Browsing by Author "Han, Ji"

Now showing 1 - 4 of 4
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    Advancing engineering design problem-exploring practice: interviews with industry professionals
    (Taylor and Francis, 2025-01-01) Obieke, Chijioke C.; Milisavljevic-Syed, Jelena; Han, Ji
    Studies highlight that conceptualising and identifying a new engineering design problem (EDP) is vital, as the solution can benefit society. However, this essential activity, referred to as engineering design problem-exploring (EDPE), is lacking in practice in engineering design. Design engineers appear to focus on providing an engineering design solution (EDS) while their role in EDPE is rarely practised. A new EDP drives innovations and inventions, and there is a need to encourage, advance and sustain the practice of identifying new EDPs. The aim of this study is to empirically highlight the underlying determinants of the scarce practice of EDPE and suggest how to advance and sustain the practice. Interviews were conducted with 32 professionals within the engineering design community, comprising 28 practitioners and four specialists – a lecturer, an inventor, and two expert trainers in creativity and problem-solving. The results of the analyses informed the suggested approaches in this study to advance and sustain the EDPE practice.
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    A computational approach to identifying engineering design problems
    (American Society of Mechanical Engineers (ASME), 2023-01-09) Obieke, Chijioke C.; Milisavljevic-Syed, Jelena; Silva, Arlindo; Han, Ji
    Identifying new problems and providing solutions are necessary tasks for design engineers at early-stage product design and development. A new problem fosters innovative and inventive solutions. Hence, it is expected that engineering design pedagogy and practice should equally focus on engineering design problem-exploring (EDPE)—a process of identifying or coming up with a new problem or need at the early-stage of design, and engineering design problem-solving (EDPS)—a process of developing engineering design solutions to a given problem. However, studies suggest that EDPE is scarcely practiced or given attention to in academia and industry, unlike EDPS. The aim of this paper is to investigate the EDPE process for any information relating to its scarce practice in academia and industry. This is to explore how emerging technologies could support the process. Natural models and phenomena that explain the EDPE process are investigated, including the “rational” and “garbage can” models, and associated challenges identified. A computational framework that mimics the natural EDPE process is presented. The framework is based on Markovian model and computational technologies, including machine learning. A case study is conducted with a sample size of 43 participants drawn worldwide from the engineering design community in academia and industry. The case study result shows that the first-of-its-kind computational EDPE framework presented in this paper supports both novice and experienced design engineers in EDPE.
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    Predicting the quantity of recycled end-of-life products using a hybrid SVR-based model
    (American Society of Mechanical Engineers, 2023-11-21) Xia, Hanbing; Han, Ji; Milisavljevic-Syed, Jelena
    End-of-life product recycling is crucial for achieving sustainability in circular supply chains and improving resource utilization. Forecasting the quantity of recycled end-of-life products is essential for planning and managing reverse supply chain operations. Decision-makers and practitioners can benefit from this information when designing reverse logistics networks, managing tactical disposal, planning capacity, and operational production. To address the challenge of small sample data with multiple factors influencing the recycling number, and to deal with the randomness and nonlinearity of the recycling quantity, a hybrid predictive model has been developed in this research. The model is based on k-nearest neighbor mega-trend diffusion (KNNMTD), particle swarm optimization (PSO), and support vector regression (SVR) using the data from the field of end-of-life vehicles as a case study. Unlike existing literature, this research incorporates the data augmentation method to build an SVR-based model for end-of-life product recycling. The study shows that developing the predictive model using artificial virtual samples supported by the KNNMTD method is feasible, the PSO algorithm effectively brings strong approximation ability to the SVR-based model, and the KNNMTD-PSO-SVR model perform well in predicting the recycled end-of-life products quantity. These research findings could be considered a fundamental component of the smart system for circular supply chains, which will enable the smart platform to achieve supply chain sustainability through resource allocation and regional industry deployment.
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    Predictive modeling for the quantity of recycled end-of-life products using optimized ensemble learners
    (Elsevier, 2023-06-09) Xia, Hanbing; Han, Ji; Milisavljevic-Syed, Jelena
    The rapid development of machine learning algorithms provides new solutions for predicting the quantity of recycled end-of-life products. However, the Stacking ensemble model is less widely used in the field of predicting the quantity of recycled end-of-life products. To fill this gap, we propose a Stacking ensemble model that utilizes support vector regression, multi-layer perceptrons, and extreme gradient boosting algorithms as base models, and linear regression as the meta model. The k-nearest neighbor mega-trend diffusion method is applied to avoid overfitting problems caused by a small sample data set. The grid search and time series cross validation methods are utilized to optimize the proposed model. To verify and validate the proposed model, data related to China's end-of-life vehicles industry from 2006 to 2020 is used. The experimental results demonstrate that the proposed model achieves higher prediction accuracy and generalization ability in predicting the quantity of recycled end-of-life products.