This research proposes a study on AI-assisted optimized Job-Shop Production System (JPS) to improve performance, reduce cost and time, and detect machine failure. In the current industrial landscape, optimizing Job Shop Production Systems (JPS) is critical for achieving higher efficiency, reduced costs, and improved resource utilization. This study presents a comprehensive review of both conventional and artificial intelligence (AI)/machine learning (ML)-based approaches applied to JPS, with particular emphasis on job sequencing and machine failure prediction. The system is modeled as a multi-stage job-shop with diverse tasks and constraints, highlighting challenges in sequencing, resource allocation, and machine reliability. This work addresses job-shop scheduling in a T-shirt manufacturing system using Grey Wolf Optimization (GWO). While conventional GWO provides feasible job sequencing, it suffers from premature convergence. An Improved Grey Wolf Optimization (IGWO) algorithm is therefore proposed to enhance exploration and convergence efficiency. Simulation results show that IGWO achieves better job sequencing with reduced make-span, lower production cost, and improved resource utilization compared to standard GWO.
Intrusion Detection Systems (IDSs) play a vital role in safeguarding modern network infrastructures against increasingly sophisticated cyber threats. However, the high dimensionality of network traffic data and the presence of imbalanced attack classes often limit the effectiveness of conventional Machine Learning (ML) approaches. This study proposes a feature-driven Intrusion Detection (ID) framework that combines XGBoost-based feature selection with multiple ML classifiers to improve attack detection performance while reducing computational complexity. The NSL-KDD dataset is utilized to evaluate the proposed approach across five traffic classes: Benign, Denial of Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). XGBoost feature ranking is employed to identify thirteen highly relevant features for each attack category, thereby reducing data dimensionality and eliminating redundant attributes. The selected features are subsequently evaluated using six ML classifiers, namely LightGBM, Voting Classifier, CatBoost, Multi-Layer Perceptron (MLP), AdaBoost, and Stochastic Gradient Descent (SGD). Performance assessment is conducted using Precision, Recall, F1-Score, confusion matrices, and cross-validation analysis. Experimental results demonstrate that ensemble-based models, particularly CatBoost and LightGBM, achieve superior performance for majority attack classes such as DoS and Probe, while all classifiers exhibit challenges in detecting minority classes such as R2L and U2R due to severe class imbalance. Cross-validation results confirm the robustness and stability of the selected feature subsets across different attack categories. Furthermore, a computational complexity analysis highlights the suitability of the proposed framework for practical and resource-constrained ID environments. The findings emphasize the effectiveness of feature optimization in enhancing classification performance and provide valuable insights for the development of efficient and scalable IDS solutions.
Aman Jyoti, Maninder Singh, V. Banga et al.· Scientific Reports· 0 citations
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