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Textile and Leather Review 2024年 7卷 1252-1272页

作者： Kaka, Shuaib Maryam, Muttahira Wassan, Rano Khan Sarwar, Umair Akbar, Ali Ansari, Jansher Department of Industrial Engineering and Management Dawood University of Engineering and Technology Pakistan

Globally, the textile industry plays a significant role in production but faces many challenges that affect its operation and sustainability. Product quality remains an essential concern despite technological advancements and quality control measures. Raw materials, production methods, and skills differ, resulting in customer dissatisfaction and damage to reputation. A quality management strategy is necessary to overcome these problems and promote quality improvement. This includes the implementation of the Lean Six Sigma approach. This article focuses on improving textile quality by developing the Six Sigma DMAIC method. An analysis of three months' data from a textile company shows that an increase in the Outgoing Quality Level (OQL) from 3% to 6% resulted in significant economic losses, amounting to PKR-240,000 due to rework, residual costs, customer returns, and lost sales time. By utilizing Six Sigma tools, the company reduced OQL from 6% to 4%, thus lowering product losses and increasing operational efficiency. As a result of these findings, the textile industry can improve quality and profitability through a Lean Six Sigma approach. © 2024, idd3. All rights reserved.

关键词： DMAIC lean manufacturing outgoing quality level Six Sigma

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System dynamics costing model for the capital cost estimation of electric vehicle batteries refurbishing facility

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International Journal of industrial and Systems engineering 2024年第3期48卷 377-404页

作者： Kalwar, Muhammad Ahmed Wassan, Asif Nawaz Sheikh, Waleed Ahmed Khan, Muhammad Ali Mari, Hussain Bux Industrial Engineering and Management Faculty of Mechanical Process and Earth Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan Department of Mechanical and Energy Systems Engineering University of Bradford United Kingdom Khairpur Mirs Sindh Pakistan

Electric vehicles (EVs) are considered sustainable vehicles due to low carbon emissions. Lithium-ion batteries (LIBs) are expensive electrical vehicle batteries (EVBs) due to their expensive rare raw materials, i.e., lithium, nickel and cobalt. With increased production of EVs, authors warned of the expected huge quantity of dismantled EVBs shortly and so explored their reusability. An in-depth review is conducted, and system dynamics (SD) model was developed in Anylogic whereas origin was used for graphs. The capital costs dynamics of the refurbishing facility were evaluated and the model was validated and initialised. The analysis was conducted in three sales scenarios:1) locally;2) regionally;3) nationally. This paper provides a framework for entrepreneurs. The results indicated scenario-2 as more suitable with a maximum return on investment (ROI) (80.26%) and minimum payback period (626 weeks). Therefore, it is concluded that it would be feasible for the refurbishing facility to operate regionally rather than locally/nationally. © 2024 Inderscience Enterprises Ltd.

关键词： batteries capital cost carbon carbon emissions electric vehicle batteries electric vehicles environment EVs infrastructural cost LIBs lithium-ion batteries recycling refurbished EVBs refurbishment retired EVs sustainability system dynamics

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A Novel Rolling-Window and Production-Maintenance-Based Machine Learning Approach to Virtual Metrology in Semiconductor Manufacturing

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15713-15727页

作者： Fan, Shu-Kai S. Zheng, An-Ting National Taipei University of Technology Department of Industrial Engineering and Management Taipei106344 Taiwan Yuan Ze University Department of Industrial Engineering and Management Taoyuan320315 Taiwan

