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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 Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

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.

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) 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.)

Purpose: The purpose of the article is to locate a bottleneck in a printing production system. Design/methodology/approach: The study is conducted through a case study utilizing bottleneck detection methods based on processes cycle times. Findings: The study conducted reveals the problem that finding a bottleneck is not a simple task, and the methods used may point to another bottleneck. The study implies that not every method used identified the same process as the bottleneck of the production system under study. Research limitations/implications: The study describes a case of a single manufacturing company from the printing industry. The results of the research should be regarded as a pilot study. Practical implications: The results of the survey are of particular importance to managers in charge of production management, providing valuable insights and pointing out areas that require special attention and monitoring. Originality/value: In the TOC, the first step is to identify the bottleneck. The study shows that identifying a bottleneck in a production process is not an easy task. Different methods may indicate different bottlenecks. [ABSTRACT FROM AUTHOR]

The design and operation of modern software systems exhibit a shift towards virtualization, containerization and service-based orchestration. Performance capacity engineering and resource utilization tuning become priority requirements in such environments. Measurement-based performance evaluation is the cornerstone of capacity engineering and designing for performance. Moreover, the increasing complexity of systems necessitates rigorous performance analysis approaches. However, empirical performance analysis lacks sophisticated model-based support similar to the functional design of the system. The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps. Hyperledger Fabric (HLF), an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach, using the standard TPC-C performance benchmark workload. [ABSTRACT FROM AUTHOR]

In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not available in advance. Therefore, we model this task as a combinatorial semi-bandit problem to which we apply a combinatorial version of Thompson Sampling and establish an upper bound on the corresponding Bayesian regret. Due to the computational intractability of the problem, we then devise an alternative problem formulation which approximates the original objective. Finally, we experimentally evaluate the performance of Thompson Sampling with the approximate formulation on real-world directed and undirected networks. [ABSTRACT FROM AUTHOR]

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

For TOC (Theory of Constraints) implementation in a production system, the determination of the system's bottleneck is a crucial step. Effective bottleneck identification allows setting priorities for the improvement of a production system. The article deals with a significant problem for the manufacturing industry related to the location of a bottleneck. The article aims for a detailed analysis of methods for bottleneck identification based on a comprehensive literature review and the design of a generalised methodology for bottleneck identification in the production system. The article uses two research methods, first, the combination of a narrative and scoping literature review, and second, the logical design. Several methods for bottleneck identification are reviewed and compared, finding some being similar, and others giving new insights into the evaluated production system. A methodology for bottleneck identification is proposed. It contains several detailed methods arranged in coherent steps, which are suggested to be followed when aiming for the recognition of a production system's bottleneck. The proposed methodology is expected to be helpful in the practical TOC implementation. The presented methodology for the identification of bottlenecks in a production system is a practical tool for managers and experts dealing with TOC. However, it is still a conceptual proposal that needs to be tested empirically. The proposed methodology for bottleneck identification is an original concept based on the current literature output. It contributes to the production management theory as a practical managerial tool.

Copyright of Journal of Central South University is the property of Springer Nature 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 21st Century Maritime Silk Road (MSR) initiated by China involves the construction of a new form of regional economic cooperation between China and the associated countries. Priority is given to eliminating maritime bottlenecks and improving maritime connectivity in the shipping network along the MSR. However, such potential bottlenecks have rarely been quantitatively identified. In this paper, we present a recursive spectral bi-partitioning method to detect the bottlenecks in the container shipping network along the MSR. This method identifies the bottlenecks by locating the cuts with the least total link frequency, normalised by the size of the port communities on either side of the cut. The results of the case study show that the proposed method performs well. Four bottlenecks with strategic geographical positions along the key trade lanes are found, namely: Suez Canal, Cape of Good Hope, northern Oceania, and the South China Sea, indicating locations in need of connectivity improvement. [ABSTRACT FROM AUTHOR]

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method. [ABSTRACT FROM AUTHOR]

