The North Atlantic Oscillation Simulated by Versions 2 and 4 of IAP/ LASG GOALS Model

The capabilities of two versions of the Global–Ocean–Atmosphere–Land–System model (i.e. GOALS–2 and GOALS–4) developed at State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), are validated in terms of the simulations of the winter North Atlantic Oscillation (NAO), which is currently the subject of considerable scientific interest. The results show that both GOALS–2 and GOALS–4 exhibit a realistic NAO signal associated with relatively reasonable spatial patterns of sea level pressure, surface air temperature, and precipitation. Generally speaking, the associated patterns of precipitation in GOALSs match better with the observation in comparison with the case of surface temperature. For the imprint of NAO on the ocean, or perhaps a coupling between the two fluids, the associated tripole patterns of the North Atlantic SST anomaly are presented distinctly in GOALS–2, for GOALS-4 however, this is not the case. Spatially, the models’ main deficiencies appear to be that the simulated Icelandic lows shift northward apparently, which in turn result in the blemish of GOALSs in reproducing the accompanied surface wind anomalies. For the interannual and even longer time scale variations of DJF sea level pressure (SLP) over the North Atlantic region, GOALSs reproduce the center with the strongest variability rationally, but the intensities are far weaker than the observation. Key words North Atlantic Oscillation (NAO) - Model evaluation - GOALS model This study was jointly supported by the National key Project (Grant No. 96-908-02-03), the Excel-lent National Key Laboratory Research Project (Grant NO. 49823002), Chinese Academy of Sciences (CAS) under grant “ Bai Ren Ji Hua” for “ Validation of Coupled Climate Models”, and IAP innova-tion fund (No.8-1204).The authors gratefully acknowledge Dv. Jin Xuingze, Mr. Liu Xiying in IAP /LASG, and Dr. Gong Daoyi in Geophysical Department of Peking University for providing ardent help.

HEURISTIC MODELING FOR A DYNAMIC AND GOAL PROGRAMMING IN PRODUCTION PLANNING OF CONTINUOUS MANUFACTURING SYSTEMS

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production, batch production and job shop systems, but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely, because of initial inadequate plant layout, stage by stage development of production lines, the purchase of second hand machineries from various countries, plurality of customers. A case of production system planning is proposed for a chemical company in which the above mentioned conditions are almost presented. The goals and constraints in this issue are as follows： （1） Minimizing deviation of customer＇s requirements. （2） Maximizing the profit. （3） Minimizing the frequencies of changes in formula production. （4） Minimizing the inventory of final products. （5） Balancing the production sections with regard to rate in production. （6） Limitation in inventory of raw material. The present situation is in such a way that various techniques such as goal programming, linear programming and dynamic programming can be used. But dynamic production programming issues are divided into two categories, at first one with limitation in production capacity and another with unlimited production capacity. For the first category, a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero- one model. At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that＇s how it is solved.

COAL QUALITY CONTROL

Coal storing and loading have much more influence on coal quality. In the paper, a goal programming model has been constructed to determine the ideal quantity extracting from stockpile and silos and a quality control model is inferred under the guidance of maximum theory of dispersed number and practice methods are given to meet production demand, with which a coal mine has achieved a better tech-economic result.

Methods and applications of DEA cross-efficiency:Review and future perspectives

The field of engineering management usually involves evaluation issues,such as program selection,team performance evaluation,technology selection,and supplier evaluation.The traditional self-evaluation data envelopment analysis(DEA)method usually exaggerates the effects of several inputs or outputs of the evaluated decision-making unit(DMU),resulting in unrealistic results.To address this problem,scholars have proposed the cross-efficiency evaluation(CREE)method.Compared with the DEA method,CREE can rank DMUs more completely by using reasonable weights.With the extensive application of this technique,several problems,such as non-unique weights and non-Pareto optimal results,have arisen in CREE methods.Therefore,the improvement of CREE has attracted the attention of many scholars.This paper reviews the theory and applications of CREE,including the non-uniqueness problem,the aggregation of cross-efficiency data,and applications in engineering management.It also discusses the directions for future research on CREE.