績效考核對多目標(biāo)管理的優(yōu)化-wenkub
2022-12-27 23:35:48 本頁面
【正文】 在執(zhí)行績效考核時(shí)是否盡責(zé)，對于考核結(jié)果的審核或者審批從來都是走走過場。從經(jīng)濟(jì)學(xué)角度來說，既然任何一項(xiàng)管理都需要支付成本，那么是否值得投入成本進(jìn)行管理在于該項(xiàng)管理獲得的效益是否高于所投入的成本。 員工對于績效考核結(jié)果表示關(guān)注最主要原因在于他們期望自己的工作結(jié)果能夠在薪酬或者職業(yè)發(fā)展上得到回報(bào)，所以企業(yè)現(xiàn)有的薪酬水平或者職業(yè)機(jī)會對于被考核者來說是有吸引力的是有效績效考核的重要前提。 Performance Assessment of Multiobjective Optimizers With many multiobjective optimization problems,knowledge about the Paretooptimal front helps thedecision maker in choosing the best promise instance, when designing puter systems,engineers often perform a socalled design space exploration to learn more about the tradeoff surface. Thereby, the design space is reduced to the set ofoptimal tradeoffs: a first step in selecting an appropriate , generating the Paretooptimal front canbe putationally expensive and is often infeasible, because the plexity of the underlying application prevents exact methods from being applicable. Evolutionary algorithms (EAs) are an alternative: they usually do not guarantee to identify optimal tradeoffs but try to find a good approximation, ., a set of solutions that are (hopefully) not too far away from the optimal front. Various multiobjective Eas are available, and certainly we are interested in the technique that provides the best approximation for a given problem. For this reason, parative studies are conducted, they aim at revealing strengths and weaknesses of certain approaches and at identifying the most promising algorithms. This,in turn, leads to the question of how to pare the performance of multiobjective notion of performance includes both the quality of the oute as well as the putational resources needed to generate this oute. Concerning the latter aspect, it is mon practice to keep the number of fitness evaluations or the overall runtime constant— in this sense, there is no difference between single and multiobjective optimization. As to the quality aspect, however, there is a difference. In singleobjective optimization, we can define quality by means of the objective function: the smaller (or larger) the value, the better the solution. In contrast, it is not clear what quality means in the presence of several optimization criteria: closeness to the optimal front, coverage of a wide range of diverse solutions, or other properties? Therefore, it is difficult to define appropriate quality measures for approximations of the Paretooptimal front, and as a consequence. Which strategy is better is not a matter of opinion but of munications ca
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