Browsing by Author "Milisavljevic-Syed, Jelena"
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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 AI-based reconfigurable inspection system (RIS): comprehensive model and implementation in industry(Elsevier, 2024-01-12) Kumar, A. Sarat; Milisavljevic-Syed, JelenaDue to global competition and continuously changing customer demand, manufacturers nowadays face frequent and unpredictable market shifts. Introducing reconfigurability into contemporary manufacturing systems can enhance cost-effective and rapid responsiveness to these variations. A Reconfigurable Manufacturing System (RMS) can provide a tailored production process in response to changes in operating procedures or machine statuses. Just like any other manufacturing system, RMS requires effective and timely diagnosis as well as prognosis to function smoothly. A Reconfigurable Inspection System (RIS) is designed within an RMS for data-oriented detection of product quality with a minimum number of inspection units. Existing studies about reconfiguration, however, focus on production while disregarding inspection. Artificial Intelligence (AI) has the potential to significantly assist manufacturers over the next decade due to their heavy dependency on data. AI applications such as Machine learning (ML) and Deep Learning (DL) can aid in addressing issues such as tracking manufacturing failures back to specific phases in the manufacturing process by learning relevant data patterns. Thus, this paper aims to provide an overview of the current literature on RMS as well as ML/DL technologies that can be integrated into RIS to enhance performance. Subsequently, a comprehensive model of an AI-based RIS is proposed based on the experimental results derived from existing publications, and the retrofitting procedure of a case study is presented. However, the proposed model and the retrofitting procedure are not validated by experimental results or physical implementation.Item Open Access 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, JiIdentifying 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.Item Open Access Designing evolving cyber-physical-social systems: computational research opportunities(American Society of Mechanical Engineers, 2023-07-03) Allen, Janet K.; Nellippallil, Anand Balu; Ming, Zhenjun; Milisavljevic-Syed, Jelena; Mistree, FarrokhIn the context of the theme for this special issue, namely, challenges and opportunities in computing research to enable next generation engineering applications, our intent in writing this paper is to seed the dialog on furthering computing research associated with the design of cyber-physical-social systems. Cyber-Physical-Social Systems (CPSS's) are natural extensions of Cyber-Physical Systems (CPS's) that add the consideration of human interactions and cooperation with cyber systems and physical systems. CPSS's are becoming increasingly important as we face challenges such as regulating our impact on the environment, eradicating disease, transitioning to digital and sustainable manufacturing, and improving healthcare. Human stakeholders in these systems are integral to the effectiveness of these systems. One of the key features of CPSS is that the form, structure, and interactions constantly evolve to meet changes in the environment. Design of evolving CPSS include making tradeoffs amongst the cyber, the physical, and the social systems. Advances in computing and information science have given us opportunities to ask difficult, and important questions, especially those related to cyber-physical-social systems. In this paper we identify research opportunities worth investigating. We start with theoretical and mathematical frameworks for identifying and framing the problem – specifically, problem identification and formulation, data management, CPSS modeling and CPSS in action. Then we discuss issues related to the design of CPSS including decision making, computational platform support, and verification and validation. Building on this foundation, we suggest a way forward.Item Open Access Industry 4.0: a systematic review of legacy manufacturing system digital retrofitting(EDP Sciences, 2022-11-07) Alqoud, Abdulrahman; Schaefer, Dirk; Milisavljevic-Syed, JelenaIndustry 4.0 technologies and digitalised processes are essential for implementing smart manufacturing within vertically and horizontally integrated production environments. These technologies offer new ways to generate revenue from data-driven services and enable predictive maintenance based on real-time data analytics. They also provide autonomous manufacturing scheduling and resource allocation facilitated by cloud computing technologies and the industrial Internet of Things (IoT). Although the fourth industrial revolution has been underway for more than a decade, the manufacturing sector is still grappling with the process of upgrading manufacturing systems and processes to Industry 4.0-conforming technologies and standards. Small and medium enterprises (SMEs) in particular, cannot always afford to replace their legacy systems with state-of-the-art machines but must look for financially viable alternatives. One such alternative is retrofitting, whereby old manufacturing systems are upgraded with sensors and IoT components to integrate them into a digital workflows across an enterprise. Unfortunately, to date, the scope and systematic process of legacy system retrofitting, and integration are not well understood and currently represent a large gap in the literature. In this article, the authors present an in-depth systematic review of case studies and available literature on legacy system retrofitting. A total of 32 papers met the selection criteria and were particularly relevant to the topic. Three digital retrofitting approaches are identified and compared. The results include insights common technologies used in retrofitting, hardware and software components typically required, and suitable communication protocols for establishing interoperability across the enterprise. These form an initial basis for a theoretical decision-making framework and associated retrofitting guide tool to be developed.Item Open Access The learning factory through the sustainability lens(SSRN, 2023-06-07) Milisavljevic-Syed, Jelena; Afy-Shararah, Mohamed; Sahin, Orhan; Salonitis, KonstantinosManufacturing