To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) ... more To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created a...
Aim This study aims to develop a free, limited-edition workshop as an effective knowledge transla... more Aim This study aims to develop a free, limited-edition workshop as an effective knowledge translation strategy to enhance nurse leader-perceived self-efficacy for competence using Park’s Sweet Spot Theory and to evaluate its effectiveness over time. Method This is a study showing the process of developing a study protocol and its details. Results A 2-day workshop was developed for innovators and early adopters among nurse leaders with a macro-level influence based on Rogers’s diffusion of innovations theory, which consists of an introduction of Park’s Sweet Spot Theory, hands-on experience, a summary session, and a presentation of a certificate of completion. The workshop will be held at the University of Alberta Faculty of Nursing, using the “enabling blends” mode. A hybrid design of comparative effectiveness research and analysis of change will be utilized to assess nurse leader-perceived self-efficacy. CONCLUSION This protocol is significant as the first step in providing scientific rationales on how to effectively implement new knowledge—optimal safe nurse staffing levels derived from Park’s Sweet Spot Theory—into the right (safe yet efficient) nursing workforce policy-making to alleviate global nursing shortages.
Abstract Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown ou... more Abstract Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs.
Recently much research has focused on both the supply chain and reverse logistics network design ... more Recently much research has focused on both the supply chain and reverse logistics network design problem. The rapid progress in computer and network technology and the increasingly fierce competition in recent times have compelled global company to ...
Production planning is a core function in manufacturing systems and is gaining even greater atten... more Production planning is a core function in manufacturing systems and is gaining even greater attention in supply chain environments where many mutually dependent and cooperative manufacturers are involved. Lot sizing is one of the most important and difficult problems in production planning. While optimal solution algorithms exist for this problem, only very small problems can be solved in a reasonable computation time because the problem is NP-hard. In this paper we present a meta-heuristic approach, which we call "Memetic Algorithm based on Refinement Procedure", to solve multi-level lot sizing (MLLS) problem. We use a local refinement procedure based on benchmarking to facilitate the solution search. The benchmark-based refinement procedure proposed by this study is also applicable to other problems where solutions are difficult to refine. Keywords Multi-level lot sizing, Benchmark-based Genetic Algorithm, Memetic Algorithm based on Refinement Procedure
As semiconductor device geometries continue to shrink, the semiconductor manufacturing process be... more As semiconductor device geometries continue to shrink, the semiconductor manufacturing process becomes increasingly complex. This usually results in unbalanced utilization of machines and decreases overall productivity. One way to resolve such a problem is to share the resource capacity between different lines divided by floors. To this end, designing an efficient lifter assignment method to more efficiently manage transfer requests (TRs) of wafer lots to different floors is required. Motivated by this, our study addresses the assignment of lifters for delivering wafer lots to different floors. Unlike previous studies, which consider the current state of the system, our study considers both the current and possible future states of the system. We formulate an optimization model based on the Markov decision process. Then, we design an efficient method as a solution using both clustering and tournament selection methods. Experiments based on historical data confirm the effectiveness o...
Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) ... more Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) in South Korea have become a new and major bottleneck. This is mainly because the number of long-distance transportation requests has increased as the FAB area has widened. This paper presents a deep-learning-based adaptive method for the storage-allocation problem to improve the AMHS throughput capacity.
To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) ... more To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created a...
Aim This study aims to develop a free, limited-edition workshop as an effective knowledge transla... more Aim This study aims to develop a free, limited-edition workshop as an effective knowledge translation strategy to enhance nurse leader-perceived self-efficacy for competence using Park’s Sweet Spot Theory and to evaluate its effectiveness over time. Method This is a study showing the process of developing a study protocol and its details. Results A 2-day workshop was developed for innovators and early adopters among nurse leaders with a macro-level influence based on Rogers’s diffusion of innovations theory, which consists of an introduction of Park’s Sweet Spot Theory, hands-on experience, a summary session, and a presentation of a certificate of completion. The workshop will be held at the University of Alberta Faculty of Nursing, using the “enabling blends” mode. A hybrid design of comparative effectiveness research and analysis of change will be utilized to assess nurse leader-perceived self-efficacy. CONCLUSION This protocol is significant as the first step in providing scientific rationales on how to effectively implement new knowledge—optimal safe nurse staffing levels derived from Park’s Sweet Spot Theory—into the right (safe yet efficient) nursing workforce policy-making to alleviate global nursing shortages.
Abstract Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown ou... more Abstract Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs.
Recently much research has focused on both the supply chain and reverse logistics network design ... more Recently much research has focused on both the supply chain and reverse logistics network design problem. The rapid progress in computer and network technology and the increasingly fierce competition in recent times have compelled global company to ...
Production planning is a core function in manufacturing systems and is gaining even greater atten... more Production planning is a core function in manufacturing systems and is gaining even greater attention in supply chain environments where many mutually dependent and cooperative manufacturers are involved. Lot sizing is one of the most important and difficult problems in production planning. While optimal solution algorithms exist for this problem, only very small problems can be solved in a reasonable computation time because the problem is NP-hard. In this paper we present a meta-heuristic approach, which we call "Memetic Algorithm based on Refinement Procedure", to solve multi-level lot sizing (MLLS) problem. We use a local refinement procedure based on benchmarking to facilitate the solution search. The benchmark-based refinement procedure proposed by this study is also applicable to other problems where solutions are difficult to refine. Keywords Multi-level lot sizing, Benchmark-based Genetic Algorithm, Memetic Algorithm based on Refinement Procedure
As semiconductor device geometries continue to shrink, the semiconductor manufacturing process be... more As semiconductor device geometries continue to shrink, the semiconductor manufacturing process becomes increasingly complex. This usually results in unbalanced utilization of machines and decreases overall productivity. One way to resolve such a problem is to share the resource capacity between different lines divided by floors. To this end, designing an efficient lifter assignment method to more efficiently manage transfer requests (TRs) of wafer lots to different floors is required. Motivated by this, our study addresses the assignment of lifters for delivering wafer lots to different floors. Unlike previous studies, which consider the current state of the system, our study considers both the current and possible future states of the system. We formulate an optimization model based on the Markov decision process. Then, we design an efficient method as a solution using both clustering and tournament selection methods. Experiments based on historical data confirm the effectiveness o...
Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) ... more Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) in South Korea have become a new and major bottleneck. This is mainly because the number of long-distance transportation requests has increased as the FAB area has widened. This paper presents a deep-learning-based adaptive method for the storage-allocation problem to improve the AMHS throughput capacity.
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