International Journal of Industrial Engineering: Theory, Applications and Practice
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Integrating Objective Weighting and Performance Analysis for Solving Green Energy Management Challenges
Get PDFThe world is facing an ecological crisis, as carbon emissions from the global construction industry place a significant burden on the environment. Well-designed green energy management (GEM) can mitigate carbon emissions and promote sustainable environmental development. However, assessing green energy involves a complex process of multi-criteria decision-making (MCDM). Traditional approaches cannot accurately account for the objective weight of evaluation criteria, fail to consider the positive and negative ideal solutions of alternatives, and lack an integrated assessment from a multi-dimensional perspective. As a result, the evaluation of green energy remains incomplete. To address these limitations, this study integrates criteria importance through inter-criteria correlation (CRITIC) and the technique for order of preference by similarity to ideal solution (TOPSIS) within a two-dimensional matrix framework to provide managers with decision-making guidance for GEM. A case study on selecting green building insulation materials was conducted to compare the proposed approach with the simple additive weighting (SAW) approach and the TOPSIS approach. The calculation results demonstrate that the proposed approach more accurately evaluates alternative ranking through a visualized framework, offering a more reliable and comprehensive decision-making tool for GEM
Effects of Control Type and Panel Angle on Kitchen Appliance Usability: A Comparative Study of Knob and Touchscreen Panels
Get PDFThis study empirically examined whether panel angle effects on kitchen appliance control panel usability vary by control type. Two representative types—cooker knobs and touchscreen panels—were tested at four angles (0°, 30°, 60°, 90°), reflecting common configurations in commercial ranges. Usability was assessed through visibility, physical comfort, and preference. Twenty participants performed heat-level adjustment tasks at each angle and rated all measures. Results showed: (1) significant interaction effects between control type and panel angle across all criteria, and (2) substantial main effects of panel angle on evaluation scores. Knob panels achieved higher ratings at steeper angles, while touchscreen panels performed better at flatter angles. These findings provide ergonomic guidance for designing control interfaces that integrate both knobs and touchscreens. Proper panel angle design can enhance visual accessibility, reduce bodily strain, and help prevent musculoskeletal disorders during prolonged and repetitive use
Design and Optimization of Modular Fixture Using Finite Element Method for Cylindrical Parts
Get PDFTo ensure the production of high-quality parts, a precise fixture is essential in minimizing the workpiece displacement while maintaining positional accuracy, stability, and avoiding interference with the cutting tool. Workpiece deformation during machining operations can be curtailed by adequate selection, number, and position of locators/clamps. This work integrates advanced finite element method (FEM) simulations to accurately predict and optimize the performance of modular fixtures, improving precision, flexibility, and efficiency of the manufacturing process. An optimized fixture layout design methodology is proposed, enabling attainment of these machining objectives for quality work on solid and thin-walled cylindrical parts. In addition to geometric optimization, clamping forces are refined using the force-moment method, minimizing workpiece deformation and improving hold accuracy. The optimal layout is determined with minimal sample runs and implemented for drilling 14.5 mm diameter holes in Al-T3 pipes used in the HVAC systems of SANPAK Pvt. Ltd. The experimental results obtained after the modular fixture optimization through FEM were found to increase the accuracy of hole (rejection rate drop from 5% to 2%) and reduction in setup time and efficiency of process. A chi-square test confirmed the significance of this improvement (χ² = 20.05, p < 0.00001). The unique study integrates multiple parameters, such as clamping force, deformation, and stability, in a single framework, for a comprehensive and improved solution to the challenges of fixture design for cylindrical parts. This approach enhances the overall effectiveness, adaptability, and cost of modular fixture design and provides manufacturing solutions applicable to diverse industrial components
Optimal Control Strategies for Adaptive Pricing in Ride-Sharing Services
No full textRideshare platforms are an example of economies of sharing where ride requests initiated by riders are fulfilled by car owners through the platform that connects both of them. When demand for a ride is initiated by the customer, the platform checks service providers' (car owners) availability and assigns a fare (ride price) that both the ride requester and provider should agree on to complete the transaction, and the ride service is fulfilled. In this research, optimal pricing strategies for ride-share platforms are considered. The optimal control approach is used to first develop differential equations to model the dynamics of the number of ride requests and for the price rate. Second, we model the total profit as a function of a linear revenue and a nonlinear cost. The optimal rate of change in the ride price is then obtained. Finally, a numerical example and extensive sensitivity analyses not only provide insights into the effect of the system parameter on the model but also lead to managerial implications to help companies determine the best price for each ride
