Knowledge management in production networks

Problem Description
The (re)design of production networks in the food industry is a complex process due to 2 requirements. First, there is a strong interaction between the sequencing of processing activities, and the processing conditions de-pend on the previous and following processing activities. Furthermore, the processing conditions are closely related to product quality that has to meet stringent requirements in the food industry. Second, targets for the production network (like e.g. throughput and lead time) can change considerably since the market changes con-tinuously.
Complicated issues in the design of foreseen production networks are an evaluation of their feasibility, an as-sessment of the effects of a different sequencing of activities, or of changes in the processing conditions. For these issues, simulation studies or approximating analysis offer opportunities.
However, there are 2 obstructions for applying simulation studies or approximating analysis. First, the develop-ment (design, implementation and validation) of simulation or analysis models is very time intensive. Reuse of existing models might offer possibilities. Only then existing models should be easy to adapt, and allow for a clear communication between developers. Next, existing simulation and analysis methodologies do not support the modelling of production processes in the semi-process industry (like e.g. the food industry). In this industry both continuous and discrete production operations are encountered.

Objective
This PhD project aims the development of a methodology for conceptual modelling of production networks in the food industry and analysing their feasibility. This methodology will be implemented in a prototype decision support system for (re)designing production networks.

Short Research Description
During this project the following 5 tasks will be performed.
The literature will be surveyed on existing modelling approaches and analysis techniques.
A conceptual-modelling methodology will be developed that uses a standardised terminology and reveals all modelling assumptions.
Analysis techniques that use tools like e.g. Petri nets, or minimax algebra, are defined that facilitate the search for feasible or even best designs concerning process sequences, processing conditions, and control strategies.
A prototype decision support system will be developed.
The methodology will be tested through performing cases studies.