The identification and alleviation of bottleneck machines in manufacturing systems are of paramount importance for optimizing production decision-making in enterprises. Extensive research has been conducted by numerous scholars on the identification of bottleneck machines; however, there is a relative scarcity of research, particularly in the context of flexible manufacturing systems, regarding how to effectively alleviate the identified bottlenecks. In this paper, a novel hyper-heuristic evolutionary scheduling algorithm with a bottleneck alleviation strategy based on the information-energy flow model (HESA-BA-IEF) is proposed for training dispatching rules and optimizing decision-making in flexible job shops with bottleneck machines. HESA-BA-IEF constructs an information-energy flow model for flexible job shops based on complex networks. Through this model, dynamic changes in machine processing status and energy consumption during the scheduling process can be effectively simulated, facilitating the accurate identification of bottleneck machines. Moreover, the algorithm adopts the genetic programming hyper-heuristics and introduces a scheduling mechanism considering the alleviation of bottleneck machines to optimize the makespan and total energy consumption, while simultaneously alleviating bottlenecks. Experiments show that HESA-BA-IEF generates dispatching rules that optimize scheduling and alleviate bottlenecks simultaneously. Furthermore, statistical analysis reveals a 7.71% reduction in bottleneck metrics of production line due to 30.40% scheduling rounds with bottleneck alleviation mechanism in effect. [ABSTRACT FROM AUTHOR]

Metro network connectivity is crucial for ensuring reliable operation of metro systems. Despite the rich literature on the connectivity analysis of transportation network, very little attention has been paid to passengers’ heterogeneous cognition toward congestion and connectivity incorporating subjective judgment. In this paper, we develop a data-driven framework to analyze metro network connectivity evolution involving individual cognition by characterizing it as a transit percolation process. The concept of individual tolerance index of congestion and a measure named network friendliness are proposed. By comparing individual tolerance index and friendliness of metro network, metro network connectivity with regard to different passengers can be depicted quantitatively. The evolution of network connectivity can be monitored both as individual tolerance changes and as time goes on. We also demonstrate how global transit breaks down when the identified bottlenecks are congested from the perspective of the passengers’ cognition. The proposed method is validated using a real-world case of the Shenzhen Metro in China. Results show that the proposed method is effective in capturing the dynamic evolution of the Shenzhen metro network connectivity and enable effective identification of transit bottlenecks. The network connectivity and friendliness are found to be significantly increased through a small improvement of the bottlenecks pinpointed.

Mining has a long history of production and operations management. Considering the increasing complexity of reserves, declining metal grades, and worldwide price volatilities, mines have to operate more efficiently than ever. In recent years, companies have been adopting innovative solutions and technologies to reduce costs and remain competitive in the global market. Specifically, management of bottlenecks has been in the focus of the manufacturing industry for decades now. The mining industry has made significant advancements in process improvement tools using equipment, human, and asset tracking capabilities. However, to date, there is no holistic approach or system to identify and manage the bottleneck in the entire value chain of mining. This research proposes a new method to identify and rank the bottlenecks in a mine value chain. The method is applied in a coal mine and the results are presented in the case study. [ABSTRACT FROM AUTHOR]

Aiming at the gap between theoretical research and practical application in the production bottleneck field, we apply five bottleneck identification methods in a serial production line in aerospace industry based on discrete event simulation and Plant Simulation software, meanwhile discuss the influence of the bottleneck machine quantity on the system performance. This paper evaluated the practicability, accuracy and limitation of various bottleneck identification methods at the practical level. The results of the bottleneck alleviation manifest that increasing the number of bottleneck machines can effectively improve the system performance, but the more machine quantity, the smaller performance improvement. More importantly, the paper studies the influence mechanism and function relationship of the bottleneck machine quantity on the maximum completion time from an interesting actual phenomenon for the first time. The function obtains the condition that the maximum completion time achieve the minimum. The research and conclusion of this paper have essential reference significance for production guidance and theoretical research, and can also contribute to narrow the gap between theory and application of the production bottleneck field. [ABSTRACT FROM AUTHOR]

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method. [ABSTRACT FROM AUTHOR]

