Abstract
The P-graph framework is a combinatorial approach to synthesising and optimising process networks. It is very efficient in handling problems with high combinatorial complexity and has shown great superiority in reducing the related computational burden. Over the years, it has proven its efficiency in solving topologically and combinatorically challenging problems. Many successful applications to scientific and real-life problems have been produced, demonstrating the benefit potential. The application areas range from the initial chemical process synthesis to identifying the mechanisms of chemical and biochemical reactions, supply chains optimisation, regional resource planning, crisis management, evacuation planning and business process modelling. There have been tools of several generations implementing the P-graph framework, with a simple user interface, but featuring serious data input requirement. The P-graph framework also allows sensitivity analysis and produces usually a set of recommended solutions as opposed to the usual single output from straight applications of MP.
The current contribution makes a critical overview of the achievements from applying the P-graph framework and the main issues to be dealt with. Based on that, a set of recommendations is made on the necessary future development of the implementations regarding modelling capability, data and algorithmic interfaces, representation of the modelled networks, as well as complexity management.
