Article
Automation & Control Systems
Fuqing Zhao, Zesong Xu, Ling Wang, Ningning Zhu, Tianpeng Xu, J. Jonrinaldi
Summary: This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP) and proposes a population-based iterated greedy algorithm (PBIGA) to address the problem. The PBIGA is shown to be effective and outperforms state-of-the-art algorithms in terms of minimizing total flowtime. Experimental results on large-scale benchmark instances demonstrate the superiority of the proposed PBIGA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Chao Lu, Liang Gao, Jin Yi, Xinyu Li
Summary: This study introduces a novel energy-efficient scheduling approach for distributed flow shop machining, using a hybrid multiobjective optimization algorithm that outperformed other state-of-the-art algorithms in real-world experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Kuljeet Kaur, Sahil Garg, Georges Kaddoum, Neeraj Kumar
Summary: This article proposes an energy-aware and SLA-driven job scheduling framework based on MapReduce, aimed at exploring the task-to-slot/container mapping problem. By dividing it into three major subproblems and using heuristics along with classical algorithms, energy efficiency is enhanced and energy consumption is reduced.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2021)
Article
Automation & Control Systems
ZiYan Zhao, MengChu Zhou, ShiXin Liu
Summary: Iterated greedy algorithm (IGA), developed in 2007, is widely used for flow-shop scheduling problems (FSPs) in production scheduling. Various FSPs have been solved using IGA-based methods, including basic IGA, variants, and hybrid algorithms. Over 100 articles related to IGA and FSPs have been published, highlighting the significance and potential of this algorithm in optimization.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Asma M. Altabeeb, Abdulqader M. Mohsen, Laith Abualigah, Abdullatif Ghallab
Summary: The study introduces a cooperative hybrid firefly algorithm to solve the capacitated vehicle routing problem (CVRP), which utilizes multiple firefly algorithm populations to collaborate, hybridizes with local search and genetic operators, and exchanges solutions among populations through communication, the results of experiments demonstrate the algorithm's outstanding performance compared to other methods.
APPLIED SOFT COMPUTING
(2021)
Article
Energy & Fuels
Chaoliang Wang, Peiran Ge, Liang Sun, Fuwang Wang
Summary: This study aims to establish a demand response model for residential flexible load to minimize electricity cost and reduce grid load variance in the context of increasing flexible load represented by smart household appliances. The study classifies the flexible loads of residents and establishes demand response models for different load demand response modes. To make full use of residential electricity data, a user-side flexible load multi-objective optimization scheduling model is developed, considering minimizing electricity cost and power grid load variance as the objective function. The results show that the model is effective and feasible.
Article
Computer Science, Artificial Intelligence
Kuo-Ching Ying, Shih-Wei Lin
Summary: This study addresses a two-stage assembly additive manufacturing scheduling problem, where multiple parts are produced in job batches using identical parallel AM machines in the first stage and then assembled into the desired products in the second stage. A mixed-integer linear programming model and an innovative reinforcement learning metaheuristic called the iterated epsilon-greedy algorithm are proposed to minimize the makespan of this significant scheduling extension. The computational results based on 810 test instances demonstrate that the developed approaches are highly effective, efficient, and robust in solving the addressed problem. Notably, the research results effectively bridge the gap between theory and practice in AM production planning by integrating the production stage with the assembly stage.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Industrial
Xueyan Sun, Weiming Shen, Birgit Vogel-Heuser
Summary: This paper addresses the distributed hybrid blocking flowshop scheduling problem with makespan criterion and proposes a hybrid genetic algorithm for solving it. Experimental results show that the proposed algorithm performs well on benchmarks and the local search method has a strong search capability.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuhang Wang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao, Yiping Liu
Summary: The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Three issues of DFGSP, including assigning groups to factories, arranging group sequences in each factory, and scheduling job sequences in each group, need to be solved due to its strong coupling. To solve these problems, a mixed-integer linear programming model is constructed and verified, and two rapid evaluation methods are designed based on group insertion and job insertion. An effective two-stage iterated greedy algorithm (tIGA) is proposed, which includes cooperative neighborhood search strategies and enhanced search strategies to improve the search breadth and depth. Experimental results show that the proposed algorithm outperforms other algorithms in terms of objective values and demonstrates the effectiveness of the proposed tIGA.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Wen-qiang Zou, Quan-ke Pan, Lei-lei Meng, Hong-yan Sang, Yu-yan Han, Jun-qing Li
Summary: This paper investigates the scheduling problem of multi-AGVs with charging and maintenance (MAGVSCM) in a matrix manufacturing workshop. It proposes a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm to reduce the total cost. The experimental results show that the proposed algorithm significantly outperforms existing algorithms in solving the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Adil Yousif, Monika Shohdy, Alzubair Hassan, Awad Ali
Summary: This paper introduces an enhanced job scheduling mechanism using cat swarm optimization to minimize the execution time of jobs in IoT cloud computing. Through experimental evaluation, it was found that the proposed mechanism outperforms the firefly algorithm and glowworm swarm optimization in reducing execution time.
