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

Charnes, A.; Stedry, Andrew C.

doi:10.21236/ad0621512

Cited by 4 publications

(3 citation statements)

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“…To take such situations into account, sophisticated techniques such as goal and integer programming, chance constraints, and system decomposition were introduced into R&D project selection. (See in particular Charnes and Stedry, 1964;Taylor, Moore and Clayton, 1982;Jackson, 1983;Madey and Dean, 1985;Mandakovic and Souder, 1985;Vepsalainen and Lauro, 1988;Bard and Feinberg, 1989).…”

Section: The Mathematical Programming Approachmentioning

confidence: 99%

Selecting R&D Projects for Technology-Based Innovation

Cheung

Greenfield

Liao

2009

Journal of General Management

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A vital question incorporated under the fundamental corporate strategy of technology-based innovation is that of ensuring that R&D would most eectively exploit technological change in the development of new or enhanced products and services. The literature contains a profusion of models, methods and techniques which guide R&D project selection. Three approaches are particularly important: one based on mathematical programming, one where project selection is understood as a process rationally embedded in the corporate behavioural, organisational, and informational structure, and one which requires projects to be strategically consistent-andintegrated. To assist the bounded-rational manager faced with this embarras de choix, the authors propose a heuristic framework which respects both the constraint imposed by decision costs and the principle that to optimise the use of knowledge as many as possible of the results from the literature should enter into the ®rm's decision-information shortlist. Exploiting this framework, the manager can readily compare the dierent approaches under the normative methodology of testing the empirical relevance of their assumptions against a core model description of the ®rm's`real world' constructed according to the methodology of scienti®c realism. Once an approach which is methodologically best (most realistic) is found, the decision process can be re®ned upon further considerations of bounded rationality to determine a model, method, or technique under this approach which is simultaneously knowledge-optimal and operationally-satis®ced.

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Section: The Mathematical Programming Approachmentioning

confidence: 99%

Selecting R&D Projects for Technology-Based Innovation

Cheung

Greenfield

Liao

2009

Journal of General Management

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“…The first is devoted to decision theoretic models. More explicitly, a dynamic R&D investment problem is treated as a control problem facing a single firm without rivalry (see for example Charnes and Stedry (1966), Lucas (1971), Kamien and Schwartz (1971), Deshmukh and Chikte (1977), Roberts and Weitzman (1981), Gallini and Kotowitz (1985), Grossman and Shapiro (1985a». The main objective in these studies is to characterize qualitatively the time pattern of R&D expenditures over the completion time of the project.…”

Section: Introductionmentioning

confidence: 99%

Optimal R&D programs in a random environment

Posner

Zuckerman

1990

Journal of Applied Probability

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Our study examines a stochastic R&D model with flexible termination time and without rivalry. Specifically, we assume a stochastic relationship between expenditures rate and the project's status. Furthermore, the termination time of the project is incorporated into the R&D model as a decision variable by allowing the controller to ‘sell' the obtained technology from the project at any point of time. The proposed framework extends the classical approach in the R&D literature.The main purpose of our study is to determine the optimal stopping time of the project and to characterize qualitatively the firm's expenditure strategy. We show that under certain realistic conditions, the optimal stopping strategy is a control limit policy. Furthermore, the research effort increases monotonically over the development time of the project.

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“…See, e.g., Miller and Wagner [20]. See also Charnes and Stedry [12]. For convenience we shall choose all p. identical.…”

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confidence: 99%