Flexible Production Research Articles

Employing battery energy storage systems for flexible ramping products in a fully renewable energy power grid: A market mechanism and strategy analysis through multi-Agent Markov games

In high-proportion renewable energy power systems, flexible ramping products (FRPs) are critical for mitigating the volatility of renewable energy outputs and enhancing the adaptability of power systems. This paper aims to explore a fully renewable energy power system, with a battery energy storage system (BESS) as the sole provider of FRPs. An innovative market mechanism and new approaches to market equilibrium analysis are introduced. Initially, a common day-ahead (DA) market is established, covering both electricity and ancillary services with FRPs. The setup allows FRP providers to make bidding decisions and get rewarded based on actual ramping mileages. A bi-level multi-agent Markov game (MG) model is formulated. The upper level integrates the proposed strategies of wind power plants (WPP), photovoltaic power plants (PVP), and BESS, while the lower level encompasses the joint market clearing mechanism. An iterative solution algorithm, using reinforcement learning, is also introduced to address the model, effectively circumventing the privacy concerns associated with traditional mathematical programming methods. The proposed solution models and strategies are validated and demonstrated to be effective through two illustrative examples, highlighting their application and relevance in fully renewable energy power systems.

Working from Home During and After COVID-19: A Narrative Review of Its Multifaceted Impacts

The COVID-19 pandemic accelerated the global adoption of working from home (WFH), fundamentally altering organizational practices and employee experiences. This narrative review synthesizes current research on the multifaceted impacts of WFH during and after the pandemic, focusing on job satisfaction, productivity, work-family balance, collaboration, innovation, and motivation. A comprehensive literature search was conducted across multiple academic databases, encompassing studies from January 2020 to July 2024 and covering diverse industries and employee demographics. Findings indicate that WFH offers numerous benefits, including increased flexibility, autonomy, and potential productivity gains. However, challenges persist, such as reduced social interaction, blurred work-life boundaries, and potential declines in collaboration and innovation. The effectiveness of WFH is influenced by factors like individual circumstances, job characteristics, technological infrastructure, and organizational support. Hybrid work models are emerging as potential solutions, balancing the advantages of remote work with the need for in-person collaboration. This review highlights the complexities of WFH and underscores the necessity for organizations to develop flexible, inclusive policies that address both employee preferences and operational needs. Further research is recommended to explore long-term impacts and best practices for integrating WFH into sustainable organizational strategies.

Mammalian cell-based production of glycans, glycopeptides and glycomodules

Access to defined glycans and glycoconjugates is pivotal for discovery, dissection, and harnessing of a range of biological functions orchestrated by cellular glycosylation processes and the glycome. We previously employed genetic glycoengineering by nuclease-based gene editing to develop sustainable production of designer glycoprotein therapeutics and cell-based glycan arrays that display glycans in their natural context at the cell surface. However, access to human glycans in formats and quantities that allow structural studies of molecular interactions and use of glycans in biomedical applications currently rely on chemical and chemoenzymatic syntheses associated with considerable labor, waste, and costs. Here, we develop a sustainable and scalable method for production of glycans in glycoengineered mammalian cells by employing secreted Glycocarriers with repeat glycosylation acceptor sequence motifs for different glycans. The Glycocarrier technology provides a flexible production platform for glycans in different formats, including oligosaccharides, glycopeptides, and multimeric glycomodules, and offers wide opportunities for use in bioassays and biomedical applications.

A Simple and Efficient One-Step Synthesis System for Flexible Production of Circular RNA in E. coli

A Simple and Efficient One-Step Synthesis System for Flexible Production of Circular RNA in E. coli

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

N-Heterocyclic Carbene-Catalyzed [3 + 3] Annulation of Enals with Aminonaphthoquinones for the Synthesis of Functionalized Aza-anthraquinones.

Rapid construction of functionalized aza-anthraquinones has been successfully developed via NHC-catalyzed formal [3 + 3] annulation of 2-aminoquinones with enals. This reaction features several advantages, such as readily available starting materials, mild reaction conditions, and flexible product transformations. The study on the atroposelective version of this strategy was also carried out, and several C-N axial chiral aza-anthraquinones were synthesized in moderate yields with moderate to good enantioselectivities.