Article
Engineering, Multidisciplinary
Erbao Xu, Yan Li, Yong Liu, Jingyi Du, Xinqin Gao
Summary: This study addresses the energy-saving scheduling issue in job shops of order-oriented manufacturing enterprises, proposing an improved Firefly Algorithm to tackle the discontinuous domain problem. By redesigning the coding structure and updating operations, the algorithm shows effectiveness in reducing energy consumption costs.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Automation & Control Systems
Yuhuai Peng, Alireza Jolfaei, Keping Yu
Summary: This article proposes a real-time deterministic scheduling scheme for slot scheduling and data transmission (SSDT) in microgrids. The scheme utilizes two heuristic algorithms to achieve multi-channel time slot allocation, resulting in significant advantages compared to traditional schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Theory & Methods
Yuanhao Yang, Hong Shen
Summary: In this study, we address the critical problem of task allocation in a large cloud data center HPC system and propose a novel deep reinforcement learning enhanced greedy optimization algorithm. By predicting the best allocation sequence using DRL and allocating tasks to servers using a greedy strategy, our algorithm improves system gain while guaranteeing performance.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yu-Cheng Wang, Toly Chen
Summary: This study reviews existing XAI techniques for explaining GA applications in job scheduling and proposes several novel XAI techniques to address existing problems. The proposed methodology is able to handle high-dimensional data and visualize the contribution of feasible solutions, satisfying the requirements for an effective XAI technique in explaining GA applications in job scheduling. Additionally, the methodology can be extended to explain other evolutionary AI applications in job scheduling.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Rafik Hamza, Alzubair Hassan, Awad Ali, Mohammed Bakri Bashir, Samar M. Alqhtani, Tawfeeg Mohmmed Tawfeeg, Adil Yousif
Summary: Privacy-preserving techniques enable the use of private information without compromising privacy. Homomorphic encryption algorithms provide solutions for performing computations on encrypted data while preserving privacy. This paper provides a comprehensive overview of homomorphic encryption tools for Big Data analysis, discussing their applications and a security framework. The paper also highlights the limitations and tradeoffs of these algorithms, and compares popular homomorphic encryption tools.
Article
Chemistry, Analytical
Awad Ali, Mohammed Bakri Bashir, Alzubair Hassan, Rafik Hamza, Samar M. Alqhtani, Tawfeeg Mohmmed Tawfeeg, Adil Yousif
Summary: Software reliability is crucial, and reliable prediction models are important in preventing software failures. However, current models face scalability and concurrent application modeling issues. We propose a scalability-enhanced reliability prediction model and evaluate it using sensor-based case studies.
Article
Thermodynamics
Muhammad Irfan, Faisal Althobiani, Abdullah Saeed Alwadie, Maryam Zaffar, Ali Abbass, Adam Glowacz, Saleh Mohammed Ghonaim, Hesham Abdushkour, Saifur Rahman, Omar Alshorman, Mohammad Kamal Asif Khan, Samar Alqhtani, Fahad Salem Alkahtani
Summary: Bearing faults are a major cause of centrifugal pump failures. Limited literature exists on diagnosing minor scratches on bearing surfaces through non-intrusive monitoring techniques. Recent research has shown promising results in analyzing bearing scratches using machine learning and convolutional neural networks (CNNs). However, the reported fault classification accuracy is low due to factors such as low harmonic amplitudes, environmental noise, and conventional feature extraction techniques. This paper addresses these challenges by developing a novel feature extractor (NFE) that extracts powerful features from integrated current and voltage sensor data, achieving significantly improved classification accuracy compared to previous methods.
ADVANCES IN MECHANICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Samar M. Alqhtani
Summary: Disasters caused by natural events pose significant threats to human life and property. The influence of fake image posting on social media regarding natural disasters is increasing. Existing machine learning algorithms fail to identify fake labeling on disaster images and struggle to handle the classification process during multiple disaster events. To address this issue, a multi-model convolutional neural network (MMCNN) is proposed to accurately detect fake label images in multi-phormic natural disaster events.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Chemistry, Analytical
Ghulam Ali, Tariq Ali, Insha Ul Hassan, Ahmad Shaf, Muhammad Irfan, Grzegorz Nowakowski, Kazimierz Kielkowicz, Adam Glowacz, Samar M. Alqhtani
Summary: This paper introduces a new routing algorithm, ESEDG, for data collection in autonomous underwater vehicles (AUVs). By designing an elliptical trajectory and using an end-to-end delay model, ESEDG achieves efficient data gathering from member nodes to gateway nodes and then to a sink node. Experimental results show that ESEDG outperforms baseline routing protocols in terms of network throughput, delay, and energy consumption.
