DCNN-BIGRU: A Proficient Hybrid Classifier for Reliable Intrusion Detection and Prevention: Hybrid Approach

Advances in networking devices have revolutionized many industries by enabling intercommunication and automation in multiple areas, such as healthcare, transportation, and manufacturing. However, the threat of cyber-attacks has also escalated with the increased connectivity and dependency on these devices. Cyber security has become critical in protecting networks from malicious activities, ensuring the privacy and integrity of the data transmitted. Multiple deep-learning methods face multiple challenges in identifying intrusion threats; however, deep learning can self-enhance and scale up for reliability. We propose an efficient hybrid deep-learning intrusion-detection classifier, DCNN-BiGRU. The classifier has a simple architecture and works well in environments that do not require saving long-term dependencies and where computational resources are limited. It achieved a multiclass-classification accuracy of 99.70% on the training and test datasets

Jointly Optimizing Parallel Batch Processing Scheduling in A Semiconductor Manufacturing Environment

The rapid growth of the semiconductor industry has led to high water and energy consumption and substantial greenhouse gas emissions. Achieving sustainability in the semiconductor industry has become an exceedingly important issue. This paper investigates a complex batch processing scheduling problem in the final testing phase of semiconductor manufacturing, where chambers and chips are modeled as batch processing machines and jobs. Machines can process multiple jobs simultaneously, with each job defined by its processing time, release time, and size. A mixed-integer linear programming model is presented, along with a constructive-based metaheuristic, the ACS-PBPMs algorithm, to optimize batch formation and scheduling decisions jointly. The algorithm uses an effective candidate list strategy to address constraints and incorporates a local search phase based on solution characteristics. Experimental results on diverse problem instances show that the ACS-PBPMs algorithm outperforms CPLEX and competitive algorithms in computational efficiency and solution quality

Equity-Oriented Two-Echelon Vehicle Routing Problem: A Three-Phase Heuristic Algorithm

Fierce competition and the requirement for sustainable development compel catering services and urban logistics industries to balance cost-efficient transportation with improved service quality and customer equity. The two-echelon vehicle cooperation system, where a primary vehicle (truck) serves as a mobile base for a secondary vehicle (UAV), has gained attention for its potential to leverage the strengths of both vehicle types, enhancing operational efficiency and service delivery. This paper presents an equity-oriented two-echelon vehicle operation problem, where trucks and UAVs cooperate to provide equitable services. We model the problem as a mixed-integer linear program (MILP), incorporating equity considerations through a set of constraints. Specifically, we adopt the relative range scheme from the literature as an equity indicator, aiming to minimize the relative deviation between the maximum and minimum arrival times for unit demand across customers. To solve it, we propose a three-phase heuristic algorithm that dynamically adjusts equity constraints while minimizing transportation costs. Numerical experiments across various instance sizes show that the algorithm consistently produces high-quality solutions with optimality gaps of less than 10%

Impact of COVID-19 on The Bullwhip Effect Across U.S. Industries

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics

Reliability Optimization of Linear and Linear Consecutive K-Out-Of-N Systems Using Teaching-Learning-Based Optimization and Genetic Algorithm

Numerous engineering applications involve ensuring the proper functioning of systems, minimizing errors, and optimizing the system and its subcomponents. Achieving desirable outcomes often requires enhancing positive factors through optimization methods while mitigating negative factors. In this context, metaheuristic algorithms are favored to find solutions aligned with the intended objectives. Among such algorithms, Teaching-Learning Based Optimization (TLBO) and Genetic Algorithm (GA) stand out, drawing inspiration from real-life processes. This study focuses on applying the TLBO algorithm to optimize the reliability of linear k-out-of-n: F and G (lin/k/n: F and lin/k/n:G) and linear consecutive k-out-of-n: F and G (lin/con/k/n:F and lin/con/k/n:G) systems. Additionally, the system was analyzed using GA, and the results from both approaches were compared. By employing these powerful metaheuristic algorithms, we aim to attain effective and robust solutions for enhancing system reliability and performance. Also, this study can be a guide in terms of contributing to the reduction of costs by ensuring more efficient use of resources, especially in complex systems. It can also increase productivity by reducing labor by ensuring the efficient operation of machines and processes

A Novel Approach to Improve Inventory Management Process of Pharmaceutical Supply Chain

The pharmaceutical industry relies heavily on efficient inventory management to guarantee the expeditious supply of medications, minimize wastage, and sustain cost control. The dynamic demand for pharmaceuticals and severe regulatory standards present unique inventory management challenges that require systematic and strategic approaches. In Vietnam, a market undergoing accelerated development, the pharmaceutical company must also ensure that its supply chain and inventory administration are efficient. The ineffective operation of the pharmaceutical company’s facility is identified as a problem requiring improvement in this study. By employing statistical data analysis and root cause analysis techniques, the researchers ascertained that an overburdened inventory level is the underlying cause of the inefficiency in the warehouse. Hence, to relieve the pressure on the organization’s warehouse, this study suggests the implementation of a buffer warehouse through the utilization of an innovative integrated Lean Six Sigma approach—DMADV (Define, Measure, Analyze, Design, Verify)—and Multi-criteria decision-making (MCDM) methodology. The design process begins with identifying and analyzing stakeholder requirements and constraints. This is followed by creating Risk Priority Numbers (RPN) and Ishikawa diagrams, which serve as capability indicators for the buffer warehouse. Utilizing the Best-Worst Method (BWM) and the Evaluation Based on Distance from the Average Solution (EDAS) with a Z-number, the study develops a buffer warehouse supplier selection that is optimal for the organization’s supply chain. In conclusion, the research outlines a strategy and a collection of metrics for assessing the efficiency of the buffer warehouse operation. The application of DMADV for analyzing problem-solving is investigated, and a buffer warehouse for inventory is established. Furthermore, the research results provide metrics for buffer warehouse capacity, a methodology for choosing buffer warehouses, and a framework for assessing their efficacy. This results in approximately USD 25,523 in savings or 30 fewer late orders