Virtual metrology enables semiconductor manufacturers to continuously monitor and predict the quality and performance of semiconductor production during the manufacturing process. This kind of real-time monitoring tool is essentially important for maintaining process stability and ensuring consistent product quality. In this paper, a new rolling-window and production-maintenance-based approach is proposed on a weekly basis to establish the virtual metrology system. The proposed thickening and dewatering framework serves as the central core of the virtual metrology model, enabling real-time information and feedback to aid in making timely adjustments to the processing tool within machinery limitations. A meticulous approach to addressing each component within this innovative framework has been instrumental in achieving exceptional VM performance. The training dataset is routinely updated as the production maintenance is taking place to facilitate model retraining. A practical challenge to the domain engineer under certain circumstances where the process parameter considered in the virtual metrology model is realistically run out of specification is also rigorously investigated in this research. For the evaluation purposes, a real-world industrial case of the chemical vapor deposition process in semiconductor manufacturing is presented to illustrate the proposed approach. The proposed virtual metrology approach shows a substantial improvement in mean absolute error with 39.99%, 26.26%, and 30.75% for three different recipes, respectively, as compared to the base model currently implemented on the operation site. © 2004-2012 IEEE.

关键词： Deposition

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Assess and Investigate the Current Techniques, Challenges and Material Innovations in the Design and Fabrication of prosthetic and Orthotic (P&O) Devices

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Jordan Journal of Mechanical and industrial engineering 2025年第2期19卷 371-386页

作者： Wassan, Asif Nawaz Jumani, Muhammad Saleh Kalwar, Muhammad Ahmed Department of Industrial Engineering and Management Mehran University of Engineering and Technology Jamshoro76062 Pakistan Department of Industrial Engineering and Management Mehran University of Engineering and Technology Jamshoro76062 Pakistan Department of Industrial Engineering and Management Mehran University of Engineering and Technology Jamshoro76062 Pakistan

Additive manufacturing (AM), often known as 3D printing, has emerged as a transformative technology with applications in a variety of industries, including the medical field. Traditional manufacturing techniques are labor-intensive, time consuming and material-wasting. Additive manufacturing has the potential to solve these problems by using digital technologies. Recently, the field of Prosthetics and Orthotics (P&O) has witnessed significant developments due to digital technologies. The aim of this review paper is to investigate and evaluate the existing technologies, materials and challenges in the design and manufacturing of prosthetic and orthotic devices. This study also offers a comprehensive review of technologies, processes, barriers, limitations and the future of additive manufacturing in the prosthetic and orthotic field. This study highlights the main barriers to additive manufacturing including high costs, post-processing, large-scale production, slow speed, training and skills, material limitations and standard compliance. The study also identified the advanced materials such as nanomaterials, biomaterials and ceramics that are sustainable and recyclable materials on which researchers and organizations are focusing. Additionally, multi-material printing, recyclable materials, automation, smart materials, 4D printing and high speed are the latest trends in additive manufacturing. Furthermore, this study offers future recommendations, emphasizing the use of modern technologies such as additive manufacturing, biocompatible materials, and user-centered design techniques. This comprehensive analysis is a valuable tool for researchers, medical professionals, and stakeholders who are striving to improve the prosthetic and orthotic field. © 2025 Jordan Journal of Mechanical and industrial engineering. All rights reserved

关键词： Biocompatibility

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Utilizing Machine Learning for Long-Term Prediction of Electricity and Transport Fuel Demand in Pakistan 5th

Utilizing Machine Learning for Long-Term Prediction of Elect...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Harijan, Khanji Khatri, Shoaib Ahmed Kumar, Prem Kumar, Laveet Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Department of Electrical Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro Pakistan Department of Mechanical & Industrial Engineering College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9789819791118

Energy has become an indispensable commodity in modern society, serving as a vital resource for communities and diverse sectors of the economy. Its demand spans across multiple essential functions, including electricity generation, transportation, cooking, heating, water pumping, and industrial processing, highlighting its pivotal role in daily life. Consequently, the technologies driving energy production and consumption have garnered unprecedented attention. The selection of energy resources and technologies holds significant sway over both economic development and environmental sustainability. In this context, forecasting the demand for electricity and transport fuel assumes paramount importance. This study employs machine learning models to forecast the country's electricity and transport fuel demand under three distinct scenarios. The electricity demand forecast indicates a projected increase in demand averaging from 3 to 6% annually across the various scenarios considered throughout the outlook period. Similarly, the estimated forecast for transport fuel demand suggests a rise averaging from 6 to 11% annually over the study period. This research would help energy policy and decision makers and stack-holders in formulation of future layout of energy system of Pakistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electricity Forecasting Machine learning Pakistan Transport fuel

