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Purpose – This study aims to investigate the relationship between university and industry as two major infrastructures of national innovation system in all leading scientific and industrial settings. Design/methodology/approach – Large complex organizations with high technology that follow non-linear dynamic rules need to define concepts and adopt new approaches to achieve organizational efficiency and effectiveness. Among various models, a dynamic model of innovation was developed based on a joint investment between industry and university. Hence, the concepts of systems thinking and system dynamics were used. Findings – The results reveal three levels of industry and university communication from the lower levels to the higher levels. Originality/value – The value of this paper lies in adding two axes of “type of relationship” and “form of relationship” to the axis of “strength of relationship”, and a static three-dimensional space as a spatial capacity of the relationship between the industry and the university has been organized. Further, this is the first study that investigates the dynamic relationship between industry and university based on the self-organization theory and system thinking.

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In this paper we consider the production control of a failure prone manufacturing network using the Hedging Point Policy (HPP). This system consist of a network of machines with relationship constraints that can be in one of four states: operational, in repair, starved and blocked. Broken machines are subject to a repair process, and up time and repair time in each phase for each machines is assumed to be exponentially distributed. The demand for the product produced by the final machine is assumed to be a Poisson process. Unmet demand is either backlogged or lost. The objective of this paper is to find the optimal production rates of each machine so as to minimize the long run average inventory and backlog cost. In order to solve this problem we use a simulation based optimization method that combines stochastic optimal control theory, discrete event simulation, experimental design and Automated Response Surface Methodology (RSM). We include a numerical example to illustrate the effectiveness of the proposed methodology.