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This paper addresses some of the new challenges posed by scheduling and controlling the production of chimeric antigen receptor (CAR) T cells for personalized therapies. In this therapy, the patient's T cells are genetically modified by adding a gene for a receptor to help T cells attach to the cancer cell antigens. Then, the modified cells are grown and multiplied (called cell expansion) and finally returned to the patient. The production takes place in a manufacturing system that comprises several devices, which are capable of processing cells from multiple patients at the same time. The efficient operation of such a complex system requires a sophisticated control system accompanied by a scheduler to optimize the production of CAR T cells for several patients simultaneously. We propose a scheduling algorithm, which can handle the uncertain duration of cell expansion, and the maximum time lags between consecutive processes simultaneously, a combination hardly studied in the literature. We also describe the main features of an adaptive control system including service-oriented device connections as well as centralized control and monitoring. Finally, we describe how the overall system works, including a JSON based communication between scheduler and control system. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement in global market integration, manufacturing enterprises face increasingly fierce competition, making the development of intelligent manufacturing systems a key factor in enhancing market competitiveness. However, manufacturing systems are characterized by varying characteristics of manufacturing resources and strong interdependencies, which make production control more complex. A bottleneck refers to the issue where the manufacturing system's actual production capacity is less than or equal to the demand placed on a resource. After extensive research, scholars have concluded that the definition, identification methods, and related studies on bottlenecks are not fixed but depend on the specific research subject and the type of bottleneck. Therefore, scholars suggest that only by accurately defining the bottlenecks within a system can appropriate models be employed for prediction, thereby avoiding issues such as inefficient resource allocation and delivery delays, or implementing measures to minimize these negative impacts. Particularly under unstable production conditions, dynamic bottlenecks are likely to occur, making the prediction and identification of bottlenecks under dynamic environmental conditions crucial. Currently, there is still a lack of research in real-time state sensing and integration, as well as a lack of systematic review of related research. To fill this research gap, this review comprehensively introduces the current state and achievements in bottleneck research within manufacturing systems, focusing on bottleneck categories, identification, shifting, and management. It also provides an outlook on future research trends and directions in the study of manufacturing system bottlenecks. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively. [ABSTRACT FROM AUTHOR]

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. [ABSTRACT FROM AUTHOR]

The relevance of assessing the applicability of intelligent wells using autonomous inflow control devices lies in the active development of the relevant sector of the oil and gas industry and the limited understanding of the economic efficiency of intelligent wells. The use of autonomous inflow control devices allows for a change in the composition of flow to the well, thus contributing to delaying the breakthrough of undesirable formation fluids, but at the same time, such an effect affects the dynamics of formation fluid production, which undoubtedly has a huge impact on the economic effect of the project. The analysis of scientific publications on the topic of "intelligent well completion" as a downhole production monitoring and remote production control system has shown that the vast majority of researchers pay attention to the evaluation of technological efficiency, ignoring the economic aspects of the proposed solutions. This study considered the dependence of the economic effect on the geological reservoir and technological well characteristics for variant 1—intelligent horizontal well (HW) completion using autonomous inflow control devices and variant 2—conventional horizontal well completion using the open hole. Calculations of production levels and dynamics in the two variants were performed on a created sector hydrodynamic model of a horizontal well operating in the depletion mode. The analysis of the obtained results allowed us to determine the applicability criteria of the proposed configuration of formation and well characteristics at the object of study, as well as to establish general dependencies of the net discounted income of an intelligent well. As a result of this study, it was determined that the economic efficiency of intelligent well completion with the use of autonomous inflow control devices relative to conventional well completion increases with decreasing permeability and drawdown pressure on the reservoir and reaches maximum values at the object of study at the thickness of the oil-saturated part of the reservoir about 5–6 m and the location of the wellbore in it at 35–40% of the thickness of the oil-saturated part below the gas–oil contact (GOC). This article covers the research gap in evaluating the economic efficiency of intelligent HW completion using AICD relative to conventional HW completion in oil rims. [ABSTRACT FROM AUTHOR]