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Developing a stage-independent multiple sampling plan with loss-based capability index for lot disposition

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Journal of the Operational Research Society 2025年第3期76卷 426-437页

作者： Wu, Chien-Wei Darmawan, Armin Liu, Shih-Wen Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering Hasanuddin University Makassar Indonesia College of Management National Chin-Yi University of Technology Taichung Taiwan

Multiple sampling plan (MSP) is a generalization of single and double sampling plans that enhances the efficiency by reducing the number of sample items needed for inspection. However, the operating characteristic function of MSP is relatively complicated to derive because the inspection at each stage is dependent on others, i.e., the lot judgment on the current lot not only depends on its inspected result but also considers the results of the previous stages. Therefore, in this study, we propose a stage-independent MSP (SIMSP) and integrate it with a loss-based capability index. SIMSP can be regarded as a relaxed form of the traditional MSP, assuming independence in inspections at each stage. SIMSP’s parameters are solved for a cost-efficient purpose under an optimization model that aims to minimize the average sample number (ASN), adhering to constraints aligned with predefined quality and acceptable risk levels. The results indicate that SIMSP can not only perform better discriminatory power but also require fewer ASN for making lot disposition. Besides, SIMSP’s performance is evaluated, analyzed, and contrasted with the traditional sampling plan under various settings. In addition, an example is presented using the developed graphical user interface to illustrate the real-world application of SIMSP. © Operational Research Society 2024.

关键词： Quality assurance

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Dependency-based FMEA model for product risk analysis: a case study of a switch mode power supply

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International Journal on Interactive Design and Manufacturing 2024年第7期18卷 5225-5242页

作者： Liou, James J. H. Liu, Perry C. Y. Lo, Huai-Wei Department of Industrial Engineering and Management National Taipei University of Technology Taipei Taiwan College of Management National Taipei University of Technology Taipei Taiwan Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan

The robustness of components/products/systems is a concern for both the company that sells them and the consumers who use them. Failure Modes and Effect Analysis (FMEA) which has evolved over 60 years, is a powerful and effective risk identification tool for both design and manufacturing, often combined with Multiple Criteria Decision-Making methods to increase its utility. However, most previous FMEA studies have neglected the interdependence of the failure modes. To address this issue, this study develops a novel method, the TEchnique for Defining the Influential Relationship of Failure Modes, to determine the risk prioritization of the failure modes. The interaction among failure modes is effectively identified and incorporated into the model. The primary analysis procedure can be divided into three parts: (i) the assessment of the initial risk scores of the failure modes;(ii) the definition of the degree of interdependence among the failure modes;and (iii) the integration of the final risk scores based on dependency. The model is used to conduct a case study for a multinational manufacturer of switch mode power supplies as a demonstration case. The practicality of the proposed model was validated through model comparisons and expert feedback. The results show that "an unstable output voltage", "no power-good signal", "recession in efficiency", "burning out of components", and "the cooling fan does not operate" are the top five risky failure modes. The research findings can be used as the basis for improvement strategies in R&D design and by quality assurance departments to enhance product reliability. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024.

关键词： Risk assessment

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A novel instance density-based hybrid resampling for imbalanced classification problems

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Soft Computing 2025年第4期29卷 2031-2045页

作者： Park, You-Jin Ma, Chung-Kang Department of Industrial Engineering and Management National Taipei University of Technology Taipei10608 Taiwan