A crowd is a gathering of a huge number of individuals in a confined area. Early identification and detection of unusual behaviors in terms of panic occurring in crowded scenes are very important. Panic detection comprises of formulating normal scene behaviors and detecting and identifying non-matching behaviors. However, panic detection and recognition is a very difficult problem, especially when considering diverse scenes. Many methods proposed to cope with these problems have limited robustness as the density of the crowd varies. In order to handle this challenge, this paper proposes the integration of different features into a unified model. Discriminant binary patterns and neighborhood information are used to model complex and unique motion patterns in order to characterize different levels of features for diverse types of crowd scenes, focusing in particular on the detection of panic and nonpedestrian entities. The proposed method was evaluated considering two benchmark datasets and outperformed five existing methods. [ABSTRACT FROM AUTHOR]

A reasonable and efficient scheduling strategy is the key to improving the shop management level of the manufacturing enterprises. It can not only maximize the current resource capacity, but also greatly increase the production capacity of the enterprise. Job shop scheduling problem (JSSP) is one of the most representative scheduling problems and one of the most difficult combinatorial optimization problems. The key development direction in this field is to study the production bottleneck problem while optimizing and upgrading the production combination. This paper proposes an improved meta-heuristic algorithm to minimize maximum completion time and average flow time in the manufacturing industry. For this algorithm, a novel two-way decoding method was first adopted in the improved algorithm, and then, a parallel search process based on global search and local search was designed; finally, two different functions, namely receive functions and influence functions were designed according to the bottleneck identification and Pareto selection strategy in the production scheduling. The research results show that the proposed meta-heuristic algorithm has obvious advantages in stability, distribution, optimal value and calculation time. The research findings provide a new theoretical basis for further effectively improving the JSSP problem in the manufacturing industry while considering the bottleneck identification problems. [ABSTRACT FROM AUTHOR]

The majority of energy used by the European Union has been imported from non-EU countries. The EU desires to increase its own renewable energy use to secure its future energy supply. In this paper, an assessment framework of technology dependence has been proposed that can be used to locate bottlenecks in the value chain of geothermal power generation. The framework consists of an 'above ground' and 'underground' domain. It was applied to binary Organic Rankine Cycle (ORC) power plants as this type has the highest proliferation potential in the EU. The above ground domain aims to locate potential bottlenecks at a key-component level via a technology hierarchy analysis, company and trade analysis as well as a survey. The underground domain focuses on the potential bottlenecks embedded in the geothermal drilling industry by means of a drilling industry screening, rig screening and a survey. The results suggest that some Binary-ORC key-components may require attention. Furthermore, the geothermal drilling industry's dependence on the oil and gas industry can be seen as a major dependence bottleneck that might jeopardize the future proliferation of binary-ORC technologies in the EU. • An assessment framework of technology dependence for geothermal power is proposed. • The framework is applied to the binary Organic Rankine Cycle plants in the EU. • Some key-components are the potential bottleneck for geothermal power proliferation. • Dependence of geothermal drilling rigs on the oil/gas sector is the main bottleneck. [ABSTRACT FROM AUTHOR]

Identifying bottlenecks and analyzing their characteristics are important tasks to city traffic management authorities. Although the speed difference was proposed for the bottleneck identification in the existing research, the use of a secondary indicator has not been fully discussed. This paper strived to develop a method to identify the bottleneck on expressways by using the massive floating car data (FCD) in Beijing. First, the speed characteristics of bottlenecks on expressway were analyzed based on the speed contour map. The results indicated that there was a significant difference between speeds on the bottleneck and downstream links when a bottleneck was observed. The speed difference could indeed be used as the primary indicator to identify the bottleneck. However, it was also shown that a sufficiently large speed difference does not necessitate an activation of a bottleneck. The speed-at-capacity was then used as the secondary indicator to distinguish the real bottleneck from the non-bottleneck speed difference. Second, a practical method for identifying the bottleneck on expressways was developed based on the speed difference and the speed-at-capacity. Finally, the method was applied to identifying the bottlenecks of the 3rd Outer Ring Expressway in Beijing. The duration, affected distance, delay and cause were used to evaluate and analyze the bottlenecks. [ABSTRACT FROM AUTHOR]

