Trust - Networking - Innovation

From innovation point of view the agri-food industry is seen as matured branch of the economy, where revolutionary new products and processes are very rare. Especially the SMEs are in squeezing situation: they have to fit very sharp prerequisites and demands on one side and very much constrained resources to give them power in order to formulate appropriate answers on the other side. They are looking for partners beyond the boundaries of their organization, mainly with other firms, universities, research organisations and government agencies. Adopting an effective innovation process to successfully introduce and develop new products to the market has become one of the most important strategies for food companies. The innovation dimension of networking activity contributes to growing network complexity, which in turn also affects the nature of traditional governance structure. Trust and other relational factors are playing an increasing role in these structures. Our research interest is whether the trust as coordination form of governance structure plays significant role in the Hungarian agri-food industry. Empirical data was drawn from a survey carried out in Central Hungary and aiming at the research of cooperation and knowledge management within the SMEs of the food economy. Structural Equation Modelling (SEM) is applied in order to determine the relationship among the three (Trust, Networking, Innovation) latent factors. We have found that trust plays significant positive role in increasing networking activity and innovation, but the extent of it is less than expected.

Improved stochastic optimization of railway timetables

We present a general model to find the best allocation of a limited amount of supplements (extra minutes added to a timetable in order to reduce delays) on a set of interfering railway lines. By the best allocation, we mean the solution under which the weighted sum of expected delays is minimal. Our aim is to finely adjust an already existing and well-functioning timetable. We model this inherently stochastic optimization problem by using two-stage recourse models from stochastic programming, building upon earlier research from the literature. We present an improved formulation, allowing for an efficient solution using a standard algorithm for recourse models. We show that our model may be solved using any of the following theoretical frameworks: linear programming, stochastic programming and convex non-linear programming, and present a comparison of these approaches based on a real-life case study. Finally, we introduce stochastic dependency into the model, and present a statistical technique to estimate the model parameters from empirical data.