Job Cycle Time Research Articles

A Fuzzy Fluctuation Smoothing Rule for Job Dispatching in a Wafer Fabrication Factory

Job dispatching in a wafer fabrication factory is a difficult task, mainly due to the complexity of the production system and the uncertainty involved in the production activities. Recently, a number of advanced dispatching rules were proposed, which estimate the remaining cycle times of jobs. This predictive nature is conducive to the effectiveness of these rules. If the uncertainty in the remaining cycle time can be better considered, incorrect scheduling will possibly be reduced. The tailored nonlinear fluctuation smoothing rule for mean cycle time (TNFSMCT) is fuzzified in this study, by expressing the remaining cycle time with a fuzzy value. The effectiveness of the proposed methodology is illustrated with a simulation study.

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

A job-classifying and data-mining approach for estimating job cycle time in a wafer fabrication factory

Before making a due date commitment to our customer, we need to estimate the cycle times of jobs. For this reason, many of the most-advanced methods classify jobs, before or after estimating the cycle times. However, job classification is not directly helpful to optimize the performances of these estimation methods. To solve this problem, a job-classifying and data-mining approach is proposed in this study to improve the performance of cycle time estimation by optimizing the results of job classification. In addition, some association rules are also extracted from the estimation results to facilitate the practical application of the proposed methodology. According to the results of a case study, the job-classifying and data-mining approach achieved a better estimation performance and could produce some useful estimation rules.

Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying fuzzy-neural approach

Estimating the cycle time for every job in a factory is a critical task. It was recently reported that job classification noticeably enhanced the accuracy of job cycle time estimation. In pre-classifying approaches, whether the pre-classification approach combined with the subsequent estimation approach is suitable for the data is questionable. Conversely, the difficulty in classifying a job according to only the estimation error not the various attributes is a problem to post-classifying approaches. To tackle these problems, a bi-directional classifying fuzzy-neural approach is proposed in this study. In the proposed methodology, jobs are not only pre-classified but also post-classified. The results of pre-classification and post-classification are aggregated into a suitability index for each job. A job is then assigned to the category to which its suitability index is the highest. A radial basis function network is also constructed to predict the suitability index of a job according to the various attributes. To evaluate the effectiveness of the proposed methodology, a practical example was used in this study. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches.

A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant

To further enhance the performance of job completion time prediction and internal due date assignment in a wafer fab, a fuzzy-neural knowledge-based system is constructed in this study. In the constructed system, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to predict the completion/cycle time of a job. Each fuzzy multiple linear regression model can be converted into an equivalent non-linear programming problem to be solved. Subsequently, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy completion time forecasts into a polygon-shaped fuzzy number, in order to improve the precision of completion time forecasting. The polygon-shaped fuzzy number contains the actual value, and its upper bound determines the internal due date of the job. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A practical example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of job cycle time prediction by 16 and 21%, respectively.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

Fuzzy-neural approaches with example post-classification for estimating job cycle time in a wafer fab

Estimating the cycle time of a job in a wafer fabrication plant (wafer fab) is a critical task to the wafer fab. Many recent studies have shown that pre-classifying a job before estimating the cycle time was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, two hybrid approaches with example post-classification, the equally-divided method and the proportional-to-error method, are proposed in this study in which a job is post-classified by a back propagation network (BPN) instead after the forecasting error is generated. In this novel way, only jobs whose cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation (PS) is applied in this study to generate test data. According to experimental results, the forecasting accuracy (measured with root mean squared error, RMSE) of the proportional-to-error method was significantly better than those of the other approaches in most cases by achieving a 26–56% (and an average of 41%) reduction in RMSE over the comparison basis – multiple-factor linear combination (MFLC). The effect of post-classification was also statistically significant.

Estimating Job Cycle Time in Semiconductor Manufacturing with an ANN Approach Equally Dividing and Post-Classifying Jobs

Estimating the cycle time of every job in a semiconductor manufacturing factory is a critical task to the factory. Many recent studies have shown that pre-classifying a job before estimating the cycle time of the job was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, an artificial neural network (ANN) approach that equally divides and post-classifies jobs is proposed in this study in which a job is post-classified by a BPN instead after the forecasting error is generated. In this novel way, only jobs which cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, some data were collected from an actual semiconductor manufacturing factory. According to experimental results, the forecasting accuracy (measured with root mean squared error (RMSE)) of the proposed methodology was significantly better than those of the other approaches in most cases by achieving a 16%~44% (and an average of 29%) reduction in RMSE over the comparison basis – multiple-factor linear combination (MFLC). The effect of post-classification was also evident.