Article
Chemistry, Multidisciplinary
Yassir Edrees Almalki, Nisar Ahmed Jandan, Toufique Ahmed Soomro, Ahmed Ali, Pardeep Kumar, Muhammad Irfan, Muhammad Usman Keerio, Saifur Rahman, Ali Alqahtani, Samar M. Alqhtani, Mohammed Awaji M. Hakami, Alqahtani S. Saeed, Waleed A. Aldhabaan, Abdulrahman Samir Khairallah
Summary: This research focuses on the analysis of retinal fundus and MRI images for ocular and cerebral abnormalities. An iterative algorithm based on the McCann Retinex algorithm is proposed to enhance the images. The results show that the proposed method successfully enhances the images and has a positive impact on the diagnosis of eye diseases and brain tumors.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Muhammad Irfan, Nasir Ayub, Qazi Arbab Ahmed, Saifur Rahman, Muhammad Salman Bashir, Grzegorz Nowakowski, Samar M. M. Alqhtani, Marek Sieja
Summary: Sentiment analysis is being studied in the field of text mining to computationally handle a text's views, emotions, subjectivity, and subjective nature. The researchers developed a novel method called ResNet-SCSO for extracting expressions from textual information and addressing the problem of removing emotional aspects through multi-labeling. By analyzing five distinct datasets and using various techniques such as preprocessing, GloVe and TF-IDF feature extraction, and word association using word2vec, the accuracy of ResNet-SCSO was tested and found to outperform other commonly used techniques.
Article
Medicine, General & Internal
Abdullah A. A. Asiri, Ahmad Shaf, Tariq Ali, Unza Shakeel, Muhammad Irfan, Khlood M. M. Mehdar, Hanan Talal Halawani, Ali H. H. Alghamdi, Abdullah Fahad A. Alshamrani, Samar M. M. Alqhtani
Summary: A brain tumor is a significant health concern. Traditional techniques such as MRI and CT scans have limitations, but computer-aided analysis of brain images offers a promising approach for accurate and reliable detection. A fine-tuned vision transformer model achieved an accuracy of 98.13% in identifying brain tumors, reducing the workload of radiologists and providing a practical approach in medical science.
Article
Biology
Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani, Khlood M. Mehdar, Hanan Talal Halawani, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani, Samar M. Alqhtani
Summary: Nowadays, brain tumors are a leading cause of death globally. Detection and classification of brain tumors accurately at early stages is challenging due to their varying structure. This research proposes an improved model using CNN with ResNet50 and U-Net to detect and segment brain tumors. The model achieves high performance metrics, outperforming other models.
Article
Computer Science, Information Systems
Reyazur Rashid Irshad, Shahid Hussain, Ihtisham Hussain, Ahmed Abdu Alattab, Adil Yousif, Omar Ali Saleh Alsaiari, Elshareef Ibrahim Idrees Ibrahim
Summary: Early diagnosis of diseases is crucial for timely treatment and reducing healthcare costs. Predictive analytics combined with Artificial Intelligence provide an effective approach for early diagnosis and intelligent decision-making in the healthcare system. The proposed ASM-RF hybrid framework shows superior performance in disease diagnosis and execution time compared to conventional methods.
Article
Computer Science, Information Systems
Reyazur Rashid Irshad, Shahid Hussain, Ihtisham Hussain, Ibrar Ahmad, Adil Yousif, Ibrahim M. Alwayle, Ahmed Abdu Alattab, Khaled M. Alalayah, John G. Breslin, Mohammed Mehdi Badr, Joel J. P. C. Rodrigues
Summary: The Internet of Things (IoT) based smart city applications aim to enhance the quality of life for urban residents by collecting and analyzing data to create more sustainable, efficient, and connected communities. However, the security threats posed by a large number of networked devices can have serious consequences for city safety, well-being, and economic development. Therefore, an advanced approach is needed to provide secure data transmission and energy resources for sustainable cities.
Article
Computer Science, Information Systems
Alzubair Hassan, Rafik Hamza, Fagen Li, Awad Ali, Mohammed Bakri Bashir, Samar M. Alqhtani, Tawfeeg Mohmmed Tawfeeg, Adil Yousif
Summary: Mobile devices play a significant role in our daily lives, but they also bring security concerns when using internet applications. This paper proposes an efficient user authentication and key agreement protocol for heterogeneous client-server mobile environments. The proposed scheme is secure and suitable for applications using different cryptographic approaches.
Review
Computer Science, Information Systems
Tawfeeg Mohmmed Tawfeeg, Adil Yousif, Alzubair Hassan, Samar M. Alqhtani, Rafik Hamza, Mohammed Bakri Bashir, Awad Ali
Summary: This paper provides a systematic review of existing literature on the importance of dynamic load balancing and reactive fault tolerance techniques in cloud security, as well as their integration methods, types, frameworks, and future directions.
