Bottleneck Station Research Articles

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

A deep learning approach for balance optimisation of patch panel assembly line

This paper introduces an integrated methodology to enhance the efficiency of patch panel assembly lines by harnessing advanced artificial intelligence (AI) techniques. The primary objective is to minimise the total completion time of patch panels, a pivotal metric for manufacturing efficiency. The study employs the Assembly Line Production Optimisation (ALPO) model to analyze and enhance the production capacity of the bottleneck station. Existing challenges related to patch panel assembly line task distribution, process content, and tooling layout are scrutinised against production process standards for operational measurements. To achieve improvement, Deep Neural Network (DNN) and Recurrent Neural Network (RNN) models are employed in hidden layers, supplemented by deep feed-forward (DFF) layers for output, in adherence to engineering design principles. The bottleneck station's load rate is successfully reduced to below 80%, contributing to an overall balance rate increase from 73% to 88%. These outcomes signify a notable enhancement in production efficiency and employee satisfaction. The research utilises a dataset derived from the Microsoft Azure AI-based PdM dataset. The paper illustrates how AI techniques offer a comprehensive solution to address the inherent complexities in patch panel assembly line optimisation.

Optimization and Enhancement of Company A's C Product Production Line

This study focuses on addressing bottleneck stations within manufacturing enterprises, emphasizing the importance of effective methods to enhance production line balance. The primary objectives are to elevate the production line's balance rate, subsequently improving production efficiency, and reducing overall production costs. The research centers around A Company's C-type liquid crystal display screen production line, employing production line balance theory and industrial engineering (IE) technology methods. Additionally, a 0-1 integer programming model is integrated to optimize and rectify production line imbalances. The optimization process results in a substantial enhancement of the balance rate and output, aligning with the evolving production development requirements of enterprises. This paper provides valuable insights into the practical application of industrial engineering techniques for optimizing manufacturing processes.

Dynamic bottleneck identification and production loss evaluation for assembly lines

Identifying bottlenecks is a crucial challenge for manufacturing firms in their quest to increase capacity. While many approaches have been proposed to address this issue, existing solutions still have limitations in dealing with highly dynamic, stochastic systems in modern smart manufacturing. Hence, identifying bottlenecks remains a complex task. To address these shortcomings, we propose a novel data-enabled mathematical model for evaluating the performance of assembly lines with limited sensor information. A dynamic bottleneck detection method is then developed based on this proposed model. Furthermore, the production loss caused by the detected bottleneck stations, referred to as the dynamic production loss (DPL), can be evaluated and attributed. Numerical case studies are presented to validate the accuracy of the data-enabled model and demonstrate the effectiveness of the bottleneck detection and DPL evaluation methods.

REDUCING WORK IN PROCESS (WIP) USING LINE BALANCING TECHNIQUE IN MANUFACTURING INDUSTRY

For organizations looking to increase productivity and efficiency in the manufacturing sector, reducing Work In Process (WIP) is a crucial goal. The deployment of line-balancing techniques to accomplish this goal is the main topic of this research article. The introduction discusses the negative consequences of WIP, including productivity loss, leads times that are too long, and bottlenecks. The application of lean manufacturing principles, workflow optimization, WIP limits establishment, improved communication and collaboration, prioritization and sequencing of work, identification and removal of bottlenecks, and performance monitoring are just a few of the methods for reducing WIP that are covered. The importance of line balancing in maximizing efficiency, effectiveness, and productivity in manufacturing output is emphasized in the study. The literature review explores the use of worksharing and line-balancing approaches to increase productivity as well as the advantages of line-balancing in eliminating operational inefficiencies. The procedure for data collection and analysis, which includes calculating cycle time and takt time, is described in the methodology section. To reduce WIP and streamline processes, the suggested methodology advises allocating greater resources to bottleneck stations. The outcomes show how the suggested methodology works to decrease WIP and boost productivity. The paper concludes by comparing the advantages of line balancing in reducing workstations, cycle time, balance delay, idle time, and overall line length. Present. The references provided offer relevant studies on line balancing and assembly line basics. This research contributes valuable insights and practical implications for improving productivity and reducing WIP in the manufacturing industry.

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.

