Replenishment decision support system based on modified particle swarm optimization in a VMI supply chain

This paper proposes a replenishment decision support system, based on response surface methodology (RSM) and modified traveling particle swarm optimization (TPSO). Cross-border cooperation lengthens the distance for transportation and in turn widens the disparity in inventory cost among business partners, thus the accentuated importance of inventory management. Therefore, this paper solves a two-stage stochastic dynamic lot-sizing problem with two-phased transportation cost under a vendor managed inventory. Modified TPSO is proposed to solve a sub-problem, the nonlinear mixed integer programming. In this algorithm, which is executed with a perturbation policy, each move is a feasible solution. RSM is being used to determine the optimal replenishment condition. The result of the experiment indicates that the proposed approach lowers the cost for both the buyer and the vendor. Moreover, the solution quality using modified TPSO was tested and compared with that of the Solver tool in Excel and 3 lot-sizing decision rules

Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

<div><p>Objectives</p><p>Rotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone.</p><p>Methods</p><p>In this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into “tear” and “no tear” groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models.</p><p>Results</p><p>Our proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear).</p><p>Conclusions</p><p>Our predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears.</p></div

Likelihood ratio.

a<p>LR+, LR−: likelihood ratios for positive and negative results, respectively.</p>b<p>Diagnostic odds ratio: a measure of the effectiveness of a diagnostic test.</p

Variable list.

<p>Variable list.</p

The use of the Fagan's nomogram (a straight line through the pretest probability of 25% and the LR+ of 17.40 yields a posttest probability of 85%; a straight line through the pretest probability of 25% and the LR- of 0.14 yields a posttest probability of 4%).

<p>The use of the Fagan's nomogram (a straight line through the pretest probability of 25% and the LR+ of 17.40 yields a posttest probability of 85%; a straight line through the pretest probability of 25% and the LR- of 0.14 yields a posttest probability of 4%).</p

Prediction performance.

a<p>average of 20 repetitive 10-fold experiments.</p>b<p>standard deviation.</p><p>* statistically significant (p<0.05) difference comparing to logistic regression model.</p

Demographic variables of the 169 patients who were diagnosed with suspected rotator cull tear by the clinical examinations.

<p>Demographic variables of the 169 patients who were diagnosed with suspected rotator cull tear by the clinical examinations.</p

Characteristics of the 169 patients categorized as tear and no tear groups using MRI imaging as a reference standard.

<p>T-test for continuous variable and Pearson Chi-square test for dichotomous variable.</p

Application of Generative Adversarial Network and Diverse Feature Extraction Methods to Enhance Classification Accuracy of Tool-Wear Status

The means of accurately determining tool-wear status has long been important to manufacturers. Tool-wear status classification enables factories to avoid the unnecessary costs incurred by replacing tools too early and to prevent product damage caused by overly worn tools. While researchers have examined this topic for over a decade, most existing studies have focused on model development but have neglected two fundamental issues in machine learning: data imbalance and feature extraction. In view of this, we propose two improvements: (1) using a generative adversarial network to generate realistic computer numerical control machine vibration data to overcome data imbalance and (2) extracting features in the time domain, the frequency domain, and the time&ndash;frequency domain simultaneously for modeling and integrating these in an ensemble model. The experiment results demonstrate how both proposed modifications are reasonable and valid