Various types of systems across a broad range of disciplines contain tandem queues with nested sessions. Strong dependence between the servers has proved to make such networks complicated and difficult to study. Exact analysis is in most of the cases intractable. Moreover, even when performance metrics such as the saturation throughput and the utilization rates of the servers are known, determining the limiting factor of such a network can be far from trivial. In our work, we present a simple, tractable and nevertheless relatively accurate method for approximating the above mentioned performance measurements for any server in a given network. In addition, we propose an extension to the intuitive “slowest server rule” for identification of the bottleneck, and show through extensive numerical experiments that this method works very well. [ABSTRACT FROM AUTHOR]

This paper attempts to overcome the defects of bottleneck recognition and scheduling optimization in open production line. For this purpose, the author analysed the impact mechanism of external disturbances and system configuration changes on the production line, and put forward a multibottleneck identification model for production line through computer simulation. Then, the proposed model was applied to optimize the scheduling of open production line. Specifically, the bottlenecks of production line were identified based on hierarchical clustering and multi-attribute decision-making, aiming to overcome the small candidate set and low accuracy of traditional bottleneck identification algorithms. The measured results show that the proposed algorithm has clear primary and secondary logics; the number of main bottleneck clusters decreased with the increase in the order; the number of machines in the main bottleneck cluster changed nonlinearly. The traditional genetic algorithm (GA) was improved in three aspects: the local optimum trap was avoided by enhancing population diversity; the iteration speed was accelerated with the introduction of adaptive crossover operator and genetic operator; without sacrificing the computing speed, the convergence quality was guaranteed through the addition of multivariate competition algorithm. The research findings provide new insights into the efficient operation of production line. [ABSTRACT FROM AUTHOR]

Manufacturing systems are constrained by one or more bottlenecks. Reducing bottlenecks improves the entire system. Finding bottlenecks, however, is a difficult task. In this study, a new bottleneck detection method based on theory of constrains and sensitivity analysis is presented to overcome the disadvantages of existing bottleneck identification methods for a job shop. First, a bottleneck index matrix is obtained by examining the sensitivity of system production performance to the capacity of each machine. Technique for order preference by similarity to ideal solution is then employed to calculate the comprehensive bottleneck index of each machine. Based on the calculation result, bottleneck machine clusters under different hierarchies are obtained through hierarchical cluster analysis. The designed identification approach, as a prior-to-run method, can identify bottleneck machine clusters under different hierarchies before the overall system circulation, thereby providing good guidance for subsequent production optimization. Finally, a set of job-shop scheduling problem benchmarks with different scales is selected for comparison between the proposed approach and existing approaches, such as, the shifting bottleneck detection method, the bottleneck detection method based on orthogonal experiment, and the bottleneck cluster identification method. By comparison, the proposed approach is proven to be credible and superior. [ABSTRACT FROM AUTHOR]

Agility has become a manufacturing paradigm of the 21st century that numerous firms have adopted it as a competitive strategy. Not surprisingly, there is a comprehensive body of research conducted on this subject. However, little study has been done for one-of-a-kind manufacturing where resource requirements are not available until well into the manufacturing process. In addition, there are other uncertain factors such as the changing production lead-time, making the environment dynamic and hard to predict. To address this issue, we conduct an action research study to enable an agile response to the factory dynamics of the subject environment. Our solution is a novel integration of four widely used technologies: radio frequency identification (RFID), simulation, bottleneck identification methods and the theory of constraints (TOC). Using numerical industry data, we illustrate how this multiple-tool-set approach is effective in achieving agility in a real one-of-akind manufacturing facility. With minor modifications, our solution can also be used in other manufacturing environments. [ABSTRACT FROM AUTHOR]