Mechanically robust, self-healing and stretchable electromagnetic shielding materials based on multi-component dynamic bonded polyurethane elastomer

Stretchable self-healing electromagnetic interference (EMI) shielding materials can automatically restore their original performance after mechanical damage, and can still maintain over 20 dB EMI shielding effectiveness (EMI SE) under tensile state. In this work, a series of highly stretchable self-healing polyurethane elastomer PUBNS-X was prepared by introducing dynamic disulfide bonds, boronic ester bonds and boron-nitrogen coordination (B-N) on the polyurethane main chain. It was found that B-N coordination significantly increased the elongation at break and the tensile strength. The high dynamic reversibility of boronic ester, disulfide bond and B-N coordination bond synergistically endowed PUBNS-X with excellent temperature-triggered self-healing properties. The mechanical properties of PUBNS-X can be restored to more than 90 % at 60 °C. The pre-assembled silver nanowires (AgNWs) conductive network was then introduced into the polyurethane surface by taking advantage of the fluidity of the dynamically bonded polyurethane backbone to obtain a series of stretchable self-healing electromagnetic shielding composites (AgNWs/PUBNS-Y). The results showed that the EMI SE of AgNWs/PUBNS-Y can reach ∼55 dB, and the conductivity can be restored to 82 % of the original value after healing at 60 °C. The investigation of the electromagnetic interference shielding performance of composites under different strains showed that the EMI SE of AgNWs/PUBNS-3 can still maintain 20 dB under 90 % strain. These composites will have broad potential applications in the fields of flexible wearable electronic products, bionic intelligent materials and soft robots.

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

Investments in green hydrogen as a flexibility source for the European power system by 2050: Does it pay off?

The European Union aims to deploy a high share of renewable energy sources in Europe’s power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply–demand balance. Green hydrogen (GH) can increase power systems’ flexibility and decrease renewable energy production’s curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around €30/MWh.

The role of process monitoring devices and digital line balancing in achieving operational excellence in garment manufacturing

This study investigates the transformative potential of integrating digital technologies with lean manufacturing principles to address the evolving challenges in the garment industry. The industry demands high-quality products, rapid delivery, cost efficiency, and production flexibility to maintain competitiveness and sustainability. While traditional lean methods have focused on waste reduction and process optimization, the advent of digitalization offers unprecedented opportunities for enhancing operational excellence. This research specifically examines the role of Internet of Things-based Process Monitoring Devices (PMDs) and digital line balancing in achieving these goals. PMDs, integrated within a digital lean framework, facilitate continuous monitoring of machine performance and operator efficiency, providing real-time insights into operational processes. This real-time visibility enables data-driven decision-making and centralized analysis, fostering agility and responsiveness. Furthermore, this study presents a dynamic digital line balancing algorithm that optimizes sewing line configurations and workload distribution. This dynamic approach allows for real-time adaptations in production flows to mitigate bottlenecks and boost productivity. By exploring the synergistic relationship between PMDs, digital line balancing, and lean principles, this paper contributes to a comprehensive understanding of how digital solutions can revolutionize operational efficiency in the garment manufacturing sector.

Additive manufacturing of heat-resistant aluminum alloys: a review

Abstract The capability for synergistic advancements in both making and shaping afforded by additive manufacturing (AM) enables the flexible production of high-performance components. Boosted by the growing demand for heat-resistant aluminum alloys in the moderate-temperature weight-critical applications, AM of heat-resistant aluminum alloys constitutes a burgeoning field. Although numerous advances have emerged in recent years, there remains a gap in the review literature elucidating the newly-developed alloy systems and critically evaluating the efficacy. This state-of-the-art review presents a detailed overview of recent achievements on the heat-resistant aluminum alloy development. It begins with the introduction of various AM technologies and the pros and cons of each technique are evaluated. The enhancement mechanisms associated with printability and high-temperature properties of AM aluminum alloys are then delineated. Thereafter, the various additively manufactured aluminum alloy systems are discussed with regard to the microstructure, heat resistance and high-temperature performance. An emphasis is put on the powder bed fusion-laser beam (PBF-LB) as it has garnered significant attention for heat-resistant aluminum alloys and the vast majority of the current studies are based on this technique. Finally, perspectives are outlined to provide guidance for future research.

Enhanced Efficiency of Dye-Sensitized Solar Cells Using Nitrogen-Titanium Dioxide Composites and Optimized Azo Dye Mixtures

