ساخت و ارزیابی پرتفوی متعادل پروژه های "تحقیق و توسعه" همراه با اثرات متقابل:روش شناسی مبتنی بر DEA
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|17250||2006||22 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 11949 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : European Journal of Operational Research, Volume 172, Issue 3, 1 August 2006, Pages 1018–1039
We propose and demonstrate a methodology for the construction and analysis of efficient, effective and balanced portfolios of R&D projects with interactions. The methodology is based on an extended data envelopment analysis (DEA) model that quantifies some the qualitative concepts embedded in the balanced scorecard (BSC) approach. The methodology includes a resource allocation scheme, an evaluation of individual projects, screening of projects based on their relative values and on portfolio requirements, and finally a construction and evaluation of portfolios. The DEA–BSC model is employed in two versions, first to evaluate individual R&D projects, and then to evaluate alternative R&D portfolios. To generate portfolio alternatives, we apply a branch-and-bound algorithm, and use an accumulation function that accounts for possible interactions among projects. The entire methodology is illustrated via an example in the context of a governmental agency charged with selecting technological projects.
Portfolio selection problems can be decomposed into two major classes: dynamic vs. static problems. In the dynamic class (Bard et al., 1988 and Cooper et al., 1997), at every decision point there are projects that have already started—denoted as active projects, and a set of proposed projects—known as candidate projects. The decision space includes both groups, and may involve the continuation of active projects at various budgeting levels; termination of other active projects; and launching new projects. In this paper we focus on the class of static portfolio selection problems (e.g., Beaujon et al., 2001 and Basso and Peccati, 2001). This class addresses situations in which all the projects that are considered at the decision point are candidates. The static setting may occur in both the business and the government sectors. As an example of the former, consider a venture capital firm that wishes to invest resources in a set of new technologies. It sets aside a certain budget dedicated for this purpose and announces a “call-for-proposals” to solicit proposals in various areas. Similarly, in the not-for-profit sector, a governmental agency may have a certain budget dedicated for new projects. Decision points may occur once a period, and the decision is which new projects to support.
نتیجه گیری انگلیسی
This paper describes a methodology for R&D portfolio analysis in which effectiveness, efficiency, and balance considerations can be integrated. The methodology is based on relative evaluation of entities (projects or portfolios), and uses an evaluation model that was inspired by an integrated DEA–BSC model that was first presented by Eilat et al. (2004). The approach in this paper may serve as an alternative to the conventional multi-dimensional knapsack approach, which applies a mixed integer-programming model to find an optimal portfolio with respect to a well-formulated objective function and multiple resource constraints. The approach here is not restricted to finding an optimal solution or to one objective, but rather to evaluate alternative portfolios in the presence of multiple objectives and possible interactions among the projects. The methodology was designed to accommodate uncertain and subjective data. This is usually what is available in such decision problems. It also allows for comparison of alternatives without requiring strict weights or conversion factors among variables, and it can combine qualitative, intangible data, together with quantitative data.