Drawing the strengths of data science and machine learning, process mining has recently emerged as an effective research approach for process management and its decision support. Bottleneck identification and analysis is a key problem in process mining which is considered a critical component for process improvement. While previous studies focusing on bottlenecks have been reported, visible gaps remain. Most of these studies considered bottleneck identification from local perspectives by quantitative metrics, such as machine operation and resource requirement, which can not be applied to knowledge-intensive processes. Moreover, the root cause of such bottlenecks has not been given enough attention, which limits the impact of process optimisation. This paper proposes an approach that utilises fusion-based clustering and hyperbolic neural network-based knowledge graph embedding for bottleneck identification and root cause analysis. Firstly, a fusion-based clustering is proposed to identify bottlenecks automatically from a global perspective, where the execution frequency of each stage at different periods is calculated to reveal the abnormal stage. Secondly, a process knowledge graph representing tasks, organisations, workforce and relation features as hierarchical and logical patterns is established. Finally, a hyperbolic cluster-based community detection mechanism is researched, based on the process knowledge graph embedding trained by a hyperbolic neural network, to analyse the root cause from a process perspective. Experimental studies using real-world data collected from a multidisciplinary design project revealed the merits of the proposed approach. The execution of the proposed approach is not limited to event logs; it can automatically identify bottlenecks without local quantitative metrics and analyse the causes from a process perspective. [ABSTRACT FROM AUTHOR]

Cytochrome P450s are interesting biocatalysts due to their ability to hydroxylate non-activated hydrocarbons in a selective manner. However, to date only a few P450-catalyzed processes have been implemented in industry due to the difficulty of developing economically feasible processes. In this study, we have used the CYP153A heme domain from Marinobacter aquaeolei fused to the reductase domain of CYP102A1 from Bacillus megaterium (BM3) expressed in Escherichia coli. This self-sufficient protein chimera CYP153A-CPR G307A mutant is able to selectively hydroxylate medium and long chain length fatty acids at the terminal position. ω-Hydroxylated fatty acids can be used in the field of high-end polymers and in the cosmetic and fragrance industry. Here, we have identified the limitations for implementation of a whole-cell P450-catalyzed reaction by characterizing the chosen biocatalyst as well as the reaction system. Despite a well-studied whole-cell P450 catalyst, low activity and poor stability of the artificial fusion construct are the main identified limitations to reach sufficient biocatalyst yield (mass of product/mass of biocatalyst) and space-time yield (volumetric productivity) essential for an economically feasible process. Substrate and product inhibition are also challenges that need to be addressed, and the application of solid substrate is shown to be a promising option to improve the process. [ABSTRACT FROM AUTHOR]

Under scenario of sports events and other kinds of massive gathering, a large amount of people would travel along road networks, thus forming crowded pedestrians. The high density situation might induce in mass panic and stampede, causing serious injuries and casualties. Therefore, it is necessary to firstly understand pedestrian flow features on the corresponding network and then apply proper control method to organize and manage pedestrian flow. Firstly, considering general characteristics of travelers' route choice behavior, all these pedestrians are dynamically assigned onto the road network via a traffic assignment model. Secondly, a nonlinear pedestrian road impedance function is introduced to address the congestion effects of massive crowds, and the pedestrian travel time changing rate is calculated for each link under various traffic volumes. It is proposed to sort the networks links according to the travel time changing rate. The rank is thus defined as a measurement of the structural importance of each link. Further, the structural importance is used to determine link robustness under different pedestrian traffic demand. Combing the result of structural importance and link robustness, bottleneck of the crowd circulating network can be identified. Finally, the Love Parade 2010 in Duisburg (an electronic music festival) is adopted as a case to illustrate the applicability of the proposed network bottleneck identification method. The threshold of pedestrian volume on the network and the effect of road control strategy are further investigated, the obtained basic data and theoretical results can benefit scientific planning and management of massive crowd road traffic. [ABSTRACT FROM AUTHOR]