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. Controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP. [ABSTRACT FROM AUTHOR]

Traditional manufacturing workshop processes are challenging to change, and passive monitoring methods, such as quality traceability and completion rate monitoring, pose challenges for discrete workshops in achieving real-time and accurate production control. Therefore, combined with the digital twin system, a multi-level production status monitoring model is established to monitor discrete manufacturing workshops actively. This article focuses on discrete manufacturing workshops' modeling and self-evolution production processes. It also proposes an active monitoring architecture for production status in discrete workshops using digital twin technology. A full-information model is constructed for a discrete manufacturing workshop. Then, a multi-node interaction logic is established for the discrete manufacturing workshop. Finally, based on the fuzzy C-means clustering algorithm, production anomalies are monitored, and real-time monitoring of disturbances caused by various production factors, including material status, equipment status, and work-in-progress status, is performed to assess their impact on production. Based on deep Bayesian network learning algorithms, we can monitor production performance and analyze workshop capacity, equipment utilization, bottleneck rhythm, etc. This allows us to actively monitor production performance through the self-evolution of digital twin models. This article aims to proactively predict changes in workshop production status and provide support for further plan completion rate improvement, capacity prediction, and capacity optimization. [ABSTRACT FROM AUTHOR]

In order to achieve intelligent production and quality control, improve production efficiency and accuracy, the author proposes an application method of artificial intelligence technology in mechanical manufacturing and automation. The author aims to explore the specific application of artificial intelligence technology in the mechanical manufacturing industry and summarize the main advantages of automation technology. By analyzing these advantages, the aim is to provide targeted recommendations for future technological development. Meanwhile, the author aims to provide theoretical support for promoting the application of modern artificial intelligence technology and automation technology in the field of mechanical manufacturing. In the manufacturing process, intelligent production and quality control can be achieved, improving production efficiency and accuracy; In the field of automation, artificial intelligence technology can achieve intelligent control systems and autonomous decision-making, improving the flexibility and adaptability of production lines. Overall, the application of artificial intelligence technology has brought revolutionary changes to mechanical manufacturing, promoting the upgrading and development of the industry. [ABSTRACT FROM AUTHOR]

To bridge the gap in the research on the control and drive methods of the key equipment of the new subsea production control system, all-electric subsea gate valve actuator, and to solve the problems of the valve control and drive system in the traditional subsea production system, this paper proposed a full-featured and feasible electrical system of the all-electric subsea gate valve actuator containing the control system and the drive system. The key functions were realized, including status monitoring, redundant communication, redundant power supply, redundant drive, and low-power open-position holding. The electrical system is suitable for monitoring and controlling all-electric subsea gate valve actuators with various specifications and is highly integrated, open, efficient, and real-time. The electrical system prototype was developed and successfully tested for several functions. The results showed that the all-electric subsea gate valve actuator electrical system was capable of controlling and driving the actuator, monitoring the status information of the internal and external environment of the system, as well as the power output information of the drive system, and having redundancy features. [ABSTRACT FROM AUTHOR]

Sand production is one of the challenges facing the oil industry. This paper reviews the latest research advances in chemical sand control and aims to provide a reference for related researchers. Firstly, the commonly used chemical sand control materials are introduced. Second, recent advances in chemical sand control are reviewed, including materials, methods, and processes. Third, laboratory methods for sand control research are presented. Finally, the gaps and challenges of chemical sand control materials are pointed out, and possible future directions for chemical sand control methods are envisioned. [ABSTRACT FROM AUTHOR]

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing technology is required to upgrade existing enterprises' management and production operations. To construct a ground breaking fusion structure, this project unites the theoretical underpinnings, technical breakthroughs, and applications of data analytics, optimisation, and intelligent production engineering. It is driven by China's desire of cutting-edge commodities and efficient growth methods. This research establishes the broad framework for merging optimisation and data analytics. There is a list of data analytics and system optimisation technologies that can address important challenges with intelligent manufacturing. By integrating data analytics and optimisation, businesses may better forecasting and management of new terrain, as well as reveal hidden information to increase decision-making efficacy. [ABSTRACT FROM AUTHOR]

