Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, efficient manipulation of large amount data for uncertain parameters constitutes crucial condition effectively tackling stochastic programming problems. this context, work proposes new data-driven Mixed-Integer Linear Programming (MILP) model Distribution & M...

Abstract We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. consider several problems arising in process, including training system to be robust rare events, testing its performance under different conditions, debugging failures. show how can help address these by specifying distributions encodin...

Scenarios are indispensable ingredients for the numerical solution of stochastic optimization problems. Earlier approaches for optimal scenario generation and reduction are based on stability arguments involving distances of probability measures. In this paper we review those ideas and suggest to make use of stability estimates based on distances containing minimal information, i.e., on data ap...

Scenario-based training has shown to be an effective instructional approach. However, it can be difficult for instructors to create scenarios as they are time-consuming to develop. This can result in the same scenarios being re-used, reducing training effectiveness. Furthermore, scenarios are based on training objectives and can vary in complexity. Researchers have investigated methods to simpl...

Using second-generation biomass and biofuel deal with environmental pollution and CO2 emissions. Therefore, this paper design an integrated multi-period bi-objective biofuel supply chain network using support vector machine (SVM) and economic analysis to reduce the cost of generating biofuels and CO2 emissions. The economic analysis consists of three scenarios for supplyin...