The classical EPQ model has been used for a long ti me and is widely accepted and implemented. Nevertheless, the analysis for finding an economic lot size has based on a number of unrealistic assumptions. A common unr...The classical EPQ model has been used for a long ti me and is widely accepted and implemented. Nevertheless, the analysis for finding an economic lot size has based on a number of unrealistic assumptions. A common unrealistic assumption in using EPQ is that all units produced are of good quali ty. The classical EPQ model shows that the optimal lot size will generate minimum ma nufacturing cost, thus producing minimum setup cost and inventory cost. However, this is only true if all products manufactured in the process are assumed to be of good quality (i.e. all products are within the specification limits). In rea lity this is not the case, therefore, it is necessary to consider the cost of im perfect quality items, because this cost can influence the economic lot size. Ma ny studies and recent papers have indicated that there is a significant relation ship between economic production lot size and process/product quality. However, their models included either the imperfect quality items (not necessarily de fective) which are to be sold at a discounted price or defective items which can be reworked or rejected. The aim of this paper is to provide a framework to integrate three different sit uations (discounted pricing/rework/reject) into a single model. 100% inspection is performed in order to distinguish the amount of good quality items, imper fect quality items and defective items in each lot. In this paper, a mathematica l model is developed, and a numerical example is presented to illustrate the sol ution procedures. It is found that the economic production lot size tends to inc rease as the average percentage of imperfect quality items and defectives (rejec ted items) increases.展开更多
A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimizati...A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.展开更多
A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial partic...A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.展开更多
文摘The classical EPQ model has been used for a long ti me and is widely accepted and implemented. Nevertheless, the analysis for finding an economic lot size has based on a number of unrealistic assumptions. A common unrealistic assumption in using EPQ is that all units produced are of good quali ty. The classical EPQ model shows that the optimal lot size will generate minimum ma nufacturing cost, thus producing minimum setup cost and inventory cost. However, this is only true if all products manufactured in the process are assumed to be of good quality (i.e. all products are within the specification limits). In rea lity this is not the case, therefore, it is necessary to consider the cost of im perfect quality items, because this cost can influence the economic lot size. Ma ny studies and recent papers have indicated that there is a significant relation ship between economic production lot size and process/product quality. However, their models included either the imperfect quality items (not necessarily de fective) which are to be sold at a discounted price or defective items which can be reworked or rejected. The aim of this paper is to provide a framework to integrate three different sit uations (discounted pricing/rework/reject) into a single model. 100% inspection is performed in order to distinguish the amount of good quality items, imper fect quality items and defective items in each lot. In this paper, a mathematica l model is developed, and a numerical example is presented to illustrate the sol ution procedures. It is found that the economic production lot size tends to inc rease as the average percentage of imperfect quality items and defectives (rejec ted items) increases.
基金supported by the Key Project of National Social Science Foundation(12AZD111)the National Project for Education Science Planning(EFA110351)+2 种基金the Humanities and Social Science Foundation of Ministry of Education of China(12YJCZH207)the Key Project for Jiangsu Province Social Science Foundation(12DDA011)the Jiangsu College of Humanities and Social Sciences outside Campus Research Base:Chinese Development of Strategic Research Base for Internet of Things
文摘A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.
文摘A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.
