Browsing by Author "Xia, Hanbing"

Now showing 1 - 7 of 7
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    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, Konstantinos
    Adopting 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.
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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.
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    Realisation of responsive and sustainable reconfigurable manufacturing systems
    (Taylor and Francis, 2023-07-18) Milisavljevic-Syed, Jelena; Li, Jiahong; Xia, Hanbing
    There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing.
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    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, Konstantinos
    In 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.
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    Transformation of the product lifecycle value chain towards industry 5.0
    (Elsevier BV, 2024-06) Xia, Hanbing; Li, Jiahong; Milisavljevic-Syed, Jelena; Salonitis, Konstantinos
    The transition from Industry 4.0 to Industry 5.0 has broadened the focus of enterprises, moving beyond their organisational boundaries limits to embrace the interconnected structure within the product lifecycle value chain. Despite this shift, there remains a significant gap in research on the framework for transforming the product lifecycle value chain from Industry 4.0 to Industry 5.0. Thus, building upon the achievements of Industry 4.0, the proposed value chain transformation frameworks are proposed, which can improve sustainability and resilience while considering human needs. The frameworks offer enterprises a comprehensive understanding of the structure and strategic approach required for the Industry 5.0 product lifecycle value chain transformation. Finally, the authors summarise six research challenges and opportunities and eleven research questions to advance the transformation of the Industry 5.0 product lifecycle value chain.
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    Uncertain programming model for designing multi-objective reverse logistics networks
    (Elsevier, 2024-05-20) Xia, Hanbing; Chen, Zhiyuan; Milisavljevic-Syed, Jelena; Salonitis, Konstantinos
    Given 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.