Habibollah, Wibowo, Antoni, Ngadiman, Mohd Salihin

“…The University Course Timetabling Problem (UCTP) such as the curriculum-based course timetabling problem is both an NP-hard and NP-complete scheduling problem.The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution.The Curriculum-Based University Course Timetabling Problem (CB-UCTP) has a high conflict-density and searching for an improved solution is not trivial.In this study, the authors propose a heuristic room matching algorithm which improves the seed of the CB-UCTP.The objective is to provide a reasonable search point to carry out any improvement phase and the results obtained indicate that the matching algorithm is able to provide very promising results as the fitness score of the solution is significantly enhanced in a very short period of time.…”
Get full text
Get full text
Get full text

Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources by Abed, Munther Hameed, Mohd Nizam Mohmad, Kahar

“…Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. …”
Get full text
Get full text
Get full text

Stereo matching algorithm based on hybrid convolutional neural network and directional intensity difference by Hamid, Mohd Saad, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Kadmin, Ahmad Fauzan

“…Fundamentally, a stereo matching algorithm produces a disparity map or depth map. …”
Get full text
Get full text
Get full text

Enhancing project completion date prediction using a hybrid model: rule-based algorithm and machine learning algorithm by Abd Rahman, Mohd Shahrizan, Jamaludin, Nor Azliana Akmal, Zainol, Zuraini, Tengku Sembok, Tengku Mohd

“…The central purpose of this research is to significantly increase the predictability of these milestone dates, thereby eliminating the risks associated with high and dynamic fluctuations in schedules. The study employs a hybrid predictive model that combines Big Data technologies, Extract Load Transfer (ELT) processes, rule-based algorithms (RBA), machine learning (ML), and Power BI visualizations. …”
Get full text
Get full text
Get full text

ANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluation by Hannan M.A., Abdolrasol M.G., Mohamed R., Al-Shetwi A., Ker P., Begum R., Muttaqi K.

“…Cost effectiveness; Cost reduction; Electric power transmission networks; Learning algorithms; Neural networks; Renewable energy resources; Scheduling; Wind; Backtracking search algorithms; Charging/discharging; Correlation coefficient; Mean absolute error; Optimal scheduling; Optimization algorithms; Renewable energy source; Virtual power plants (VPP); Electric power system control…”

Survey on job scheduling mechanisms in grid environment by S. M., Argungu, Che Mohamed Arif, Ahmad Suki, Omar, Mohd Hasbullah

“…Grid systems provide geographically distributed resources for both computational intensive and data-intensive applications.These applications generate large data sets.However, the high latency imposed by the underlying technologies; upon which the grid system is built (such as the Internet and WWW), induced impediment in the effective access to such huge and widely distributed data.To minimize this impediment, jobs need to be scheduled across grid environments to achieve efficient data access.Scheduling multiple data requests submitted by grid users onto the grid environment is NP-hard.Thus, there is no best scheduling algorithm that cuts across all grids computing environments.Job scheduling is one of the key research area in grid computing.In the recent past many researchers have proposed different mechanisms to help scheduling of user jobs in grid systems.Some characteristic features of the grid components; such as machines types and nature of jobs at hand means that a choice needs to be made for an appropriate scheduling algorithm to march a given grid environment.The aim of scheduling is to achieve maximum possible system throughput and to match the application needs with the available computing resources.This paper is motivated by the need to explore the various job scheduling techniques alongside their area of implementation.The paper will systematically analyze the strengths and weaknesses of some selected approaches in the area of grid jobs scheduling.This helps researchers better understand the concept of scheduling, and can contribute in developing more efficient and practical scheduling algorithms.This will also benefit interested researchers to carry out further work in this dynamic research area.…”
Get full text
Get full text
Get full text

Development of job matching algorithm with collective learning by Cheng, Kam Ching

Get full text
Get full text

Bee foraging behaviour techniques for grid scheduling problem by Alyaseri, Sana, Ku-Mahamud, Ku Ruhana

“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.These resources are collected together to make a huge computing power.Job scheduling problem is one of the key issues in grid computing and failing to look into grid scheduling results in uncompleted view of the grid computing.Achieving optimized performance of grid system, and matching application requirements with available computing resources, are the objectives of grid job scheduling.Bee colony approaches are more adaptive to grid scheduling due to high heterogeneous and dynamic nature of resources and applications in grid.These algorithms have shown encouraging results in terms of time and cost.This paper presents some resent research activities inspired by bee foraging behavior for grid job scheduling especially ABC and BCO approaches.Different original studies related to this area are briefly described along with their comparisons against them and results.The review summary of their derived algorithms and research efforts is done.…”
Get full text
Get full text
Get full text

The development of integrated planning and scheduling framework for dynamic and reactive environment of complex manufacturing problem by Zakaria, Zalmiyah, Deris, Safaai, Mat Yatim, Safie, Othman, Muhamad Razib

“…Lastly, in Chapter 5, we investigate the problem of integrating new rush orders into the current schedule of a real world FMS. The aim is to introduce match up strategy with genetic algorithms (GA) that modify only part of the schedule in order to accommodate new arriving jobs.…”
Get full text
Get full text
Get full text

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm by Jaber M.M., Yussof S., Elameer A.S., Weng L.Y., Abd S.K., Nayyar A.

“…Automation; Complex networks; Computational complexity; Deep learning; Image analysis; Medical imaging; Pattern matching; Pixels; Distribution pattern-matching rule; Distribution patterns; Gray wolf-optimized deep convolution network; Gray wolves; Learning patterns; Matching rules; Medical fields; Medical image analysis; Pattern matching algorithms; Pattern-matching; Convolution…”

A new function of stereo matching algorithm based on hybrid convolutional neural network by Hamid, Mohd Saad, Kadmin, Ahmad Fauzan, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Abd Gani, Shamsul Fakhar, Herman, Adi Irwan

“…This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. …”
Get full text
Get full text
Get full text