A Chance-Constrained Model for Real-Time Control in Research and Development Management

Abstract

Funding of research projects is considered as encompassing three stages: (1) an initial short run plan for funding based upon projected regular demands and availability subject to random deviations; (2) adjustment of the initial plan to take into account the actual regular demands and availability and the funding of significant break-throughs which occur at random intervals preempting other demands; and (3) a plan for longer-run availability and demands which constitute a "posture" desired subsequent to the funding adjustments of (2). The essence of the distribution of the unexpected demands is multi-modality with low probability of occurrence but high resource demand when they do occur. This approach represents a substantial departure from the usual planning model development which produces only an optimal plan based on forecasted developments without provision for adjustment when the forecasted events actually materialize and additional unexpected demands are placed on resources. The adjustment process explored here--which provides the mechanism for optimal implementation of the original plan or control of resource allocation-enables optimal response to information received in "real-time" avoiding the frequently observed over- or under-response to receipt of such information without reference to the impact of the interim decision on future capabilities.

R & D project selection models: An assessment

“…Linear [4], [9], [11] and nonlinear [5], [36], [52] models have linear and nonlinear objective functions, respectively, linear constraint functions, and continuous decision variables (with the exception of integer models); however, some allocate R&D personnel or man-hours [4], [11] rather than determine project budgets. Also, stochastic [18], [22], [36] and goal programming [18] models have been suggested. A plethora of R&D project selection/resource allocation models have appeared.…”

A comprehensive framework for project selection problem under uncertainty and real-world constraints

“…At below, we present the most relevant research works dealing with uncertainty in the project selection area. Charnes and Stedry (1966) proposed a technique named as chance-constrained programming model in which random variables are defined to consider uncertainty for availability of facilities that are required for performing R&D projects. But, other kinds of uncertainties are not considered in this method.…”

One-stage R&D portfolio optimization with an application to solid oxide fuel cells

“…Stochastic programming modifies mathematical programming to explicitly incorporate probabilistic outcome scenarios. Surprisingly, it has been used only rarely in an R&D setting, see [14,17,43] and [36] for early examples. More recently, [23] use stochastic programming to model a multi-staged mixed portfolio of R&D projects and securities without any project interactions.…”