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Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making. 相似文献
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Long-term unemployment in Romania has grown in both absolute and relative terms in the last few years, leading to increased expenditures, both absolutely and in relation to unemployment benefits, for the support allowance and social assistance programs and for pensions to labor force drop-outs. The paper uses a variety of data sources, including registration information, labor force surveys, and our own survey of registered unemployed (SRU) to describe these trends in the characteristics of Romanian unemployment and to examine differences across unemployment benefit (UB), short-term and long-term support allowance (SA) recipients. We employ the data to estimate the transition flow probability from the UB to the SA program; discuss the work incentives, income maintenance effects, and public costliness of the labor market and social insurance (including pension and disability) policies; and investigate the effects of the policies and of other characteristics of the unemployed and the areas where they live on the hazard for the escape rate from unemployment for UB and SA recipients separately. 相似文献
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