The study offers an analytical approach for assessing production control system performance in terms of volume and variety features of the product being manufactured for the flexible manufacturing system. Throughput, resource utilization, cycle time, and maximum completion time are the four performance indicators taken into account. The objective was to quantify the performance of the production system and define the reactive capability of production control architectures in a market that is becoming increasingly competitive. The examination of the production performances of control systems motivates the evaluation of these architectures through the introduction of scheduling approaches that deal with uncertainty. Results revealed the semi-heterarchical control architecture outperforms the hierarchical control architecture through multiple performance criteria. A case study with regard to a manufacturing control system was presented in order to highlight the significance of the adopted methodology and the contribution of the research. [ABSTRACT FROM AUTHOR]

Problem definition: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system's production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system's production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.17.708]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0473. [ABSTRACT FROM AUTHOR]

Copyright of Amazonia Investiga is the property of PRIMMATE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The efficiency of production planning and control is increased by the application of contemporary information and communication technologies. The influence of the existence of an information system on the entire functioning of a business system, as well as on the effectiveness of production planning and control, is generally known and does not need to be proven. The paper presents the effectiveness of production control of a particular product with the use of simulation software tools that are integrated with the production information system. The basic space of the condition of the problem which the management of planning and production control should solve in the case of the production of a certain product will be presented tabularly. [ABSTRACT FROM AUTHOR]

Schedule conformance depends on construction activities starting and finishing on time. However, construction activities are often delayed because construction flows necessary to start their execution are unavailable. Construction flows can be classified into: labor, equipment, workspace, materials, precedence, information, and external. Current construction models do not formally represent, measure, and track all flow types. Hence, field managers lack formal methods for managing flows and instead rely on their intuition and experience managing them. This paper presents a construction model based on activities and flows called the activity–flow model (AFM). The AFM formally represents the activities, the flows, and their interactions. The AFM was validated prospectively through its implementation on three building projects that were in different phases (foundations, core and shell, and finishing), at different geographic locations (Bogota, Copenhagen, and Lima), and used different planning and control methods (master schedule and weekly planning, last planner system, and location-based management system). The AFM was able to represent all the activities and flows in the test projects, track variations of activities and flows, and quantify activity and flow variability. Field managers can use the metrics enabled by the AFM to plan and control the project. Such proactive flow management can help field managers improve flow readiness, which should reduce activity delays and improve schedule conformance. [ABSTRACT FROM AUTHOR]

In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios. [ABSTRACT FROM AUTHOR]

The research project highlights the significance of incorporating reclaimed asphalt (RA) into hot mix asphalt (HMA) production for sustainable road construction. Despite limited RA utilization in Hungary in the past decades, the project demonstrates the feasibility of manufacturing HMA with significant RA content using advanced technology. The establishment of a capable asphalt plant and the development of laboratory assessments and mix design methodologies laid a solid foundation for future high RA integration in Hungarian road projects. Large-scale production trials confirmed the practicality of integrating high RA content into heavy-duty asphalt mixes with various binder types, including normal bitumen (B), polymer modified binder (PmB), and rubber modified bitumen (GmB). The large-scale validation project described in this paper was based on crucial binder blend designs carried out prior to the trials. Production control and performance-based laboratory testing proved that asphalt mixes can be designed and manufactured with high RA content while maintaining performance standards. Balancing resistance to distress modes like rutting and low-temperature cracking through careful binder blend design and mix design is achievable, even with high RA proportions. Visual assessments and production control indicated the uniformity of high RA content asphalt mixes. The details provided in this paper emphasise the potential for economic and environmental benefits through increased RA utilization in Hungarian road construction. [ABSTRACT FROM AUTHOR]

