1,720,968 research outputs found
Determining the Optimal Values of Exponential Smoothing Constants--Does Solver Really Work?
Get PDFA key issue in exponential smoothing is the choice of the values of the smoothing constants used. One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE). We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants. We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants
Bias in Aggregations of Subjective Probability and Utility
No full textSubjective estimates of probability or utility are prone to two kinds of error: random and systematic (bias). The usual approach to reducing random error is averaging. How to reduce systematic error is not clear. This paper deals with the question of bias propagation when the estimates that are averaged are bias prone. Both simple and weighted averages are examined. The idea of using averaging as a bias reduction technique is explored. When the weights used in the averaging process are themselves biased, the random error in the average is also affected, possibly adversely. A balance has to be struck between bias reduction requirements and random error reduction requirements
SPT Sequencing With Dependent Processing Times
No full textThis paper investigates the applicability of SPT-based strategies to situations where the processing time for a particular task is not known until some other task has been completed. For comparison a random strategy is used. Two cases are considered: the static case which assumes that all jobs to be processed are present in the shop, and the dynamic case in which jobs arrive randomly over time. The improvement in flow time resulting from the SPT-based strategy is quantified analytically for the static case. For the dynamic case some simulation results are presented. Both sets of results indicate that SPT is a very robust strategy which results in significant reductions in a wide variety of situations
Determining The Optimal Values Of Exponential Smoothing Constants – Does Solver Really Work?
Get PDFA key issue in exponential smoothing is the choice of the values of the smoothing constants used. One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE). We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants. We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants
Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?
Get PDFThis paper examines exponential smoothing constants that minimize summary error measures associated with a large number of forecasts. These forecasts were made on numerous time series generated through simulation on a spreadsheet. The series varied in length and underlying nature no trend, linear trend, and nonlinear trend. Forecasts were made using simple exponential smoothing as well as exponential smoothing with trend correction and with different kinds of initial forecasts. We found that when initial forecasts were good and the nature of the underlying data did not change, smoothing constants were typically very small. Conversely, large smoothing constants indicated a change in the nature of the underlying data or the use of an inappropriate forecasting model. These results reduce the confusion about the role and right size of these constants and offer clear recommendations on how they should be discussed in classroom settings
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Decision Making in a Standby Service System
No full textA standby service option allows a firm to lower its risk of not having sufficient capacity to satisfy demand without investing in additional capacity. Standby service options currently exist in the natural gas, electric, and water utility industries. Firms seeking standby service are typically large industrial or institutional organizations that, due to unexpectedly high demand or interruptions in their own supply system, look to a public utility to supplement their requirements. Typically, the firm pays the utility a reservation fee based on a nominated volume and a consumption charge based on the volume actually taken. In this paper, a single-period model is developed and optimized with respect to the amount of standby capacity a firm should reserve. Expressions for the mean and variance of the supplier\u27s aggregate standby demand distribution are developed. A procedure for computing the level of capacity needed to safely meet its standby obligations is presented. Numerical results suggest that the standby supplier can safely meet its standby demand with a capacity that is generally between 20 to 50% of the aggregate nominated volume
The Treatment Of Six Sigma In Leading Operations Management Textbooks
No full textThis paper critically examines the treatment of the statistical basis for Six Sigma and process capability in popular operations management textbooks. It discuss areas of confusion and suggest ways of treating the topic that make sense to instructors as well as students. Even though Six Sigma was introduced almost 30 years ago, misconceptions persist. In the textbooks we have found no consistency of approach or understanding of the statistical underpinnings (3.4 defects per million opportunities) of Six Sigma. Sometimes statements are made that are factually incorrect and cause frustration for students and instructors. Similar difficulties are encountered in discussions of the related concept of process capability. The paper suggests changes that will help resolve these issues and bring much-needed clarity to discussions of these important ideas. Students will find the material much more accessible and instructors will find it much easier to convey the concepts underlying this important topic
Random error in holistic evaluations and additive decompositions of multiattribute utility — an empirical comparison
No full textThis paper details the results of an empirical investigation of the random errors associated with decomposition estimates of multiattribute utility. In a riskless setting, two groups of subjects were asked to evaluate multiattribute alternatives both holistically and with the use of an additive decomposition. For one group, the alternatives were described in terms of three attributes, and for the other in terms of five. Estimates of random error associated with the various elicitations (holistic, single‐attribute utility, scaling constants, or weights) were obtained using a test‐retest format. It was found for both groups that the additive decomposition had significantly smaller levels of random error than the holistic evaluation. However, the number of attributes did not seem to make a significant difference to the amount of random error associated with the decomposition estimates. The levels of error found in the various elicitations were consistent with theoretical bounds that have recently been proposed in the literature. These results show that the structure imposed on the problem through decomposition results in measurable improvement in quality of the multiattribute utility judgements, and contribute to a greater understanding of the decomposition method in decision analysis
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