Ant Colony optimization application in bottleneck station scheduling

Finding optimal solutions to production planning and scheduling problems is crucial for surviving in a competitive environment and meeting customer expectations over time. Planning can become complicated in sectors with many different products such as tire production. This study focuses on the bottleneck problem caused by a machine called a Quadruplex Extruder in a tire factory. With this machine, rubber is extruded and transformed into a tread material product, which is critically important in some essential tire features, such as low rolling resistance and brake distance. This study aims to minimize the set-up times in production by optimizing the manufacturing order of the products produced in a quadruplex extruder machine using the Ant Colony Algorithm (ACA), a well-known metaheuristic method to solve polynomial optimization problems. In addition, the second version of the Lin–Kernighan–Helsgaun (LKH-2) algorithm was adapted to this problem. Manually prepared, LKH-2 and ACA-produced schedules were compared in terms of global efficiency. As a result, it has been shown that ACA can provide fast and suitable solutions for decision makers in production planning.

Predicting future production system bottlenecks with a graph neural network approach

Predicting throughput bottlenecks has received an increasing research attention in the recent years. Several statistical and machine learning models have been developed, with the objective of identifying future bottleneck locations for more efficient and cost-saving productions. However, bottleneck prediction remains a challenging task due to the complexity of manufacturing systems and the interactive effects from many impacting factors. This paper proposed the BSTAN (bottleneck spatial-temporal attention network) – an interpretable modeling framework to improve system bottlenecks prediction capability for complex manufacturing systems by: (1) characterizing line topological features using graph representations; (2) modeling spatial-temporal relationships and interactions between stations using graph attention network with gated recurrent units. In this proposed framework, the production line is first transformed into the representation of a weighted undirected graph to depict network structural information of a production line. An attention-based graph neural network with gated recurrent units is applied to capture both the temporal trends and station interactions to predict each station’s blockage and starvation. Future bottleneck stations are identified by analyzing the forecasted blockage and starvation distribution. To evaluate the model performance, an industrial case study is performed with data collected from a one-year production at an automotive powertrain assembly line. With the proper model construction, BSTAN has outperformed both statistical and machine learning benchmark models, achieving up to 15% lower root mean square error compared to the best benchmark method in predicting overall system blockage and starvation time.

Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams

Congestion; operational delays due to a vicious circle of passenger congestion and train-queuing; is an escalating problem for metro systems because it has negative consequences from passenger discomfort to eventual mode shifts. Congestion arises due to large volumes of passenger boardings and alightings at bottleneck stations, which may lead to increased stopping times at stations and consequent queuing of trains upstream. Such queuing further reduces line throughput and implies an even greater accumulation of passengers at stations. Alleviating congestion requires control strategies such as regulating the inflow of passengers entering bottleneck stations. The availability of large-scale smartcard and train movement data from day-to-day operations facilitates the development of models that can inform such strategies in a data-driven way. In this paper, we propose to model station-level passenger congestion via empirical passenger boarding–alightings and train flow relationships, henceforth, fundamental diagrams (FDs). We emphasise that estimating FDs using station-level data is empirically challenging due to confounding biases arising from the interdependence of operations at different stations, which obscures the true sources of congestion in the network. We thus adopt a causal statistical modelling approach to produce FDs that are robust to confounding and as such suitable to inform control strategies. The closest antecedent to the proposed model is the FD for road sections, which informs traffic management strategies, for instance, via locating the optimum operation point. Our analysis of data from the Mass Transit Railway, Hong Kong indicates the existence of concave FDs at identified bottleneck stations, and an associated critical level of boarding–alightings above which congestion sets in unless there is an intervention.

A Comparative Study of Unbalanced Production Lines Using Simulation Modeling: A Case Study for Solar Silicon Manufacturing

In the solar silicon manufacturing industry, the production time for crystal growth is ten times longer than at other workstations. The pre-processing time at the ingot-cutting station causes work-in-process (WIP) accumulation and an excessively long cycle time. This study aimed to find the most effective production system for reducing WIP accumulation and shortening the cycle time. The proposed approach considered pull production systems, and the response surface methodology was adopted for performance optimization. A simulation-based optimization technique was used for determining the optimal pull production system. The comparison between the results of various simulated pull production systems and those of the existing solar silicon manufacturing system showed that a hybrid production system in which a kanban station was installed before the bottleneck station with a CONWIP system incorporated for the rest of the production line could reduce the WIP volume by 26% and shorten the cycle time by 16% under the same throughput conditions.