Copyright of Opuntia Brava is the property of Universidad de Ciencias Pedagogicas de Las Tunas, Centro de Documentacion e Informacion Pedagogica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Investigación e Innovación en Ingenierías is the property of Universidad Simon Bolivar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the current research presents a new methodology for multi-agent production control based on integration with ERP, which improves the capabilities of the system in the face of the above deficiencies. The research method employed in this study is qualitative, and developmental-applicative, aiming to enhance the integration of multi-agent production control systems with ERP. The objective is to improve the flow of material, production, and the quality of semi-finished products on the production line by considering the parameters that influence them. The key accomplishment of this research is the development of a reliable production control methodology that encompasses three components: a data exchange framework, tools, and implementation. These components are derived from existing ERP information systems that are functionally mature and designed based on best practices with a focus on maintenance, modification, and performance, aiming to minimize errors. The developed methodology offers a practical and agile solution for enhancing production control using an ERP system, with a lower implementation cost than the implementation of a commercial ERP system with a separate multi-agent system. [ABSTRACT FROM AUTHOR]

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses. In this study, we propose an automatic workflow based on machine learning (ML) to monitor seismicity in the southern Sichuan Basin of China. This workflow includes coherent event detection, phase picking, and earthquake location using three-component data from a seismic network. By combining PhaseNet, we develop an ML-based earthquake location model called PhaseLoc, to conduct real-time monitoring of the local seismicity. The approach allows us to use synthetic samples covering the entire study area to train PhaseLoc, addressing the problems of insufficient data samples, imbalanced data distribution, and unreliable labels when training with observed data. We apply the trained model to observed data recorded in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average differences in latitude, longitude, and depth are 5.7 km, 6.1 km, and 2 km, respectively, compared to the reference catalog. PhaseLoc combines all available phase information to make fast and reliable predictions, even if only a few phases are detected and picked. The proposed workflow may help real-time seismic monitoring in other regions as well. [ABSTRACT FROM AUTHOR]

Multi-agent systems (MASs) have gained a lot of interest recently, due to their ability to solve problems that are difficult or even impossible for an individual agent. However, an important procedure that needs attention in designing multi-agent systems, and consequently applications that utilize MASs, is achieving a fair agreement between the involved agents. Researchers try to prevent agreement manipulation by utilizing decentralized control and strategic voting. Moreover, emphasis is given to local decision making and perception of events occurring locally. This manuscript presents a novel secure decision-support algorithm in a multi-agent system that aims to ensure the system's robustness and credibility. The proposed consensus-based model can be applied to production planning and control, supply chain management, and product design and development. The algorithm considers an open system; i.e., the number of agents present can be variable in each procedure. While a group of agents can make different decisions during a task, the algorithm chooses one of these decisions in a way that is logical, safe, efficient, fast, and is not influenced by factors that might affect production. [ABSTRACT FROM AUTHOR]

Throughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as 'throughput bottlenecks'. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice. [ABSTRACT FROM AUTHOR]

We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed‐integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real‐time condition‐based control of the production rate through reoptimization. The model is further generalized to the nonlinear production–degradation relation. Based on a real production–degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented. [ABSTRACT FROM AUTHOR]

ABSTRACTThroughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.

The main goal of the studies was to collect information on the impact of the identified risk factors on the amount of costs incurred in the life cycle of buildings. The own studies were focused especially on residential and non-residential buildings. The studies consisted in obtaining expert opinions on the subject of the research involves in the non-random (arbitrary) selection of a sample of respondents from among specialists corresponding to the industry purpose of the studies and the research material. The research used the expert questionnaire method. The studies were divided into three stages. In the first stage, the identification and division of risk factors in the life cycle of buildings was performed. Then, experts assessed 45 selected risk factors that may affect the amount of costs incurred in the life cycle of buildings. In the last step, the research results were developed in the form of a checklist knowledge base, containing information about the potential impact of the identified risk factors in the life cycle of buildings on the amount of the corresponding components of life cycle costs. [ABSTRACT FROM AUTHOR]

