relative-value

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.

The problem of choosing weights for beams in a multifield plan which maximizes tumor dose under conditions that recognize the volume dependence of organ tolerance to radiation is considered, and its solution described. Structures are modelled as collections of discrete points, and the weighting problem described as a combinatorial linear program (LP). The combinatorial LP is solved as a mixed O/l integer program with appropriate restrictions on normal tissue dose. The method is illustrated through the assignment of weights to a set of 10 beams incident on a pelvic target. Dose-volume restrictions are placed on surrounding bowel, bladder, and rectum, and a limit placed on tumor dose inhomogeneity. Different tolerance restrictions are examined, so that the sensitivity of the target dose to changes in the normal tissue constraints may be explored. It is shown that the distributions obtained satisfy the posed constraints. The technique permits formal solution of the optimization problem, in a time short enough to meet the needs of treatment planners. Radiation therapy, Computer assisted radiation treatment planning, Linear programming, Optimization.

We consider a productive asset, called equipment or capital good, and we examine the properties of, as well as the interactions between, the operating policies, which are determined by its optimal utilization and maintenance, and the capital policy of scrapping, which defines the optimal time when the productive asset is retired from its current use. Starting with an abstract model and using the approach of optimal control, initially we characterize the various types of equipment by assigning to them a single total profit index, which indicates how the above policies affect the flow of operating revenues plus capital gains or losses. This index is a function of market-determined prices. So using it we then investigate how the operating and capital policies are influenced by the rate of discount, the price of new equipment, and the rise or fall of the price of new equipment relative to the value of its output. Among other interesting results, we find that the effects of these prices on the nature and interactions of optimal policies depend crucially on whether the equipment is of the usual profit making type, where output is the main source of revenue, or of the antique type, where the main source of revenue is capital gains.