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Bin Zhang

Researcher at Sun Yat-sen University

Publications -  31
Citations -  1099

Bin Zhang is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Knapsack problem & Newsvendor model. The author has an hindex of 13, co-authored 28 publications receiving 962 citations. Previous affiliations of Bin Zhang include University of Science and Technology of China.

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Multi-item production planning with carbon cap and trade mechanism

TL;DR: In this article, the authors investigated the multi-item production planning problem with carbon cap and trade mechanism, in which a firm uses a common capacity and carbon emission quota to produce multiple products for fulfilling independent stochastic demands, and the firm can buy or sell the right to emit carbon on a trading market of carbon emission.
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Predicting corporate financial distress based on integration of support vector machine and logistic regression

TL;DR: An integrated binary discriminant rule (IBDR) for corporate financial distress prediction is developed by interpreting and modifying the outputs of the SVM classifiers according to the result of logistic regression analysis.
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Integration TRIZ with problem-solving tools: a literature review from 1995 to 2006

TL;DR: In this paper, a survey of TRIZ integration into other creativity tools, methods and philosophies using a literature review of publications from 1995 to 2006 is presented, and a categorical analysis on how TRIZ has been integrated with these tools based on publications of combining TRIZ with design problem-solving tools.
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A new approach of forecasting intermittent demand for spare parts inventories in the process industries

TL;DR: This paper develops a new approach for forecasting the intermittent demand of spare parts and shows that this method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.
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A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts

TL;DR: A hybrid forecasting approach is developed, which can synthetically evaluate autocorrelation of demand time series and the relationship of explanatory variables with demand of spare part, and produces more accurate forecasts of distribution of lead-time demands of spare parts than do current methods across almost all the lead times.