Predicting the effects of common levels of variability on flow processing systems

The implementation of flow processing is essential to the successful application of lean manufacturing practices since it provides the infrastructure for pull production to take place and the focused elimination of waste. With the adoption of lean practices into a broader range of production environments there is an increasing need for flow processing to operate under a wider range of conditions particularly with respect to the sources and levels of variability that exist. In order to ensure efficient flow processing under such conditions a range of methods has been developed for both reducing levels of variability and for managing the effects of variability. However, ensuring the effective use of each of these methods requires detailed knowledge of the effects this variability has on the resource requirements of individual workstations. The current paper presents a methodology for developing predictive models that can be used to quantitatively estimate the levels of blocking and waiting that occur on individual workstations along a flow processing line. The methodology presented makes use of discrete event simulation to generate data from which estimating models are derived that relate %blocking and %waiting arising at individual workstations with the coefficient of variation of their job cycle time distributions.

Field evaluation of a new grocery checkstand design

A novel express checkstand with basket cut-out was developed and evaluated in a small grocery store chain. Eight checkers performed three trials of simulated grocery checkout work with actor customers using both old and new checkstand designs. Usability was assessed by survey, structured interview and observation. Time–motion video analysis was used to evaluate differences in body postures, motions, and productivity. The new express checkstand design, which lowered and angled the presentation of the grocery basket, significantly ( p<0.05) reduced right wrist flexion duration and repetition, and right arm elevation repetition. No significant differences were found in job cycle time between designs. These results demonstrate the utility of a new express checkstand design that could reduce extreme postures and motions with no loss in productivity.

Towards primary care for non-serious 999 callers: results of a controlled study of "Treat and Refer" protocols for ambulance crews

To develop and evaluate "Treat and Refer" protocols for ambulance crews, allowing them to leave patients at the scene with onward referral or self-care advice as appropriate. Crew members from one ambulance station were trained to use the treatment protocols. Processes and outcomes of care for patients attended by trained crews were compared with similar patients attended by crews from a neighbouring station. Pre-hospital records were collected for all patients. Records of any emergency department and primary care contacts during the 14 days following the call were collected for non-conveyed patients who were also followed up by postal questionnaire. Twenty three protocols were developed which were expected to cover over 75% of patients left at the scene by the attending crew. There were 251 patients in the intervention arm and 537 in the control arm. The two groups were similar in terms of age, sex and condition category but intervention cases were more likely to have been attended during daytime hours than at night. There was no difference in the proportion of patients left at the scene in the intervention and control arms; the median job cycle time was longer for intervention group patients. Protocols were reported as having been used in 101 patients (40.2%) in the intervention group; 17 of the protocols were recorded as having been used at least once during the study. Clinical documentation was generally higher in the intervention group, although a similar proportion of patients in both groups had no clinical assessments recorded. 288 patients were left at the scene (93 in the intervention group, 195 in the control group). After excluding those who refused to travel, there were three non-conveyed patients in each group who were admitted to hospital within 14 days of the call who were judged to have been left at home inappropriately. A higher proportion of patients in the intervention arm reported satisfaction with the service and advice provided. "Treat and Refer" protocols did not increase the number of patients left at home but were used by crews and were acceptable to patients. The protocols increased job cycle time and some safety issues were identified. Their introduction is complex, and the extent to which the content of the protocols, decision support and training can be refined needs further study.

Towards primary care for non-serious 999 callers: results of a controlled study of “Treat and Refer” protocols for ambulance crews

Objective: To develop and evaluate “Treat and Refer” protocols for ambulance crews, allowing them to leave patients at the scene with onward referral or self-care advice as appropriate. Methods: Crew...

Musculoskeletal symptoms, ergonomic aspects and psychosocial factors in two different truck assembly concepts

The project was carried out as a multidisciplinary correlational study of two truck-axle assembly systems (System A and B). System A (the old system) could be characterized as an unpaced line assembly system, whereas the change to the new system, System B, involved a parallelized system with longer job cycle time. The study included all permanent assembly workers in the two systems (System A 17 workers, System B 28 workers). The purpose of this study was to evaluate the two systems with respect to ergonomic factors, psychosocial factors and self-reported musculoskeletal symptoms. The job cycles were studied by means of video observation. Each subject answered a questionnaire regarding musculoskeletal symptoms and physical and psychosocial environment. The assemblers in System B were more satisfied with the physical work environment (e.g. noise, air), but less satisfied with the psychosocial environment, and the period prevalence of musculoskeletal symptoms was not lower, compared with the assemblers in System A. Video observation techniques showed that deep forward flexion was less common, but that the work was more hand intense in System B. In System B, neck/shoulder symptoms were associated principally with physical work (load) variables, whereas low back symptoms were associated with psychosocial variables.