Joint Optimization of Pricing and Inventory for Cross-Border E-Commerce Retail Export Supply Chains, Considering Export Tax Rebates and Stochastic Demand

This paper investigates a cross-border dual-channel supply chain involving domestic manufacturers and foreign retailers. The supply chain integrates wholesale and online sales channels managed by domestic manufacturers with traditional retail channels operated by foreign retailers. The Chinese government provides export tax rebates to manufacturers to stimulate international sales, while foreign import tariffs influence pricing and demand dynamics. Consumer purchasing behavior in traditional retail channels is influenced by inventory levels. Excess inventory often triggers intensified promotional efforts by sales staff. The interaction between domestic manufacturers and foreign retailers is modeled as a Stackelberg game, with random demand following a Poisson distribution. The findings reveal that, under certain conditions, an increase in the export tax rebate rate reduces wholesale, direct sales, and distribution prices, which in turn boosts order volumes from retailers. Similarly, higher retail import tariffs can lower wholesale and direct sales prices, although the distribution price varies based on specific factors, ultimately increasing retailer order volumes. In contrast, an increase in wholesale import tariffs results in lower prices across all channels and reduced retailer order volumes. Additionally, when consumer preference for online shopping increases, wholesale prices decrease, direct sales prices rise, distribution prices drop, and retailer order volumes decline. Numerical simulations provide strategic insights for optimizing profitability in cross-border dual-channel supply chains. These insights contribute to greater supply chain stability, alleviation of domestic overcapacity issues, and promotion of international cooperation for mutual benefits. This research offers both theoretical and practical guidance for multinational firms aiming to refine wholesale and inventory strategies in tariff-sensitive environments. It highlights pathways to enhance resilience and expand operations in global markets

Design of Vehicle Scheduling for Last-Mile Fresh-Food Delivery Using A Data-Driven Approach

The continuously growing demand for fresh food in China is accompanied by a significant increase in delivery volume, which requires timely and efficient vehicle scheduling. To find optimal and practical solutions, we studied a vehicle routing problem for last-mile fresh-food delivery that incorporates both actual traffic time and customer time windows. Actual traffic data were collected and analyzed to forecast future traffic times. A data-driven optimization approach was designed to integrate data prediction and decision optimization models. Specifically, the support vector regression model and adaptive large neighborhood searching algorithm were employed to solve the data prediction problem and search for optimal decision solutions, respectively. Numerical experiments suggest that the proposed data-driven approach is highly applicable to last-mile delivery problems with time sensitivity, and the solutions found are of favorable practicality. In addition, an in-depth analysis of the impact of different prediction accuracies on the performance of decision optimization was conducted, suggesting that an unnecessarily high data prediction accuracy may not improve the overall performance of last-mile delivery

State Monitoring of The Machining Process in Multi-Variety and Small-Batch Production Systems Based on Power Data

Multi-variety and small-batch production is prevalent in today's manufacturing industries, where identifying the operational state is crucial for achieving efficient and effective manufacturing. However, real-time and intrusive monitoring is challenging due to the nature of multi-variety and small-batch production compared to flowline production. In the context of machining systems, power data not only offers insights into energy consumption but also aids in controlling the production process. Taking into account the characteristics of multi-variety and small-batch manufacturing systems, along with the economic and technological viability of collecting power data, a novel state monitoring method based on power data for multi-variety and small-batch production is proposed. First, to bolster the sample size, power data from the machining process is decomposed using wavelet analysis to extract features across three distinct layers. Then a Dynamic Time Warping (DTW) based workpiece recognizer is established, which calculates the features distance between real-time power signal and predefined templates, thereby facilitating workpiece identification. Thereafter, Recurrence Quantification Analysis (RQA) is applied to the Cross Recurrence Plot (CRP) of the real-time power signals and their corresponding template workpiece powers. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then utilized to construct an anomaly detection model, which is fed by the outcomes of the RQA. The validity of this proposed methodology is confirmed through experimental validation. A case study demonstrates that the accuracy rates for workpiece recognition and anomaly detection are 98.40% and 98.8%, respectively. This method addresses the issue of limited sample size and provides an in-depth analysis of the input power in the machining system, making it suitable for state monitoring during the machining process within a multi-variety and small-batch production framework. It also has the potential to support dynamic state monitoring and energy optimization in practical machining system applications

A Reinforcement Learning and The Northern Goshawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

This paper introduces northern goshawk optimization, a novel global search strategy for the flexible job shop scheduling problem. It uses two-stage encoding and random-key-based encoding to transform individual position vectors into flexible job shop scheduling problem solutions. To improve local search, reinforcement learning is integrated, converting the flexible job shop scheduling problem into a Markov decision process with 10 states and 6 rules. A reward function based on optimal completion time guides the search. The proposed hybrid northern goshawk optimization-Q-learning-state-action-reward-state-action framework combines global and local search strengths. Experiments on standard datasets show the algorithm's superior performance, validating its effectiveness and practicality in solving the flexible job shop scheduling problem and real-world production scheduling problems