In line with the principles of modern design a building structure should not only be safe but also optimized. In deterministic optimization, the uncertainties of the structures are not explicitly taken into account. Traditionally, uncertainties of the structural system (i.e. material parameters, loads, dimensions of the cross-sections) are considered by means of partial safety factors specified in design codes. Worth noticing, that optimal structures are sensitive to randomness design parameters and deterministic optimal solutions may lead to reduced reliability levels. It therefore seems natural to extend the formulation of deterministic optimization with the random scatter of parameter values. Such a formulation is offered by robust optimization and reliability-based design optimization. The applicability of RBDO is strongly dependent on the availability of the joint probability density function. A formulation of non-deterministic optimization that better adapts to the design realities is robust optimization. Unlike RBDO optimization, this formulation does not require estimation of failure probabilities. In the paper using the examples of steel beams, the authors compare the strengths and weaknesses of both formulations. [ABSTRACT FROM AUTHOR]

The construction contractor is concerned with reducing the cost of the project, including reducing unnecessary downtime. This is achieved when resources are fully utilized; this means the crews work continuously moving without interruption from one location to the other. However, any disturbance in the optimally scheduled workflow caused by random events is likely to result in delays, interruptions in the crews work, and productivity losses. There is therefore a need for scheduling methods that allow plans to be more resilient to disruptions and ensure a reduction in downtime and implementation costs. The authors put forward a proactive-reactive approach to the schedule risk management. Proposed method makes it possible to protect schedule deadlines from the impact of risk factors by allocating time buffers (proactive approach). It also takes into account the measures that managers take during execution in response to delays that occur, such as changing construction methods, employing extra resources, or working overtime (reactive approach). It combines both ideas and is based on project simulation technique. The merits of the proposed approach are illustrated by a case of a repetitive project to erect a number of buildings. The presented example proves that the proposed method enables the planner to estimate the scale of delays of processes' start and consider the impact of measures to reduce duration of processes in particular locations taken in reaction to delays. Thus, it is possible to determine the optimal schedule, at which the costs of losses associated with delays and downtime are minimal. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider a joint control of pricing and production in a two‐item inventory system, where each item can be produced by its dedicated source or a shared flexible source, under the lost‐sales setting. The demands of the two items are correlated. Due to the complexity caused by the lost‐sales assumption and the interplay between pricing and production decisions, we show the optimal value function satisfies five structural properties under certain conditions. The optimal ordering and pricing policies are then fully characterized by five production curves and two pricing functions. Different from existing literature, we show that the optimal price path may not be monotone in the inventory level of either item. Next, we estimate the value of flexibility by comparing two systems, one with the flexible source only and the other with the dedicated source only, and show that the value of flexibility is positive (or negative) if the flexible source has a faster (or slower) production rate and a lower (or higher) production cost. Through numerical experiments, we find that in most cases flexibility and dynamic pricing are substitutes except for a few exceptions; and product substitution can either increase or decrease the value of flexibility. [ABSTRACT FROM AUTHOR]

Analytical measurement methods are used in different areas of production and quality control, diagnostics, environmental monitoring, or in research applications. If direct inline or online measurement methods are not possible, the samples taken have to be processed offline in the manual laboratory. Automated processes are increasingly being used to enhance throughput and improve the quality of results. In contrast to bioscreening, the degree of automation in (bio)analytical laboratories is still low. This is due in particular to the complexity of the processes, the required process conditions, and the complex matrices of the samples. The requirements of the process to be automated itself and numerous other parameters influence the selection of a suitable automation concept. Different automation strategies can be used to automate (bio)analytical processes. Classically, liquid handler-based systems are used. For more complex processes, systems with central robots are used to transport samples and labware. With the development of new collaborative robots, there will also be the possibility of distributed automation systems in the future, which will enable even more flexible automation and use of all subsystems. The complexity of the systems increases with the complexity of the processes to be automated. [ABSTRACT FROM AUTHOR]

