Browsing by Author "Xia, Hanbing"
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Item Open Access Achieving net zero neighborhoods: a case study review of circular economy initiatives for South Wales(Elsevier, 2024-07-11) Edwards, Jacob; Xia, Hanbing; Li, Qian Jan; Wells, Peter; Milisavljevic-Syed, Jelena; Gallotta, Alberto; Salonitis, KonstantinosAdopting net zero neighborhoods (NZN) combined with circular economy are increasingly recognized as an essential strategy for construction companies, allowing their transition towards sustainability and contributing to climate change mitigation. Transforming existing neighborhoods into NZN is necessary to achieve energy self-sufficiency and net zero by 2050. The success of NZN hinges on government initiatives. However, existing studies appear to lack a comprehensive exploration of the transformation initiatives within NZN, spanning aspects like raw materials, construction practices, transportation, and waste management, particularly in terms of technological advancements. To address the gap, this research introduces a conceptual framework that integrates the multiple case studies design method with an urban transformative capacity model. This innovative framework is the first application of a multi-level urban transformative capacity model in transforming existing neighborhoods into NZN within a specific region. This research specifically utilizes South Wales in the UK as a case study. It identified four key initiatives in technology, construction, transportation, and waste management. These initiatives offer significant benefits, including fostering a more sustainable and environmentally friendly construction industry through enhanced traceability and efficiency, and simultaneously promoting social sustainability by improving community engagement, providing social benefits through sharing economy initiatives, and creating new green job opportunities.Item Open Access 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, JelenaThe 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.Item Open Access Supply chain 4.0: a machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risk(MDPI, 2023-09-04) Sani, Shehu; Xia, Hanbing; Milisavljevic-Syed, Jelena; Salonitis, KonstantinosIn today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional approaches to SCM lack the ability to predict risks accurately due to their computational complexity. In the present research, a hybrid Bayesian-optimized Light Gradient-Boosting Machine (LightGBM) model, which accurately forecasts backorder risk within SCM, has been developed. The methodology employed encompasses the creation of a mathematical classification model and utilises diverse machine learning algorithms to predict the risks associated with backorders in a supply chain. The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management.Item Open Access Uncertain programming model for designing multi-objective reverse logistics networks(Elsevier, 2024-05-20) Xia, Hanbing; Chen, Zhiyuan; Milisavljevic-Syed, Jelena; Salonitis, KonstantinosGiven the contradiction between the rapid growth of products and the modest recovery rate of end-of-life products, there is a pressing need to understand the societal significance of establishing a reverse logistics network for end-of-life products. This research constructs an open-loop five-tier reverse logistic network model encompassing customers, centres for collection, disassembly and inspection, remanufacturing, and disposal. A multi-objective mixed-integer nonlinear programming model under uncertainty has been developed. Unlike previous research, this model accounts for surrounding residents' disutility of facilities while simultaneously minimizing economic costs and environmental impact. Besides, uncertainty theory is introduced in solving the proposed model. More specifically, the formulated model converts all uncertain variables into uncertain distributions by implementing the uncertain multi-objective programming method. Furthermore, a customised non-dominated sorting genetic algorithm III (NSGA-III) is proposed and is employed for the first time to address facility selection and recycling volume distribution within the network. The model is then validated using a real-life case study focusing on end-of-life vehicles in Changchun, China. This research could assist decision-makers in both governmental and private sectors in achieving a balanced approach to the interests of the economy, environment, and local communities comprehensively when designing reverse supply chains.