A Novel Model for Accessibility of Bus Station and Its Application to Bottlenecks Identification: A Case Study in Harbin

Accessibility plays a crucial role in evaluating and optimizing the service quality of public transport systems. Traditionally, bus accessibility research studies mainly focus on the evaluation from the perspective of the aggregate model and analyze the service radius of the site considering the front end of travel. This paper aims to model microlevel bus accessibility calculation and accurate bottleneck station identification according to the opportunity method with AVL (Automatic Vehicle Location) data. The research featured the following control elements: (1) redefining bus accessibility with bus station as the research object; (2) running time between interstation sections is selected as the control index; (3) A new measure index of accessibility. The farthest direct reachable distance is proposed. Moreover, by introducing the quality control algorithm, the study establishes a model of upper and lower limits of accessibility based on the probability index of bilateral norms, which can be employed to identify the bottleneck stations according to the significance level. Harbin City is selected as the case study for methodology verification and shows that the accessibility method is effective and reliable. Additionally, by taking the travel demand in the region as the adjustment parameter of the quality control algorithm, the study demonstrates the ideal accessibility interval of stations and accurately locates all bottlenecks. The study presented here adds to our understanding of accessibility in the construction and management of buses and provides an essential reference for the following optimization.

Digital twin-based research on the prediction method for the complex product assembly abnormal events

ABSTRACT The emergence of abnormal events (e.g. personnel abnormalities, equipment failures, etc.) on the assembly floor of complex products can seriously affect normal assembly progress. In response to the problems of poor timeliness and lack of predictability in the control of abnormal events on the assembly floor, a method for predicting abnormal events on the assembly floor of complex products based on digital twin technology is proposed. A model for predicting abnormal assembly events is constructed with the physical assembly workshop, virtual assembly workshop, assembly workshop twin data platform and abnormal events prediction service system working together, and its prediction operation mechanism is designed based on the classification of abnormal events and the workflow of the mechanism under different states is analysed. The Grey-Markov method is used to predict abnormal assembly events and provide real-time information for the planning and scheduling system. In order to verify the effectiveness of this scheme, combined with the electrical multiple units bogie assembly workshop, the prediction of the number of equipment failures at the bottleneck station is achieved. The prediction accuracy is much better than that of the GM(1,1) model and can be applied to actual production.

Performance Measurement and Improvement of Healthcare Service Using Discrete Event Simulation in Bahir Dar Clinic

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the health care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization. Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

A real-time 3D scanning of aluminum 5052-H32 laser welded blanks; geometrical and welding characterization

In this study, distortion mapping (deformation or warping) of aluminum Laser Welded Blanks (LWBs) is investigated during an automated forming process. Here, a novel approach of automated and real-time inspection is presented which enhances final product quality, reduces lead time, and increases manufacturing cadence while reducing product cost. As a result, flange zone distortions are detected in the range of ±3mm which considers as a large deviation for aluminum assemblies. Promising the proposed model results to comply with an evaluation of Cracking Susceptibility (CS) in the welding structure by increasing the harmful distortion, mainly by preventing the processing of defective parts in the value chain and especially in bottleneck stations are well estimated.

ANALYSIS OF THE DYNAMIC BEHAVIOR OF THE EFFICIENCY AT THE BOTTLENECK STATION OF A WATER PUMP ASSEMBLY LINE

General Equipment Efficiency (GEE) index allows to contrast the theoretical use of a machine with the real use given to it and thus proceed to identify possible problems and the need to implement improvement actions [1]. In order to continuously evaluate the total efficiency of a pump assembly line, a dynamic model was developed in which actions derived from failure analysis were incorporated. During the evaluation scenarios developed in the simulation, the impact of the improvements on the performance of the assembly line bottleneck station is evaluated.

Execution of lean and industrial techniques for productivity enhancement in a manufacturing industry

In current global manufacturing scenario, every organization wants to fulfill their customer needs to retain their position in the market. Lead time plays a vital role to improve them in the global competitive market. The aim of this research work is to minimize the overall lead time of the product by finding out bottlenecks and to eliminate them. The important processes in the production line are work in progress, layout optimization and line balancing. The existing manufacturing techniques and the process of the product were studied thoroughly and then time study was followed. Based on the time study results TAKT time was calculated for the demand followed with current state VSM was drawn and the bottleneck stations were identified. After developing current state VSM, assembly line was balanced using flexible line balancing software. Further improvements were made with the lean techniques of fish bone diagram, Pareto chart and layout optimization. After the execution of the above lean techniques the future state VSM was drawn and observed that the lead time and value added time were reduced by about 15 and 16 min respectively.