In actual dynamic system, uncertainty is absolute and certainty is relative. This paper presents the optimal dynamic pricing and production control strategy for perishable products in finite horizon. The influence of external environmental disturbance on the system is considered by means of a special uncertain process (Liu process). Then based on uncertainty theory and Hurwicz criterion, the optimization model is built, where control variables are restricted to an admissible control set. In addition, uncertain differential equation is used to describe the changes of inventory. By applying the optimality equation, we determine the optimal price and production strategy to maximize profit. Besides, both the optimal price and production rate are linearly decreasing with inventory. Afterwards, two numerical examples are given, the results reveal that reducing the uncertain disturbance of inventory and expanding the potential market size are beneficial to improving the optimal profit. Moreover, risk-loving decision makers can gain more profits while facing large risks. [ABSTRACT FROM AUTHOR]

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In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection ofmanufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UAbased approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation. [ABSTRACT FROM AUTHOR]

There is no denying smart manufacturing is a critical step in responding to a new round of energy crises and promoting the high-quality development of the manufacturing industry, among which, the construction of intelligent production lines is the key link. In the practical production of complex aerospace components, production scheduling optimization plays an important role in achieving cost savings and energy reduction for a range of existing problems, such as cumbersome process design, difficult real-time scheduling adjustment, inefficient quality data testing, and complex interrelationships of state-type data. In this work, the optimization of scheduling objective, the selection of scheduling method, and the construction of scheduling management control system are the pointcuts to review the recent development of production scheduling optimization, systematically. The research on the more practical implications of multi-objective production scheduling optimization has shown that efficiency and energy consumption are the primary priorities of scheduling objectives. Scheduling rules and heuristic algorithms are the crucial research methods. Intelligent information technologies are an effective means to decrease the complexity of scheduling. Meanwhile, building intelligent management control systems for production scheduling is of great significance for the transformation and upgrading of production from digitalization to intelligence. [ABSTRACT FROM AUTHOR]

The rapid industrial changes triggered by the fourth industrial revolution or I4.0 have ensued a dynamic and integrative perspective in production planning and control (PPC) decisions. Prior research on PPC transformation in I4.0 environment is in the form of computational experiments to solve sector-specific issues, with little emphasis on how PPC is influenced by the evolving digital capabilities, and how it operates in the new manufacturing paradigm? This study aims to develop an analytical framework to consolidate the prevailing research on PPC in I4.0 and identify future research possibilities. We used morphological analysis (MA) approach for reviewing 104 studies identified through a systematic review protocol. The MA framework is created with five dimensions (i.e., study setting, PPC functions, enabling technologies, digital capabilities, and core performance outcomes) defined based on input-process-outcome (IPO) approach of system analysis. This study is possibly the first to use MA for reviewing the PPC literature, presenting practitioner and theoretical contributions. The MA framework acts as a ready reckoner of the literature and its modular representation has the flexibility to be modified or augmented as the literature evolves. [ABSTRACT FROM AUTHOR]

Accelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the current research presents a new methodology for multi-agent production control based on integration with ERP, which improves the capabilities of the system in the face of the above deficiencies. The research method employed in this study is qualitative, and developmental-applicative, aiming to enhance the integration of multi-agent production control systems with ERP. The objective is to improve the flow of material, production, and the quality of semi-finished products on the production line by considering the parameters that influence them. The key accomplishment of this research is the development of a reliable production control methodology that encompasses three components: a data exchange framework, tools, and implementation. These components are derived from existing ERP information systems that are functionally mature and designed based on best practices with a focus on maintenance, modification, and performance, aiming to minimize errors. The developed methodology offers a practical and agile solution for enhancing production control using an ERP system, with a lower implementation cost than the implementation of a commercial ERP system with a separate multi-agent system.