In response to the increasing global energy demand, developing cost-effective and reliable renewable energy sources is crucial. Dye-Sensitized Solar Cells (DSSCs) have emerged as a promising alternative due to their low production cost, flexibility, and capability to operate under diffuse sunlight. This study aims to enhance the efficiency of DSSCs by incorporating Nitrogen-Titanium Dioxide (N-TiO2) composites synthesized via the Sol-Gel method [1]. The DSSCs were constructed using N-TiO2 composites in conjunction with VC, RP, and RM azo dyes. Nitrogen in the TiO2 matrix effectively reduced the band gap, leading to improved light absorption. The presence of Nitrogen dopants is expected to introduce additional energy levels within the TiO2 bandgap, enhancing light absorption and charge carrier separation, which are crucial factors for improved DSSC performance [2,3]. The current-voltage (I-V) characteristics of the DSSCs revealed significant variations in key performance metrics, including short circuit current (Isc), fill factor, and power conversion efficiency. Specifically, DSSCs utilizing N-TiO2 with VC-RP dye mixture achieved a higher efficiency of 0.349%, an Isc of 0.85 mA, and a fill factor of 44.59%, with Rs and Rsh values of 328.13Ω and 1637.18Ω, respectively [4]. These results underscore the potential of N-TiO2 composites, particularly when paired with optimized dye mixtures, to significantly enhance the electronic properties and overall efficiency of DSSCs.

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Comparison of Positioning Error Prediction Results of Industrial Robots Based on Three Different Types of Neural Networks

ABSTRACTWith the increasing development of industry, the market demand for manufacturing has shifted to large‐scale customized production. This poses new challenges to the production flexibility of industrial robots. The offline programming method can perfectly meet this challenge. But its disadvantage is that it relies heavily on the absolute positioning accuracy of industrial robots. In recent years, there has been an increasing number of studies using neural networks (NN) to predict the positioning errors of industrial robots to improve their absolute positioning accuracy. However, most of these studies only focus on the application of NNs, and do not compare the prediction results and performance of different kinds of NNs. This paper selects three typical network models: backpropagation neural network (BPNN), particle swarm algorithm optimization BPNN (PSO‐BPNN), and radial basis function neural network (RBFNN). Through in‐depth experiments and analysis of these networks, the purpose is to reveal their respective prediction effects and characteristics and to summarize their advantages and disadvantages. Experimental results show that BPNN performs poorly in predicting positioning errors. As an optimization method, the particle swarm algorithm can effectively improve the prediction performance of BPNN. In contrast, the RBFNN performs well, which makes it very suitable for predicting the positioning error of industrial robots.

Configuration and multistep switching of machines in dynamic hybrid MTS/MTO production systems

This study proposes the configuration of MTS-/MTO-dedicated and hybrid (switchable) machines under a multistep switching policy where each hybrid machine can be switched stepwise and compares it to the existing model with MTS-dedicated and hybrid machines under a group switching policy that makes all hybrid machines switch together. Since companies have been required to satisfy wide varieties of customers' needs in recent years, the dynamic hybrid make-to-stock (MTS) / make-to-order (MTO) production system–one of the flexible production strategies with superior efficiency and productivity–is widely adopted in the garment industry. This system contains MTS and MTO streams in a shared facility and holds dedicated and hybrid machines that can be switched between MTS and MTO production. The machine configuration and switching policy are essential factors to increase the effectiveness of this system; hence, the features of such components must be investigated. Numerical experiments demonstrate that the cost and the number of MTO customers waiting can be reduced by appropriately setting the multistep switching policy and the machine configuration. Effective operating rules can also be achieved using our proposed search heuristics. These results contribute to the improvement of the existing dynamic hybrid system and the expansion of its possible applications.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

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.

Sanyensin with an Unprecedented Architecture: An Effective Strategy from Discovery to Stereochemical Identification of Flexible Natural Products.

Under the guidance of genome mining combined with bioassay-coupled metabolomic analyses, an unprecedented macrodiolide sanyensin (1), with two flexible macrolides fused by the rigid oxabicyclo[3.3.1]nonane core, was isolated from the deep-sea-derived Streptomyces sp. OUCT16-30. The stereochemistry of 1 was established by NOEs (nuclear Overhauser effects), J-based configuration analysis, Marfey's analysis, and together with a newly introduced stereochemical study workflow, which greatly shortens the time to obtain correct conformations of flexible structures based on the NMR constraints, thus leading to reliable quantum chemical calculations to establish the absolute configurations. This workflow is expected to have broad applicability for elucidating the stereochemistry of flexible natural products. The macrodiolide framework of 1 is proposed to be formed through a biocatalytic cyclodimerization, followed by a series of nonenzymatic reactions. This work leads to new insights into the unexplored biosynthetic potential of deep-sea microbes and also provides a practical streamline for efficient mining of novel natural products, from discovery to stereochemical finalization.

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

Multi-Objective Production Rescheduling: A Systematic Literature Review

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with “a priori” and “a posteriori” optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, “interactive” and “Pareto pruning” methods, as well as the consideration of human factors in flexible production systems, remain under-explored.