Aiming at the problems of bottleneck identification using complicated models, single performance indicators and hard to operated in some studies, the different kinds of performance indicators, and how to simplified the model were researched. Then multiple performance indicators and fuzzy inference system were used to propose a comprehensive bottleneck identification method. The factors that caused the bot-tleneck occurred were fully considered, and multiple corresponding performance indicators were defined, AHP was used to give them proper weight, as the fuzzy inference system is not depend on object’s accurate model, so it was introduced to establish a comprehensive bottleneck identification model, calculated values of comprehensive bottleneck index. At last Witness simulation was carried out with an example. The results indicate that this method can identify the bottleneck of product system more accurate and more simple, can provide theoretical refer-ence for production management and control of the enterprise under the fuzzy information. [ABSTRACT FROM AUTHOR]

ABSTRACT System of systems are of high complexity, and for each system, many different requirements are implemented in parallel. Systems are developed with some degree of managerial independence but later on have to work together. In this situation, many requirements are written, implemented, and tested in parallel for different systems that are to be integrated. This makes identifying bottlenecks challenging, and visualizations often used on project level (such as Kanban boards or burndown charts) have to be extended/complemented to cope with the increased complexity. In response to these challenges, the contributions of this study are to propose the following: (i) a visualization for early identification and proactive removal of bottlenecks; (ii) a visualization to check on the success of bottleneck resolution; and (iii) to provide an industry evaluation of the visualizations in a case study of a system of systems developed at Ericsson AB in Sweden. The feedback by the practitioners showed that the visualizations were perceived as useful in improving throughput and lead time. The quantitative analysis showed that the visualizations were able in identifying bottlenecks and showing improvements or the lack thereof. On the basis of the qualitative and quantitative data collected, we conclude that the visualizations are useful in bottleneck identification and resolution. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion. [ABSTRACT FROM AUTHOR]

Abstract: Production systems engineering (PSE) is an emerging branch of engineering intended to uncover fundamental principles of production systems and utilize them for analysis, continuous improvement, and design. In PSE, manufacturing systems are addressed based on first principles and at the same level of rigor as that in other engineering disciplines. This paper is intended to give a general characterization of PSE and consider, in some details, several PSE problems. Specifically, the problems of performance analysis, bottlenecks, leanness, system-theoretic properties, and transient behavior are addressed. In addition, several case studies are described and the PSE Toolbox, which implements the techniques and algorithms developed, is presented. [Copyright &y& Elsevier]

The bottleneck (BN) of a production system is a machine with the strongest effect on the system's throughput. In this paper, a method for BN identification in serial lines with rework and Bernoulli machines is developed. The method can be applied using either calculated or measured data on blockages and starvations of the machines. For the case of calculated data, a technique for evaluating performance measures of Bernoulli lines with rework is developed. Along with these quantitative contributions, the paper provides three qualitative results. First, it shows that Bernoulli lines with rework do not observe the property of reversibility. Second, it demonstrates that downstream machines may have a larger effect on the throughput than upstream ones. Third, it demonstrates that BNs may be shifting not only because of changes in machine and buffer parameters but also due to changes in quality of parts produced. [ABSTRACT FROM AUTHOR]

In closed production lines, each part is placed on a carrier at the input of the first machine and is removed from the carrier at the output of the last machine. The first machine is starved if no carriers are available, and the last machine is blocked if the empty carrier buffer is full. The number of carriers in the system is S and the capacity of the empty carrier buffer is No. Under the assumption that the machines obey the Bernoulli reliability model, this paper provides methods for determining if a pair (No, S) impedes the open line performance and, if it does, develops techniques for improvement with respect to S and No. In addition, bottlenecks in closed lines are discussed, and an approach to selecting the smallest No and S, which result in no impediment, is described. Note to Practitioners--In closed production lines, parts are transported throughout the system on pallets. Clearly, this could impede the system performance since the first operation may be starved for pallets and the last may be blocked by full empty pallets buffer. Therefore, it is important to establish conditions on the number of pallets and the capacity of the empty pallets buffer, under which the impediment does not take place. This paper provides such conditions under the assumption that the operations obey a simple (Bernoulli) reliability model. In addition, this paper provides a method for identification of bottlenecks in closed lines. Future work will extend these results to systems with exponential and non-Markovian reliability models. [ABSTRACT FROM AUTHOR]