Bottleneck Identification Method Research Articles

Digital Twin-based bottleneck prediction for improved production control

Bottlenecks in manufacturing systems may significantly reduce their efficiency and productivity. Therefore, bottleneck analysis is a consolidated topic in Industrial Engineering, both in research and practice. Recently, traditional methods for bottleneck analysis have been enhanced with data-driven approaches, such as artificial intelligence and big data analytics. Nevertheless, their exploitation built on the full scope of technologies from digitalization is still not fulfilled. Indeed, the integration with simulation-based methods remains under-explored. This work aims to address bottleneck prediction leveraging on Digital Twin simulation capabilities to predict manufacturing system behavior. For this purpose, the work first offers an extensive review of bottleneck identification methods, inclusive of the ones based on Digital Twin. The main contribution of the work lies in the proposal of a novel Digital Twin-based bottleneck prediction framework with the end purpose to achieve performance improvements actuated through production control. The framework utilizes the Digital Twin for predicting and mitigating bottlenecks in manufacturing systems. The Digital Twin enables the simulation of the future system behavior, while accounting for the current conditions. This insight can then be used by a bottleneck identification method to infer future system bottleneck. The information on the predicted bottleneck is eventually used to support production control decisions, by adapting the order release and sequencing according to the predicted bottleneck. The benefits of adapting production control to the predicted bottleneck are evaluated quantitatively, highlighting how system performance is enhanced. By doing so, this research contributes to bridging the gap between Digital Twin-based performance analysis and production control, providing knowledge and a practical framework transferable to researchers and industrial practitioners.

Bottleneck Detection in Modular Construction Factories Using Computer Vision.

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System

Bottleneck identification is of great interest in discrete manufacturing fields, as they limit the system’s throughput. However, the bottlenecks are difficult to accurately identify due to the instability and complexity of discrete manufacturing systems. This paper proposes a dynamic bottleneck identification method (DBI-BS) that is based on effective buffers and fine-grained machine states to identify bottlenecks accurately. First, the complex manufacturing system (CMS) with strong coupling between elements is decoupled into several independent parts under the guidance of the effective buffer theory. Then, the machine activity duration method is improved through further fine-grained division, and the machine states are described by the timing flow model. The method to quantify the degree of bottleneck that restricts the system throughput (TH) is proposed on the basis of the turning point theory, and the one-to-one mapping relationship between the simulated and authentic complex manufacturing systems is also studied. Simulation results show that the DBI-BS can effectively identify dynamic bottlenecks in complex manufacturing processes, and the decoupling of complex systems can effectively improve the accuracy of dynamic bottleneck identification.

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems.

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

Freeway bottleneck identification is an essential component in the process of deploying mitigation strategies to reduce congestion at freeway bottlenecks. Most previous studies on bottleneck identification focus on recurrent bottlenecks, and limited work has been conducted to identify the locations of non-recurrent bottlenecks. Therefore, in this study, we propose a new travel time reliability (TTR) measurement and develop a freeway bottleneck identification method based on this measurement, which can identify with high probability not only recurrent bottlenecks but also the locations of non-recurrent bottlenecks. The TTR measurement is developed based on statistical distance between travel time distributions. Three statistical distance measurements, Jensen–Shannon divergence, Wasserstein distance, and Hellinger distance, are applied in the TTR measurement. The bottleneck identification method is evaluated in a case study on I-15 freeway corridor in Salt Lake City, Utah. The three statistical distance measurements show good consistency in ranking locations by the impacts of recurrent and non-recurrent congestion, especially for extreme cases with very high or low variation between travel time distributions. The recurrent bottlenecks identified in this study show their clustering characteristics, which is similar to the generating and dismissing process of recurrent congestion. The locations with high probability of non-recurrent bottlenecks scatter both spatially and temporally, which agrees with the random characteristic of non-recurrent congestion.

