• 산업경영시스템학회지
• /
• 제13권21호
• /
• pp.143-151
• /
• 1990

This paper studies a single machine scheduling problem in which all jobs have the common due date and penalties are assessed for jobs at different rates. The scheduling objective is to minimize the weighted mean absolute deviations(WMAD). This problem may provide greater flexibility in achieving scheduling objectives than the mean absolute deviation (MAD) problem. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. This article extends the results to the problem to the problem of scheduling n-jobs on m-parallel identical processors in order to minimize the weighted mean absolute deviation.

• 산업경영시스템학회지
• /
• 제14권24호
• /
• pp.111-121
• /
• 1991

This paper considers a non preemtive single processor scheduling problem in which each set have the two common due dates. The objective of the problem is to minimize the weighted mean absolute deviation of job completion times about such two common due dates under the assumption that each job has a different weight. Such a job sequence is an W-shaped sequence. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. The performance comparison is made among three heuristic scheduling procedures.

• Wind and Structures
• /
• 제26권6호
• /
• pp.383-395
• /
• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• 산업공학
• /
• 제17권2호
• /
• pp.242-248
• /
• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for minimizing the mean weighted absolute deviation of job completion times from due dates when jobs are scheduled in a no-wait lot-streaming flow shop. In a no-wait flow shop, each sublot must be processed continuously from its start in the first machine to its completion in the last machine without any interruption on machines and without any waiting in between the machines. NGA replaces selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and adopts the idea of inter-chromosomal dominance. The performance of NGA is compared with that of GA and the results of computational experiments show that NGA works well for this type of problem.

• 대한산업공학회지
• /
• 제30권2호
• /
• pp.159-164
• /
• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots and then scheduling these sublots in order to accelerate the completion of jobs in a multi-stage production system. Anew genetic algorithm (NGA) is proposed for an-job, m-machine, equal-size sublot lot-streaming flow shop scheduling problem with delivery windows in which the objective is to minimize the mean weighted absolute deviation of job completion times from due dates. The performance of NGA is compared with that of an adjacent pairwise interchange (API) method and the results of computational experiments show that NGA works well for this type of problem.

• 산업경영시스템학회지
• /
• 제38권3호
• /
• pp.159-168
• /
• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• 품질경영학회지
• /
• 제28권2호
• /
• pp.70-88
• /
• 2000

Short runs where it is neither possible nor practical to obtain sufficient subgroups to estimate accurately the control limit are common in modem business environments. In this study, the standardized control chart, Hillier's exact method, Q chart, EWMA(Exponentially Weighted Moving Average) chart for Q statistics and EWMA chart for mean and absolute deviation among many SPC(Statistical Process Control) techniques for short runs have been reviewed and advantages and disadvantages of these techniques are discussed. The simulation experiments to compare performances of these variable charts for process mean and variations are conducted for combination of subgroup size, scale and timing of shifts of process mean an/or standard deviation. Based upon simulation results, some guidelines for practitioners to choose short run SPC techniques are recommended.

• 산업경영시스템학회지
• /
• 제14권23호
• /
• pp.27-36
• /
• 1991

This paper considers scheduling a set of n-jobs on m-paraller identical processors in which all jobs have the common due date. The objective of the problem is to minimize the weighted mean absolute deviation of job completion times about such common due dates under the assumption that each job has a different weight. and to determine the optimal value of a common due date. We propose four heuristic solution methods based on several dominance conditions, and its solution procedure is illustrated with numerical examples. The Performance comparison is made among four heuristic scheduling procedures.

• International Journal of Air-Conditioning and Refrigeration
• /
• 제14권3호
• /
• pp.118-123
• /
• 2006

A few commonly used correlation equations of the enthalpy of vaporization essential to the analysis of refrigeration cycles are reviewed. A new four-parameter correlation equation is proposed assuming that the enthalpy of vaporization could be represented with a linear form of the temperature and an additional function which slowly decreases as the temperature increases. It is not a common practice to measure the enthalpy of vaporization by experiment; therefore, performance of the new correlation is examined using numeric data from the ASHRAE tables for 22 pure substance refrigerants. The new correlation equation and other existing ones are fitted to the data optimizing the root mean squared deviation. All data points are weighted equally and NBP (normal boiling point) is used as a fixed point since the NBP is important for refrigeration application. The new four-parameter equation yields an average absolute deviation of 0.05% for 22 refrigerants which is smaller than those of other four-parameter equations, such as Guermouche-Vergnaud (0.08%), Aerebrot (0.13%), Radoz-Lyderson (0.08%), and Somayajulu four-parameter equation (0.08%).

• 대한전기학회논문지:시스템및제어부문D
• /
• 제54권4호
• /
• pp.251-258
• /
• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.