Search Results (52)

Medical consumable orders are characterized by diverse product types, small batch sizes, frequent orders, and high customization requirements, often leading to inefficient workshop scheduling and difficulties in meeting multiple production constraints. To address these challenges, this study proposes a bi-level optimization model for order splitting and reorganization considering multi-dimensional and multi-scale characteristics. The multi-dimensional characteristics encompass materials, processes, equipment, and work efficiency, while the multi-scale aspects involve finished products, components, assemblies, and parts. At the upper level, the model optimizes order task splitting by refining splitting strategies and preprocessing constraints to generate high-quality input for the reorganization phase. The lower level optimizes sub-task prioritization, batch sizes, and resource scheduling to develop a production plan that balances cost and efficiency. Subsequently, to solve this bi-level optimization problem, a hybrid bi-objective optimization algorithm is designed, integrating a collaborative iterative strategy to enhance solution efficiency and quality. Finally, a case study and comparative experiments validate the practicality and effectiveness of the proposed model and algorithm. Full article

This study examines how climate citizens’ assemblies (CCAs) influence youth engagement, both in practice and in shaping perceptions of political “voice”, drawing on Albert O. Hirschman’s “Exit, Voice, and Loyalty” framework. Through qualitative analysis of 71 interviews, participatory workshops, and observational data across four cities—Barcelona, Bologna, Geneva, and Paris—it explores whether CCAs address youth climate activists’ demands, enable meaningful participation, and yield policy outcomes aligned with their claims. Findings reveal divergent perceptions: in Bologna, co-design with activists tended to foster trust, while Barcelona saw disillusionment due to unmet expectations. Geneva’s embedded deliberative processes strengthened institutional loyalty, whereas Paris faced skepticism as activists turned to civil disobedience. Overall, CCAs’ effectiveness hinges on inclusivity, follow-through, and communication; when perceived as tokenistic, they risk reinforcing disengagement (“exit”). The study underscores the need for embedded, responsive democratic innovations to sustain youth participation amid climate crises. Full article

In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. The model conducts quantitative analyses of workshop layout, assembly line design, worker status, operating status of robotic arms and AGV vehicles, and production system failure rates. A hybrid NEH-DDQN algorithm is integrated into the simulation model via a COM interface and DLL, where the NEH algorithm ensures the model maintains optimal performance during the early training phase. Four scheduling strategies are designed for workpiece allocation across different workshops. A deep neural network replaces the traditional Q-table for greedy selection among these four scheduling strategies, using each workshop’s completion time as a simplified state variable. This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. Experimental comparisons show that for the data of 500 workpieces, the NEH algorithm in 3 s demonstrates equivalent quality to that produced by the GA algorithm in 300 s. After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue. Full article

Longitudinal research commenced in 2012 and was repeated in 2022 in two regional areas in Victoria, Australia. The researchers sought to understand the facilitators and barriers to climate adaptation, given the perception of the authors that climate adaptation was making little progress, a view supported following an extensive literature review and international consultations. Adaptation was not part of the debate when climate change was first discussed by the UN General Assembly in 1988 and not identified by the IPCC until 2007. Recent Australian governments have shown a ‘hands-off’ and uniformed approach. Research workshops and consultations sought the views of residents, community organisations, local governments and representatives of state agencies, who were invited or requested attendance. The workshops were designed to understand the perspective of participants, using a Search Conference methodology with both guided questions and participant-led issues. The results suggest that, despite the presence of many adaptation plans, the fundamental arrangements needed for the scale of adaptation required were not in place in 2012, nor in 2022. There was a lack of federal and state government action beyond their own institutional structures, responsibility for action being passed down the line to local government, business and community. Yet this devolvement was commonly not accompanied by financial support, supportive and inclusive governance arrangements, expert advice, data, or clear guidance for action. Climate adaptation policy remains disconnected from the broader economy, with little progress on how to achieve this task, which is rapidly growing in size and complexity. There is not an accepted roadmap for effective adaptation, an approach that does not easily fit into the risk-averse approach of public sector management that has prevailed in Australia since the 1980s. Full article

The pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneously optimized. However, existing research predominantly focuses on product scheduling, with limited attention given to AGV scheduling. This paper proposes an optimized solution for the pulsating assembly line scheduling problem, incorporating multifunctional AGV scheduling. A mathematical model is developed and three AGV selection strategies and three AGV standby strategies are designed to optimize AGV scheduling and control. To improve scheduling efficiency, nine heuristic strategies are introduced, along with the Variable Neighborhood Descent (VND) algorithm as a metaheuristic method for product scheduling. The VND algorithm refines the solution through multiple neighborhood searches, enhancing both the precision and efficiency of product scheduling. Our experimental results demonstrate that the proposed strategies significantly improve the production efficiency of pulsating assembly workshops, reduce AGV scheduling costs, and optimize overall production workflows. This study offers novel methods for intelligent scheduling in pulsating assembly workshops, contributing to the advancement of manufacturing toward “multiple varieties, small batches, and customization”. Full article

Bacterial communities are highly susceptible to fluctuations in amino acid content. To investigate the response of microbial communities in daqu to environmental perturbations, we employed high-throughput sequencing and statistical analyses. Samples were collected from two workshops (A and B) at distinct stages of daqu fermentation and storage. Our analysis, using the β-nearest taxon index (βNTI), revealed that fungal community assembly is shaped by both stochastic and deterministic processes. In contrast, bacterial communities exhibited a shift towards deterministic assembly under environmental stress, with fluctuations in amino acid content being a primary driver. Notably, communities with active amino acid metabolism displayed a greater involvement of stochastic processes and harbored a higher number of bacterial keystone taxa, which contributed to the stability of microbial networks. This study provides novel insights into the complex interplay between microbial communities and their environment in the context of daqu. Full article