DCNN-BIGRU: A Proficient Hybrid Classifier for Reliable Intrusion Detection and Prevention: Hybrid Approach
No full textAdvances 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
Strategic Investment in BIST100: A Machine Learning Approach Using Symbolic Aggregate Approximation Clustering
Get PDFThis study employs the Symbolic Aggregate Approximation (SAX) clustering method to enhance investor decision-making on the Borsa Istanbul (BIST100) by identifying companies exhibiting analogous stock movements. The data from 81 BIST100 companies over a three-year period has been analyzed, with a focus on risk minimization and strategic investment. The SAX method, integrated with a dendrogram, categorizes stocks into sector-based and non-sector-based clusters, providing insights for portfolio optimization. The results demonstrate the effectiveness of the method in identifying relevant stock patterns across sectors, aiding in more informed investment decisions. This approach highlights the need for considering multiple factors in investment strategies, offering a new perspective on stock market analysis with advanced clustering techniques
The Development of Artificial Intelligence-Based Optimal Route Selection Framework for Rescue Services Process Management
Get PDFAn increase in urban traffic congestion has emerged as a critical bottleneck in the operational efficiency of emergency response systems, leading to substantial delays in rescue service deployment and a measurable increase in roadway mortality rates. Unplanned blockage placements by law enforcement agencies further disrupt traffic flow, elevating the vehicular density and impeding emergency response times. This study presents a data-driven framework that forecasts optimal blockage points and predicts congestion on alternative routes using a combination of operational research strategies and AI-based traffic modeling. The novelty of this work lies in leveraging AI-driven techniques to optimize blockage placement while minimizing disruptions near healthcare and public safety services. The framework employs supervised machine learning models to classify traffic flow (non-congested: 0, congested: 1) based on feature vectors linked to healthcare accessibility, achieving a 99% F1 score on both validation data and real-time traffic monitoring. Additionally, the A-star algorithm is utilized to determine the most efficient alternative routes post-blockage. To enhance practical usability, the framework is integrated into a Graphical User Interface (GUI) application capable of predicting congestion at specific time intervals throughout the day. This system serves as a decision-support tool for local agencies, aiding in strategic traffic planning and ensuring uninterrupted access to critical healthcare services. By mitigating congestion near essential service areas, the proposed approach enhances emergency response efficiency and contributes to overall public safety
Enhancing Resilience in Electricity Supply Chains: A SCOR Model Approach for Vertically Integrated Utilities
Get PDFThis study introduces an innovative application of the Supply Chain Operations Reference (SCOR) model in a vertically integrated electricity supply chain. In this context, utilities manage all activities from primary energy procurement, generation, and transmission to distribution, which poses unique challenges due to process interdependence. The framework's comprehensive approach—covering plan, source, make, deliver, return, and enable—serves as a diagnostic and benchmarking tool that enhances supply chain resilience, reliability, responsiveness, flexibility, cost efficiency, and asset management. By aligning standardized supply chain methodologies with the specific requirements of the electricity industry, this study effectively identifies potential bottlenecks and risks across the integrated supply chain. This application differentiates this study from the existing literature and provides new actionable insights for sustainable supply chain management practices in the electricity sector
A Genetic Algorithm for Collaborative Truck-Drone Routing and Scheduling Problem in Surveillance Operations
No full textDrones can access areas that are difficult to reach for ground surveillance resources. However, drones have limited surveillance operations over large areas because of their short flight durations. To tackle the limitations of drones, one viable approach to use trucks as mobile platforms for the takeoff and landing of drones, ensuring close proximity to surveillance areas. However, coordinating the trucks and the drones is challenging due to the combinatorial complexity of scheduling their surveillance routes collaboratively. Motivated by this challenge, this study develops a genetic algorithm to solve the truck-drone routing and scheduling problem for surveillance. This algorithm determines the routes and schedules of multiple trucks and drones to monitor a given set of surveillance areas, aiming to minimize the time spent completing all surveillance operations. A set of numerical experiments is performed to validate the performance of the algorithm and discuss the managerial implications of collaborative surveillance
Design of Vehicle Scheduling for Last-Mile Fresh-Food Delivery Using A Data-Driven Approach
No full textThe 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