Bp-ant colony algorithm: escape the Louvre

With the spread of terrorism in the new era, France has suffered many terrorist attacks in recent years. The new situation calls for a review of emergency evacuation plans for popular destinations. Taking the Louvre Museum as the research object, this paper discusses the emergency evacuation plan in detail. At the same time, in order to make effective planning, this study selects the day with the largest number of visitors to the Louvre Museum as the sample. In the establishment of emergency evacuation model, ant colony algorithm is introduced. The training time of two kinds of traffic evacuation algorithm is given, and BP neural network is used for training. In addition, the dynamic Bottleneck Identification Method Based on fuzzy set theory is used in this study. Through model optimization, the possible bottleneck location is accurately determined. Finally, this paper analyzes the advantages and disadvantages of the model, and gives the specific evacuation routes.

Identifying Recurring Bottlenecks on Urban Expressway Using a Fusion Method Based on Loop Detector Data

The accurate identification of recurrent bottlenecks has been an important assumption of many studies on traffic congestion analysis and management. As one of the most widely used traffic detection devices, loop detectors can provide reliable multidimensional data for traffic bottleneck identification. Although great efforts have been put on developing bottleneck identification methods based on loop detector data, the existing studies are less informative with respect to providing accurate position of the bottlenecks and discussing the algorithm efficiency when facing with large amount of real-time data. This paper aims at improving the quality of bottleneck identification as well as avoiding excessive data processing burden. A fusion method of loop detector data with different collection cycles is proposed. It firstly determines the occurrence and the approximate locations of bottlenecks using large cycle data considering its high accuracy in determining bottlenecks occurrence. Then, the small cycle data are used to determine the accurate location and the duration time of the bottlenecks. A case study is introduced to verify the proposed method. A large set of 30 s raw loop detector data from a selected urban expressway segment in California is used. Also, the identification result is compared with the classical transformed cumulative curves method. The results show that the fusion method is valid with bottleneck identification and location positioning. We finally conclude by discussing some future improvements and potential applications.

Markov modeling and analysis of multi-stage manufacturing systems with remote quality information feedback

Modeling and analysis of multi-stage manufacturing systems (MMSs) for product quality propagation have attracted a great deal of attention recently. Due to cost and resources constraints, MMSs do not always have ubiquitous inspection, and MMSs with remote quality information feedback (RQIF, i.e., quality inspection operation is conducted at the end of the production line) are widely applied. This paper develops a Markov model to analyze quality propagation in MMSs with RQIF. Analytical expressions of the final product quality are derived and the monotonicity properties are investigated. A quality bottleneck identification method is explored based on the proposed Markov model. The results of case study demonstrate the effectiveness of the proposed model.

Identification approach for bottleneck clusters in a job shop based on theory of constraints and sensitivity analysis

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.

Modeling, analysis, and improvement of integrated productivity and quality system in battery manufacturing

A battery manufacturing system typically includes a serial production line with multiple inspection stations and repair processes. In such systems, productivity and quality are tightly coupled. Variations in battery quality may add up along the line so that the upstream quality may impact the downstream operations. The repair process after each inspection can also affect downstream quality behavior and may further impose an effect on the throughput of conforming batteries. In this article, an analytical model of such an integrated productivity and quality system is introduced. Analytical methods based on an overlapping decomposition approach are developed to estimate the production rate of conforming batteries. The convergence of the method is analytically proved and the accuracy of the estimation is numerically justified. In addition, bottleneck identification methods based on the probabilities of blockage, starvation, and quality statistics are investigated. Indicators are proposed to identify the downtime and quality bottlenecks that remove the need to calculate throughput and quality performance and their sensitivities. These methods provide a quantitative tool for modeling, analysis, and improvement of productivity and quality in battery manufacturing systems and can be applied to other manufacturing systems ameanable to investigation using integrated productivity and quality models.

Re-entrant lines with unreliable asynchronous machines and finite buffers: performance approximation and bottleneck identification

In this paper we study a re-entrant line with unreliable asynchronous exponential machines and finite buffers. First, an approximation method is presented to estimate the throughput of the re-entrant line. The idea of the approximation is to transform an M-machine re-entrant line into a 2M-machine serial line. Then, a system approach to identify a c-bottleneck based on blockage and starvation information is proposed, where a c-bottleneck machine is the machine whose improvement in machine capacity leads to the largest improvement in system throughput compared with improving all other machines. It is shown that the approximation method results in acceptable accuracy, and the bottleneck identification method can correctly detect the bottlenecks in most cases.