organizations are facing increasing global pressures to be more digitalized, sustainable, and lean. Whereas the manufacturing industry is facing a shortage of demanding competencies of Industry 4.0., because the talent pool is either dry or the knowledge related to such skills is still not clearly formulated. Learning factories, through their triangular depth of education, research, and training, have been seen by many researchers in recent years as suitable environments to address these gaps in knowledge and skills. Although it is very useful for academia and industry alike, not much has been found on how to develop a learning factory. In this paper, the authors propose a new morphology and shed light on the sustainability that should be addressed when designing or reconfiguring learning factories. They provide existing limitations and future challenges and questions as research opportunities that must be addressed to further advance this learning environment.Item Open Access Managing multi-goal design problems using adaptive leveling-weighting-clustering algorithm(Springer, 2022-09-25) Guo, Lin; Milisavljevic-Syed, Jelena; Wang, Ru; Huang, Yu; Allen, Janet K.; Mistree, FarrokhIn this paper, we address the issue of solving problems with multiple components, multiple objectives, and target values for each objective. There are limitations in managing these multi-component, multi-goal problems such as the need for domain expertise to combine or prioritize the goals. In this paper, we propose a domain-independent method, Adaptive Leveling-Weighting-Clustering (ALWC), to manage the exploration of design scenarios of multi-goal, engineering-design problems. Using ALWC, designers explore combinations and priorities of the goals based on their interrelationships. Through iteration, design scenarios are obtained with higher goal achievements and an improved understanding of the relationship among subsystems. This is achieved without increasing computational complexity. This knowledge is helpful for multi-component design. The ALWC method is demonstrated using a thermal-system design problem.Item Open Access 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, JelenaEnd-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.Item Open Access Predictive maintenance servitisation pathways(Elsevier BV, 2024) Li, Jiahong; Milisavljevic-Syed, Jelena; Salonitis, KonstantinosThe competition within the manufacturing industry is becoming increasingly intense in the Industry 4.0 era. Some manufacturers are transforming their traditional product-selling business model into product-service combos to gain customer loyalty and new sources of revenue through the so-called manufacturing servitisation. Predictive maintenance (PdM) is a strategy that significantly overlaps with manufacturing servitisation in terms of enabling technologies, including IoT, Big Data, Cloud Computing, etc. PdM is expected to both maximise productivity and minimise maintenance costs. Although much attention has been given to PdM and manufacturing servitisation, previous studies have ignored the synergy of PdM and manufacturing servitisation. This paper highlights the importance and feasibility of implementing PdM servitisation. Additionally, by analysing practical PdM servitisation cases, three PdM servitisation pathways are defined. Finally, the defined pathways are discussed, and future research directions are proposed.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 Quantification of students’ active learning in design, build, and test engineering modules(University of Liverpool, 2023-01-18) Milisavljevic-Syed, JelenaThe focus in this paper is to address the primary research question ‘How can instructors leverage assessment tools in design, build, and test modules to quantify students’ active learning well enough to improve modules for future students?’ In the engineering module, Product Design Group Project (PDGP), the primary goal is to enable students to internalize five principles of engineering design (POED), wherein each assignment students are tasked with writing learning statements (LS). LS captures how much students internalize the target POED and formulate an understanding of how to apply this knowledge moving forward. Each academic year in the PDGP module, at a university in north-west England, around 780 LS are consented by module students. In this paper, a flexible text mining framework is used to process LS and analyse students’ learning and improve the delivery of design, build, and test engineering modules, such as the PDGP.Item Open Access Realisation of responsive and sustainable reconfigurable manufacturing systems(Taylor and Francis, 2023-07-18) Milisavljevic-Syed, Jelena; Li, Jiahong; Xia, HanbingThere 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.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 Towards pre-emptive resilience in military supply chains: a compromise decision support model-based approach(Taylor & Francis, 2023-06-09) Sani, Shehu; Schaefer, Dirk; Milisavljevic-Syed, JelenaThe complex and dynamic nature of military supply chains (MSC) requires constant vigilance to sense potential vulnerabilities. Several studies have employed decision support models for the optimization of their operations. These models are often limited to a best single-point solution unsuitable for complex MSC constellations. In this article, the authors present a novel approach based on decision support models to explore a range of satisficing solutions against disruptions in MSCs using a compromise Decision Support Problem (cDSP) construct and Decision Support in the Design of Engineered Systems (DSIDES). Two cases were evaluated: (1) a baseline scenario with no disruption and (2) with disruption to achieve target values of three goals: (1) minimizing lead time, (2) maximizing demand fulfilment and (3) maximizing vehicle utilization. The results obtained in Case 1 identified a more stable solution space with minimal deviations from the target value, while in Case 2 the solution space was unstable with deviations from the target values.Item Open Access Transformation of the product lifecycle value chain towards industry 5.0(Elsevier BV, 2024-06) Xia, Hanbing; Li, Jiahong; Milisavljevic-Syed, Jelena; Salonitis, KonstantinosThe 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.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.