The article presents a proposal for optimizing the production process of corrugated cardboard based on measurements of process variables as well as the knowledge and skills of the operator conducting production. This technique involves continuous recording and analysis of process quantities that affect the quality of the produced cardboard. For this purpose, a network of temperature sensors with a system of continuous registration and monitoring of the process variables was designed and installed in the industrial environment of the corrugator. The recorded data is analyzed to estimate the desired values of the measured process variables, giving clues to how to control the production line. Unlike existing systems, the proposed algorithm for controlling production parameters allows each operator to use their preferred values for process variables independently of others. The proposed system allows for improving the quality of the produced cardboard and increasing the efficiency of its production by taking into account the individual experience and habits of the operator conducting production. [ABSTRACT FROM AUTHOR]

The molecular factors and genetic adaptations that contributed to the emergence of Mycobacterium tuberculosis (MTB) from an environmental Mycobacterium canettii-like ancestor, remain poorly investigated. In MTB, the PhoPR two-component regulatory system controls production and secretion of proteins and lipid virulence effectors. Here, we describe that several mutations, present in phoR of M. canettii relative to MTB, impact the expression of the PhoP regulon and the pathogenicity of the strains. First, we establish a molecular model of PhoR and show that some substitutions found in PhoR of M. canettii are likely to impact the structure and activity of this protein. Second, we show that STB-K, the most attenuated available M. canettii strain, displays lower expression of PhoP-induced genes than MTB. Third, we demonstrate that genetic swapping of the phoPR allele from STB-K with the ortholog from MTB H37Rv enhances expression of PhoP-controlled functions and the capacities of the recombinant strain to colonize human macrophages, the MTB target cells, as well as to cause disease in several mouse infection models. Fourth, we extended these observations to other M. canettii strains and confirm that PhoP-controlled functions are expressed at lower levels in most M. canettii strains than in M. tuberculosis. Our findings suggest that distinct PhoR variants have been selected during the evolution of tuberculosis bacilli, contributing to higher pathogenicity and persistence of MTB in the mammalian host. Author summary: Mycobacterium tuberculosis (MTB) likely emerged from an environmental ancestor by adaptation of endogenous pathways enhancing its capacity to colonize and transmit within the human host. The closest extant relatives of the MTB ancestor are the Mycobacterium canettii strains which are opportunist pathogens responsible for rare human tuberculosis cases in East-Africa. Here we found that the sensor protein of the important two-component regulatory system PhoPR displays amino acid substitutions that modulate its activity in M. canettii in comparison to MTB. These mutations impact the production and secretion of virulence factors important for multiplication within the host and cough induction. Therefore, our findings support the hypothesis that variants of the PhoPR regulatory system were selected as key evolutionary adaptations for enhancing the epidemic capacity of the tuberculosis bacilli. [ABSTRACT FROM AUTHOR]

Summary: During photoperiodic growth, the light‐dependent nature of chlorophyll synthesis in angiosperms necessitates robust control of the production of 5‐aminolevulinic acid (ALA), the rate‐limiting step in the initial stage of tetrapyrrole biosynthesis (TBS). We are interested in dissecting the post‐translational control of this process, which suppresses ALA synthesis for chlorophyll synthesis in dark‐grown plants.Using biochemical approaches for analysis of Arabidopsis wild‐type (WT) and mutant lines as well as complementation lines, we show that the heme‐synthesizing ferrochelatase 2 (FC2) interacts with protochlorophyllide oxidoreductase and the regulator FLU which both promote the feedback‐controlled suppression of ALA synthesis by inactivation of glutamyl‐tRNA reductase, thus preventing excessive accumulation of potentially deleterious tetrapyrrole intermediates.Thereby, FC2 stabilizes POR by physical interaction. When the interaction between FC2 and POR is perturbed, suppression of ALA synthesis is attenuated and photoreactive protochlorophyllide accumulates. FC2 is anchored in the thylakoid membrane via its membrane‐spanning CAB (chlorophyll‐a‐binding) domain.FC2 is one of the two isoforms of ferrochelatase catalyzing the last step of heme synthesis. Although FC2 belongs to the heme‐synthesizing branch of TBS, its interaction with POR potentiates the effects of the GluTR‐inactivation complex on the chlorophyll‐synthesizing branch and ensures reciprocal control of chlorophyll and heme synthesis. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a basic step in the upgrade of manufacturing companies into digital enterprises. It allows more flexibility in manufacturing, as well as mass customization, provides better quality, and enhances productivity. As a result, it empowers companies to meet the challenges of smart manufacturing which is increasingly individualizing products with shorter time-to-market span and improving quality. Smart manufacturing has an important part in Industry 4.0. This study aims to characterize and analyze a smart manufacturing process designed for a company specialized in the production of brass accessories: spherical bushels. We basically set up a simulation tool to develop a numerical production platform for Industry 4.0 which is able to efficiently operate and manage the production and procurement through material requirement planning (MRP, master production program), logistics warehouse, and cyber-physical production system (CPPS). The findings have been optimized by a new redesigned approach of MRP 2: load-capacity adjustment for smart workshop and Industry 4.0 manufacturing planning. It is the setup of an integrated manufacturing system process, which resulted in reducing the spherical bushels' assembly time and controlling the production and the assembling process. It allowed us to increase the equipment utilization rate by comparing it with the company's former equipment running time prior to switching to smart manufacturing. The proposed model proves that we can successfully optimize the practical application of Industry 4.0 and therefore significantly increase the production efficiency. We are confident that this is the first work addressing the implementation of a simulation platform controlled by a dedicated cyber-physical production system (CPPS) and a master production program (MPP). A case study of a company manufacturing brass accessories is presented in this paper. The developed simulation platform offers the possibility to create a future digital twin of the company. [ABSTRACT FROM AUTHOR]