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations. Full article

Infrastructure decommissioning refers to retiring infrastructure at the end of its useful life. Since all types of tangible infrastructure have a finite lifespan, the challenges of decommissioning are as significant as those of its creation. Despite this, there is an insufficient accumulation of knowledge regarding decommissioning. Given the intergenerational nature of this issue, the current brief report explores how the methodology of Future Design and its key concept, ‘imaginary future people’, can transform the perspectives of individuals involved in infrastructure decommissioning. In 2018, Uji City Hall in Kyoto Prefecture invited 32 citizens to participate in a series of 4 workshops contemplating the future of local communities and the 132 public assembly facilities, constituting 47.3% of the city’s public facilities. Future Design methodology and the concept of imaginary future people were adopted. Then the workshop discussions were transcribed and analyzed to compare how participants’ perspectives changed when they embraced the concept of imaginary future people, as opposed to when they did not. The analysis revealed that the concept encourages individuals to envision the future broadly. Based on this envisioning, they derive answers about the role of public assembly facilities, often considered peripheral. The paper concludes that Future Design is effective in exploring how ‘value’ can be created in decommissioning, motivating individuals to address infrastructure decommissioning. To elucidate the mechanisms by which Future Design produces such effects, further and more thorough research is required, which falls outside the scope of this report. Nevertheless, reporting that such an effect has been observed in a single case is considered to have both social and academic value. Full article

This research describes an online solution for the collection and processing of production data, which are gathered from manufacturing and assembly processes at automotive companies. The solution describes the process for live monitoring of the production health and then evaluation through reports, with the option to generate reports for up to six months. Since the data are located in multiple sources, it is challenging to monitor them live or generate reports on demand. The solution described in this research outlines applications that simplify users’ tasks and provide immediate insights into the processes and health of production lines. Research will be divided into three applications which are delivered in one package, which is called Cycle Time Deviation (CTD): (i) workshop application for live monitoring; (ii) for evaluating data older than 24 h, the shift report application; and (iii) for comparing and monitoring the impact of process changes on the analysis, the before and after application—the Plant Improvement Tracker (PIT)—will be presented. The aim of the research is to describe the proposed solution that was implemented in a multinational automotive corporation and to outline the benefits gained from the implementation. Full article

In order to dynamically respond to changes in the state of the assembly line and effectively balance the production efficiency and energy consumption of mixed-model assembly, this paper proposes a deep reinforcement learning sustainable scheduling model based on the Deep Q network. According to the particularity of the workshop material-handling system, the action strategy and reward and punishment function are designed, and the neural network structure, parameter update method, and experience pool selection method of the original Deep Q network dual neural network are improved. Prioritized experience replay is adopted to form a real-time scheduling method for workshop material handling based on the Prioritized Experience Replay Deep Q network. The simulation results demonstrate that compared with other scheduling methods, this deep reinforcement learning approach significantly optimizes material-handling scheduling in mixed-flow assembly workshops, effectively reducing handling distance while ensuring timely delivery to the assembly line, ultimately achieving maximum output with sustainable considerations. Full article

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control. Full article

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production. Full article

Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed. Full article

Topological interlocking assembly (TIA) and computational architecture treat form as an emergent property of a material system, where the final shape results from the interplay of geometries and geometric interdependencies influenced by contextual constraints (material, structure, and fabrication). This paper posits that TIA is an ideal pedagogical tool for introducing students to computational architecture, and its theoretical foundations and design principles. Specifically, defining TIA as a material system provides a robust educational approach for engaging students with computation; fostering design processes through bottom-up, hands-on investigations; expressing design intents as procedural logic; understanding generative geometric rules; and exploring the flexibility of parametric variations. The methodology is detailed and illustrated through a design workshop and study unit from the Bachelor’s and Master’s programs at the Faculty for the Built Environment, University of Malta. Four case studies of TIA—of tetrahedra, cones, octahedra, and osteomorphic blocks—demonstrate how these exercises introduce students to computational thinking, parametric design, and fabrication techniques. This paper discusses the advantages and limitations of this pedagogical methodology, concluding that integrating computational architecture in education shifts students’ design processes to investigation and innovation-based approaches, enabling them to address contemporary design challenges through context-driven solutions. Full article

Traditionally, food system governance has been executed at provincial, national, or supranational levels, but recently, there has been an increasing trend of local areas coordinating initiatives to influence their own food systems. They are distinguished by their focus on the principles of food democracy, especially in terms of citizen participation. Although initiatives to boost citizen collaboration in local food systems are on the rise, achieving active citizen involvement can prove challenging. This study aims to understand citizen engagement and participation in local food systems at the regional, municipal, or neighborhood levels. A narrative literature review was performed, covering academic and gray literature and using descriptive statistics and semi-inductive thematic analysis. The analysis identified five categories of local food system citizen participation programs, i.e., governance committees that include citizens, citizen working groups, participatory research, participatory workshops, and citizen forums and assemblies. The review highlighted factors that influence the operation and perceived success of citizen participation initiatives. Findings underscore the importance of creating citizen participation mechanisms that are inclusive, transparent, and adaptable. Proper organization and facilitation by a dedicated local organization ensure the success of citizen engagement initiatives. The identified categories and factors can guide policymakers in designing effective local food system initiatives that enhance meaningful citizen involvement. Full article