Timetable Scheduling Using a Hybrid Particle Swarm Optimization with Local Search Approach

Psarra, Evgenia; Apostolou, Dimitris

doi:10.1109/iisa.2019.8900723

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…[24], [25] presented an approach of coloring the edges of a two-part graph to solve the UCTTP problem, while [26] used graph coloring to schedule classes by assigning courses to a given number of time slots (colors). On the other hand, [27], [28], [29] proposed a genetic coloring approach to solve the UCTTP problem using a combination of graph coloring and genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Jarraya

2024

IJCDS

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Blended learning, an innovative educational approach adopted by the Saudi Electronic University, combines traditional face-to-face teaching with online methods to improve educational outcomes. However, implementing it effectively faces challenges, especially in shifting physical classes to virtual formats for faculty and students spread across distant campuses, potentially compromising the core principles of blended learning and reducing its effectiveness. Ideally, each student would attend one face-to-face and one virtual session per course weekly. Yet, operational challenges emerge due to geographical disparities between faculty and student locations, necessitating the conversion of face-to-face sessions into virtual ones. To address these challenges, this study proposes two novel heuristic strategies for scheduling timetables in blended learning contexts. The first heuristic, called Minimum Load Accumulation Heuristic (MLAH), aims to evenly distribute teaching loads among faculty members and time slots while maximizing the number of groups assigned to faculty members from the same campus. The second heuristic, called Average Load Accumulation Heuristic (ALAH), calculates the average load of all faculty members and time slots and reduces the number of iterations searching for the minimum load, as performed by MLAH. These strategies aim to minimize the conversion of face-to-face sessions to virtual, ensure fair distribution of teaching responsibilities, and maintain a balanced allocation of face-to-face and virtual classes throughout the academic year. The paper demonstrates the effectiveness of these algorithms in producing high-quality solutions comparable to those generated by CPLEX, with significantly reduced computational complexity.

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Section: Related Workmentioning

confidence: 99%

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Jarraya

2024

IJCDS

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“…Local search is one of the heuristic methods for combinatorial optimization problems in computer science and artificial intelligence. It has been demonstrated that local search is a simple but effective method for solving numerous computationally hard problems in computer science, mathematics, engineering, and bioinformatics, including the maximum satisfiability problem ( Gao, Li & Yin, 2017 ; Luo et al, 2017 , 2014 ), timetable scheduling ( Psarra & Apostolou, 2019 ), and clustering ( Tran et al, 2021 ; Levinkov, Kirillov & Andres, 2017 ). To solve complex optimization problems, local search algorithms with various search strategies have proven very effective in the literature.…”

Section: Proposed Local Search Algorithmmentioning

confidence: 99%

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities

Zhou

Liu

Gao

2023

PeerJ Computer Science

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Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network’s data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

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