Production planning and project scheduling for engineer-to-order systems- case study for engineered wood production

Small companies that prefabricate parts for the construction industry in the context of engineer-to-order (ETO) systems, often encounter production issues as they usually do not systematically optimise their planning. In this paper, a production planning method for a multi-project ETO system is proposed. Four optimisation models testing combinations of three objectives are proposed. The main objective present in all models is cost reduction. The other objectives considered are minimising project finish time and minimising set-up time of a bottleneck station. The models are applied in a case study with an engineered wood production firm considering fifteen construction projects over a period of forty weeks. Two scenarios for adding long and complex projects and projects with medium length and complexity are also tested to provide a decision support tool in the project acceptance phase. This article contributes to the scientific literature by providing four production scheduling models which integrate a multi-objective production scheduling approach in a multi-project context for the prefabrication of ETO parts in construction. Furthermore, the research applies the models to a real case which highlights empirical issues that had not been entirely explored in current literature.

Modelling and Simulation of a Shearcut Process Flow at a Steel Processing Company

Modelling and simulation of industrial systems have become a conventional method for productivity improvement through optimizing design, operation, performance and troubleshooting. This paper analyses the current Shearcut process flow through modelling and simulation. A case study research was undertaken at a steel processing company that transform steel coils into sheets, plates, slits, tubes, blanks and various roofing products. Two models were created using Arena software. The models were a representation of the process flow found in the Shearcut warehouse. The first model was developed from data obtained from production records and time studies that were carried out on some of the operations. Bottleneck stations and non-value adding operations were identified. The second model was created with some improvements that included reduced non-value adding times. Results from the second model had an improved output. This paper contributes to the use of computer simulation as a tool to improve productivity.

A Simulation Based Study for Managing Hospital Resources by Reducing Patient Waiting Time

In Pakistan most of the local hospitals have limited resources and budget deficits to address day-to-day routine activities. This is further intensified by inefficient management of hospital resources. Hence, it is essential to efficiently manage hospital resource as it will lead to health care costs reduction and increased patient’s satisfaction. The focus of this research is to demonstrate the use of the tool in healthcare system, specifically in Pakistan, and balance the resources of a hospital, which will reduce the waiting time for the Outdoor Patients Departments. The resources at the Outdoor Patients Department including X-ray, Labs, Ultrasound and the ECG sections were included in the computer-based simulation model with the aim to find the bottleneck departments as they are the main cause of waiting time in the hospital. These bottlenecks are anticipated due to the lack of an optimized model, which has been addressed in this simulation. Based on the simulation study an optimized model is proposed to the hospital administration where the resources are readjusted resulting in the utilization of underutilized departments and decreasing the waiting time of the stations particularly the bottleneck stations. The motivation behind this research is to aid hospitals in better management of resources, which can ultimately improve customers (patients) satisfaction.

A New Cobot Deployment Strategy in Manual Assembly Stations: Countering the Impact of Absenteeism

Abstract This paper discusses a new strategy for deploying cobots in assembly lines having manual stations. The paper first presents the contribution of absenteeism to the temporary appearance of bottleneck stations and the resulted lost throughput. It then delineates some barriers and obstacles of cobots’ deployment in these stations. Finally, it discusses the advantages of employing a cobot-specialist for a defined segment of the assembly line. The suggested role of the cobot-specialist is to quickly find ways in which a cobot will alleviate the work of a temporary workers filling positions of absentees. Such replacement workers are in their early stages of their learning curve, and are considerably slow, with high chances of becoming a bottleneck. A bottleneck station dictates the pace of the whole line and reducing its cycle-time, increases the throughput of the whole line. Thus, the suggested approach prevents throughput losses due to absenteeism. Having a line-segment specialist that is familiar with the various stations in his/her segment ensures both having some pre-prepared schemes of cobot deployment for each station, and the ability of rapid installation of the cobot. Moreover, replacement workers typically are too busy to deal with the cobot’s installation or operation. The approach is validated using ARENA simulation scenarios for various line segments.