Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization DOI Creative Commons

Isam Sadeq Rasham

Wasit Journal of Computer and Mathematics Science, Год журнала: 2024, Номер 3(4), С. 40 - 53

Опубликована: Дек. 30, 2024

Context: In the age of rapid e-commerce growth; Robotic Mobile Fulfillment Systems (RMFS) have become major trend in warehouse automation. These systems involve use self- governed mobile chares to collect shelves as well orders for deliveries with regard optimization task allocation and reduced expenses. However, a manner implement such systems, one needs find enhanced algorithms pertaining resource mapping planning movement robots sensitive environments. Problem Statement: Despite RMFS certain challenges especially when it comes distribution tasks overall distances that employees cover. Objective: The main goal this paper is propose new compound model based on RL-ACO optimize RMFS’s assignment navigation. Also, direction study investigate how methods can be applied real-life automation effective large scale. Methodology: This research introduces selection which integrates reinforcement learning Ant Colony Optimization (ACO). Specifically, real gym environment was created perform order training way robotic movement. Reinforcement Learning (RL) models were trained Proximal Policy (PPO) improving dynamic control ACO used computing optimal shelf trajectories. performance also measured by policy gradient loss, travelled distance time taken complete tasks. Results: proposed framework showed potential enhancing efficiency required travel involved. each RL shortest paths identified best route determined total 102.91 units. other values as, value function loss convergence iterations. To build global solution, integration went step forward enabling through combinatorial problems solving. Implications: offers practical, generalizable flexible approach improvement operations thinking

Язык: Английский

A Bibliometric Review of Trends and Insights of Internet of Things on Cybersecurity Issues DOI
Mushtaq Yousif Alhasnawi, Ahmed Abbas Jasim Al-Hchaimi, Yousif Raad Muhsen

и другие.

Studies in computational intelligence, Год журнала: 2025, Номер unknown, С. 127 - 147

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Leveraging machine learning for enhanced cybersecurity: an intrusion detection system DOI

Wurood Mahdi sahib,

Zainab Ali Abd Alhuseen,

Iman Dakhil Idan Saeedi

и другие.

Service Oriented Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 11, 2024

Язык: Английский

Процитировано

1
Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms DOI Creative Commons

Ali Alhchaimi

Wasit Journal of Computer and Mathematics Science, Год журнала: 2024, Номер 3(2), С. 19 - 31

Опубликована: Июнь 30, 2024

Context: Cloud-based services are increasingly central in financial technology, enabling scalable and efficient transactions. However, they also heighten vulnerability to fraud, challenging the security of online activities. Traditional fraud detection struggles against sophisticated tactics, highlighting need for advanced, cloud-compatible solutions. Objectives: This study assesses machine learning (ML) algorithms' ability detect cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), XGBoost (XGB). It uses a comprehensive dataset determine which ML model best identifies fraudulent transactions, aiming optimize these models accuracy, precision, efficiency real-time detection. Results: The outperformed others with showing high effectiveness. These were particularly good at balancing precision recall, minimizing false positives, accurately identifying complex transaction patterns. Conclusion: ML, especially ensemble boosting like Forest, offers strong approach detecting cloud-based systems. Their capacity handle vast data volumes adapt new patterns enhances security. Implication: provides guide implement emphasizes importance continual innovation tackle digital finance suggesting that adopting advanced can significantly reduce risks, ensuring secure, efficient, trustworthy platform users.

Язык: Английский

Процитировано

0
Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization DOI Creative Commons

Isam Sadeq Rasham

Wasit Journal of Computer and Mathematics Science, Год журнала: 2024, Номер 3(4), С. 40 - 53

Опубликована: Дек. 30, 2024

Context: In the age of rapid e-commerce growth; Robotic Mobile Fulfillment Systems (RMFS) have become major trend in warehouse automation. These systems involve use self- governed mobile chares to collect shelves as well orders for deliveries with regard optimization task allocation and reduced expenses. However, a manner implement such systems, one needs find enhanced algorithms pertaining resource mapping planning movement robots sensitive environments. Problem Statement: Despite RMFS certain challenges especially when it comes distribution tasks overall distances that employees cover. Objective: The main goal this paper is propose new compound model based on RL-ACO optimize RMFS’s assignment navigation. Also, direction study investigate how methods can be applied real-life automation effective large scale. Methodology: This research introduces selection which integrates reinforcement learning Ant Colony Optimization (ACO). Specifically, real gym environment was created perform order training way robotic movement. Reinforcement Learning (RL) models were trained Proximal Policy (PPO) improving dynamic control ACO used computing optimal shelf trajectories. performance also measured by policy gradient loss, travelled distance time taken complete tasks. Results: proposed framework showed potential enhancing efficiency required travel involved. each RL shortest paths identified best route determined total 102.91 units. other values as, value function loss convergence iterations. To build global solution, integration went step forward enabling through combinatorial problems solving. Implications: offers practical, generalizable flexible approach improvement operations thinking

Язык: Английский

Процитировано

0