scholarly journals Short-Term Scheduling Model of Cluster Tool in Wafer Fabrication

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1029
Author(s):  
Ying-Mei Tu
Keyword(s):  
Arrival Time ◽  
Time Estimation ◽  
Advanced Technology ◽  
Shop Floor ◽  
Process Time ◽  
Short Term ◽  
Work In Process ◽  
Scheduling Model ◽  
Cluster Tools ◽  
Cluster Tool

Since last decade, the cluster tool has been mainstream in modern semiconductor manufacturing factories. In general, the cluster tool occupies 60% to 70% of production machines for advanced technology factories. The most characteristic feature of this kind of equipment is to integrate the relevant processes into one single machine to reduce wafer transportation time and prevent wafer contaminations as well. Nevertheless, cluster tools also increase the difficulty of production planning significantly, particularly for shop floor control due to complicated machine configurations. The main objective of this study is to propose a short-term scheduling model. The noteworthy goal of scheduling is to maximize the throughput within time constraints. There are two modules included in this scheduling model—arrival time estimation and short-term scheduling. The concept of the dynamic cycle time of the product’s step is applied to estimate the arrival time of the work in process (WIP) in front of machine. Furthermore, in order to avoid violating the time constraint of the WIP, an algorithm to calculate the latest time of the WIP to process on the machine is developed. Based on the latest process time of the WIP and the combination efficiency table, the production schedule of the cluster tools can be re-arranged to fulfill the production goal. The scheduling process will be renewed every three hours to make sure of the effectiveness and good performance of the schedule.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

scholarly journals THE UPGRADED ROLE OF THE VALUE STREAM DESIGN FOR REDESIGNING THE FACTORY LAYOUT

2020 ◽  
Vol Vol. 36 (No. 2) ◽  
pp. 49-57
Author(s):  
István Vajna ◽  
Anita Tangl
Keyword(s):  
Material Flow ◽  
Layout Design ◽  
Simulation Software ◽  
Shop Floor ◽  
Value Stream ◽  
Work In Process ◽  
Production Area ◽  
Production Areas ◽  
Milk Run ◽  
Factory Layout

The case study shows the re-optimization of an initial new factory layout design with Value Stream Design (VSD). The VSD is a quantitative method and its’ final goal is to make a waste free optimized material flow. The primary goal of arrangement is to reduce transportation distances and frequencies, optimize human load. Initially the whole factory shop floor layout design was already made in push concept. The plans were made by production management, logistics, engineering department at the headquarter of the multinational automotive company with based on VDI2870 holistic concept linking strategy on tactics and operation. On the layout (v1.) the hundreds of machines were placed and arranged by CAD (Computer Design) engineers to fit the space. The factory building has 15,000 m2 with empty shop floor waiting for the final decisions for equipment. The factory production area was shared into six main production areas (P1-P6), which correlates with their product complexity of the product families. Each production area output can be finished product (FP) or semi-finished product (SFP) for the next production areas. To validate the whole factory layout it was necessary to involve lean experts that identified disadvantages and constraints. Without lean implementation the company’s transportation waste would be 49% more per year. The Value Stream Design importance nowadays is upgrading to a higher level, when the whole global business is changed, the labor force fluctuates, and the cost and delivery time reduction plays a vital role in the company’s profit and future. The research shows that if the decision taking is based on real data and facts the controlling and management can do its best in time. Using VSD and re-evaluating the transportation routes, frequency and costs is the first step to define a smooth, low cost, material flow (v2.). This development ensured the company to drive from push to pull production through mixed production system. Originally, the production flow was clockwise orientation. It was changed step by step to mixed production by eliminating work in process storages, implementing FIFO lanes, Milk Run, and Kanban. The total annual transportation distances were reduced from 4,905,000 m between the rump-up and serial production period. The warehouse storage size was reduced to 50% and implementation cost from €75,000 to €32,500. By eliminating work in process storages along production lines it was possible to open a new two way transportation road that also will serve the AGV’s operations in industry 4.0 projects. Due to decreased lead time the logistic labor productivity increased by 45%. Besides taking measurements for the VSD it was used Value Stream Mapping as a lean tool and an own designed VSD evaluation and a simulation software. The VSD team’s cooperative actions reduced the evaluation and validation time with 65% then it was initially planned. The implementations were evaluated from the rump-up phase to the first serial productions and the results were confirmed by controlling and management

scholarly journals Implementation of Lean Manufacturing in a Cement Industry

2021 ◽  
Vol 11 (3) ◽  
pp. 7069-7074
Author(s):  
M. Masmali
Keyword(s):  
Production Line ◽  
Lean Manufacturing ◽  
Production Control ◽  
Value Added ◽  
Cement Industry ◽  
Shop Floor ◽  
Value Stream Mapping ◽  
Cement Production ◽  
Work In Process ◽  
Production Processes

The lean manufacturing concept is a systematic minimization of waste and non-value activities in production processes introduced by the Toyota production system. In this research, lean manufacturing is implemented in a cement production line. Value Stream Mapping (VSM) is applied to give a clear picture of the value chain in cement production processes and to highlight the non-value-added in the shop floor. To begin, the existing VSM is constructed based on the information and data gathered during visiting and observing the manufacturing process in the firm. As a result, the excess inventory between workstations was identified as a major waste generation, hence, the proposed VSM conducts further improvement and makes action plans to alleviate the unwanted activities. Then, the takt time to ensure smooth material flow and to avoid any occurring delay or bottleneck in the production line was figured out. The supermarket pull-based production control is suggested to be adopted in the future map. Two pull production strategies are selected in this case. The first is applying the Kanban system to control the level of inventory between workstations. The other is the CONWIP approach to control the amount of work in process to the entire production line. The outcome of the proposed models indicates a decrease of the none-value time from 23 days in the current state to about 4 and 2 days in Kanban and CONWIP systems respectively, so the CONWIP was suggested as most efficient. Some suggestions for further research are also mentioned.

scholarly journals A Two-Season Impact Study of Radiative Forced Tropospheric Response to Stratospheric Initial Conditions Inferred From Satellite Radiance Assimilation

Climate ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 114
Author(s):  
Min Shao ◽  
Yansong Bao ◽  
George P. Petropoulos ◽  
Hongfang Zhang
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Stratospheric Ozone ◽  
Wrf Model ◽  
Lower Troposphere ◽  
Short Wave ◽  
Advanced Technology ◽  
Short Term ◽  
Radiative Processes ◽  
Stratospheric Temperature

This study investigated the impacts of stratospheric temperatures and their variations on tropospheric short-term weather forecasting using the Advanced Research Weather Research and Forecasting (WRF-ARW) system with real satellite data assimilation. Satellite-borne microwave stratospheric temperature measurements up to 1 mb, from the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS), and the Special Sensor microwave Imager/Sounder (SSMI/S), were assimilated into the WRF model over the continental U.S. during winter and summer 2015 using the community Gridpoint Statistical Interpolation (GSI) system. Adjusted stratospheric temperature related to upper stratospheric ozone absorption of short-wave (SW) radiation further lead to vibration in downward SW radiation in winter predictions and overall reduced with a maximum of 5.5% reduction of downward SW radiation in summer predictions. Stratospheric signals in winter need 48- to 72-h to propagate to the lower troposphere while near-instant tropospheric response to the stratospheric initial conditions are observed in summer predictions. A schematic plot illustrated the physical processes of the coupled stratosphere and troposphere related to radiative processes. Our results suggest that the inclusion of the entire stratosphere and better representation of the upper stratosphere are important in regional NWP systems in short-term forecasts.

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