The class imbalance problem is one of the challenging issues in various machine learning applications. This problem occurs when the number of instances of a class is much smaller (or larger) than those of the other classes. To handle the imbalanced classification problems, many useful approaches have been developed, for example, synthetic minority oversampling technique (SMOTE). However, the SMOTE is often sensitive to the predetermined k value, i.e., the number of nearest neighbors used to generate the synthetic instances. For example, if the k value is moderately large, some of the synthetic instances generated by the SMOTE would be located near a decision boundary or even within the majority class area and thus these can be treated as unnecessary noisy instances. Thus, in this study, we propose an efficient hybrid resampling method based on instance density called IDHR (Instance Density-based Hybrid Resampling) to improve the classification performance by generating instances that are closer to the minority class than the majority class while avoiding generation of noisy instances. For this, we first apply the instance density-based oversampling (IDO) technique to generate new synthetic instances. And then, we eliminate some of the synthetic instances that are close to the decision boundary and determine the number of the synthetic instances among the retained synthetic ones which can be eliminated based on maximum of the distances from all the synthetic instances to the minority class instances and minimum of the distances from all the synthetic instances to the majority class instances as well as classification performances. To demonstrate the effectiveness of the proposed resampling method, comprehensive experiments are conducted on sixteen imbalanced datasets with considering three classifiers, i.e., C4.5 decision tree algorithm, support vector machine (SVM), and multi-layer perceptron neural network (MLP-NN). Through the experimental analysis, it is shown th

关键词： Multilayer neural networks

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A genetic-based approach for vehicle routing problem with fuzzy alpha-cut constraints

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Soft Computing 2025年第2期29卷 1169-1189页

作者： Kang, He-Yau Lee, Amy H. I. Department of Industrial Engineering and Management National Chin-Yi University of Technology Taichung Taiwan Department of Industrial Management Chung Hua University Hsinchu Taiwan

In today’s business environment, logistics and supply chain management are especially important for the timely delivery of materials and goods. Delivery must not only be fast, it also needs to be performed within a specific time frame. Therefore, this study examines a vehicle routing problem (VRP) that considers multiple goals and allows vehicles to reach their destinations within a time window with a crashed traveling time. Two objectives are considered, the minimization of total cost and the maximization of customer satisfaction. Firstly, a fuzzy multi-objective linear programming (FMOLP) model with alpha-cut technique and epsilon-constraint method is proposed to transform the multi-objective problem into a single-objective mathematical model, and then an improved genetic algorithm (IGA) is constructed to solve large-scale problems. The performances of the proposed methods are evaluated through several case studies. Design of experiments and sensitivity analysis are applied to evaluate the performance and robustness of IGA. The results show that both the FMOLP and IGA are effective, and the IGA can efficiently obtain a near-optimal solution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Sensitivity analysis

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Browsing History Data Analysis Based on a Modified Latent LSTM Allocation Model Considering Browsing Time and its Application to a Fresh Flower EC Site

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Journal of Japan industrial management Association 2024年第3期75卷 89-101页

作者： Zhang, Zhiying Yang, Tianxiang Goto, Masayuki Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan Department of Industrial and Systems Engineering Faculty of Science and Technology Keio University Japan

In recent years, it has become possible to accumulate a large amount of browsing history data from users on websites and it is desirable to make use of such data for marketing purposes. In particular, customer browsing history collected on EC sites is an important resource for analyzing purchasing behavior because it shows the differences in user preferences and their motivations behind purchasing products. In a previous study, the Latent LSTM Allocation model was proposed for grouping customers’ browsing history data. This is a model that combines the Long Short-Term Memory and the Latent Dirichlet Allocation models, but it does not consider the relationship between browsing behavior and purchase made. In addition, a customer’s browsing time on each page is not taken into consideration, and a page is treated the same regardless of whether a user has spent a long time carefully browsing it, or if they have quickly moved on to the next page. In this study, a new analysis model is proposed that can analyze a user’s willingness to purchase each product based on their relationship between their user’s browsing behavior data including their browsing time and the purchasing behavior, based on the understanding that their browsing time for each page is also an important factor in the customer’s browsing behavior. Specifically, the present paper proposes a new modified Latent LSTM Allocation model that is able to consider not only the information related to a user’s browsing and purchasing behaviors but also the time intervals for their browsing behaviors on individual pages on the target EC site. The proposed model enables to the extraction of groups based on purchasing tendencies considering users’ browsing times for each page. Finally, the proposed model is applied to a case study for an EC site that handles fresh flower products. The obtained findings are then analyzed by applying the proposed model to actual usage history data on the flesh flower EC site. © 2024 Japan

关键词： Sales

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