Management strategies for industrial laboratories with knowledge memory

Debasis Mitra, Qiong Wang

Research output: Contribution to journalConference articlepeer-review


We present a simplified abstraction of an industrial laboratory consisting of a two-stage network, Research (R) and Development (D). Ideas and prototypes are incubated in the R stage, the projects departing this stage are assessed, and, if favorable, the project proceeds to the D stage. Revenue is generated from the sale of products/solutions that are outputs of the D stage, and the sale and licensing of patents that are generated at both stages. In our discrete time model, in each time period the managers of the industrial laboratory are given a constant amount of money to invest in the two stages. The investments determine the capacities of the stages based on linear unit costs. A novel feature of the model is “knowledge stocks” for the stages, which represent the accumulated know-how from practicing research and development activities; higher knowledge stock implies lower cost. The memory in knowledge stocks makes current investment decisions have long term impact on costs and profits. Three strategies for profit maximization are investigated. In myopic profit maximization we show the existence of multiple equilibria and the phenomenon of state entrapment in suboptimal regimes, which are absent in the other strategies. Numerical results illustrate the main features of the model.

Original languageEnglish (US)
Pages (from-to)210-216
Number of pages7
JournalPerformance Evaluation Review
Issue number3
StatePublished - Mar 20 2018
Event35th IFIP International Symposium on Computer Performance, Modeling, Measurements and Evaluation, IFIP WG 7.3 Performance 2017 - New York, United States
Duration: Nov 13 2017Nov 17 2017

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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