The agronomic potential of glutamate dehydrogenase 2 (GDH2) in maize kernel production was investigated by examining the impact of a mutation on the corresponding gene. Mu-insertion homozygous and heterozygous mutant lines lacking GDH2 activity were isolated and characterized at the biochemical, physiological and agronomic levels. In comparison to the wild type and to the homozygous ghd2 mutants, the heterozygous gdh2 mutant plants were characterized by a decrease in the root amino acid content, whereas in the leaves an increase of a number of phenolic compounds was observed. On average, a 30 to 40% increase in kernel yield was obtained only in the heterozygous gdh2 mutant lines when plants were grown in the field over two years. The importance of GDH2 in the control of plant productivity is discussed in relation to the physiological impact of the mutation on amino acid content, with primary carbon metabolism mostly occurring in the roots and secondary metabolism occurring in the leaves. [ABSTRACT FROM AUTHOR]

Panax notoginseng is one of the most valuable traditional Chinese herbs. The main active ingredients, dammarane-type ginsenosides, show multiple pharmacological activities. Recently, the key UDP-dependent glycosyltransferases (UGTs) involved in the biosynthesis of common ginsenosides have been widely studied. However, only a few UGTs that catalyze ginsenoside formation have been reported. This study further investigated the new catalytic function of 10 characterized UGTs from the public database. PnUGT31(PnUGT94B2) and PnUGT53 (PnUGT71B8)exhibited promiscuous sugar-donor specificity of UDP-glucose and UDP-xylose, which could catalyze the glycosylation of C20-OH sites and elongation of the sugar chain at the C3 and/or C20 sites. We further analyzed the expression patterns in P. notoginseng and predicted the catalytic mechanisms of PnUGT31 and PnUGT53 using molecular docking simulations. Moreover, different gene modules were built to increase the yield of ginsenosides in engineered yeast. The metabolic flow of the proginsenediol (PPD) synthetic pathway was enhanced by LPPDS gene modules based on the engineered strain. The resulting yeast was constructed to produce 1.72 g/L PPD in a shaking flask, but cell growth was significantly inhibited. EGH and LKG gene modules were constructed to achieve high-level production of dammarane-type ginsenosides. The production of G-Rg3 controlled by LKG modules increased 3.84 times (254.07 mg/L), whereas the G-Rd titer reached 56.68 mg/L after 96 h in shaking flask culture under the control of all modules, both of which yielded the highest values for known microbes. [ABSTRACT FROM AUTHOR]

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model's robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves. [ABSTRACT FROM AUTHOR]