Simulation and Optimization

Proposta de tesi Investigadors/es Grup de Recerca

Optimization and simulation of industrial and engineering systems

Internet Computing & Systems Optimization (ICSO) is an official IN3 programme supported by the DPCS research group. One of the main research topics in ICSO is the development of new hybrid algorithms and methods which combine applied optimization (eg heuristics and metaheuristics), discrete-event simulation and data analysis to support decision-making processes in realistic environments. In particular, we are interested in the real-life application of these algorithms in the contexts of logistics, transportation and production systems. Thus, the doctoral theses will be related to any of the following topics: rich (real-life) vehicle routing problems, real-life scheduling problems, green logistics, intelligent transport systems, horizontal collaboration in logistics, etc. These topics represent important challenges for the industrial sector in any developed country, which explains their relevance in the context of current international research.

Dr Angel A Juan 

Dr Daniel Riera Terrén

ICSO

Analytics in smart cities, transportation & logistics, and management

Descriptive business analytics (DBA) refers to the pre-processing and processing of historical data gathered by companies in order to describe the real business context, generate information, and make rational decisions from the acquired knowledge. After describing the real business context, one can also benefit from predictive business analytics (PdBA), which relies on the use of time series analysis, regression models, and even machine learning methods in order to forecast the future or predict how certain business factors will evolve under scenarios of uncertainty. Finally, prescriptive business analytics (PsBA) aims at supporting complex decision-making processes in business through the use of optimization and simulation algorithms (including metaheuristics and simheuristics). These algorithms allow managers to “learn from the future” (by performing what-if analyses), significantly increasing the efficiency of business processes and systems, reducing operational costs and, at the end of the day, raising companies’ profits. PsBA have many applications in a variety of fields, including smart cities, transportation and logistics, finance, healthcare, tourism, etc. The aim of this line of research is to explore, from an interdisciplinary and IT-based perspective, some of the almost unlimited applications of analytics to any of the previously described service industries.

Dr Angel A Juan 

Dr Daniel Riera Terrén

ICSO

Uncertainty and sensitivity analysis for model verification and validation

Sensitivity analysis is an essential tool to verify mathematical models, algorithms, system of indicators, and any form of quantification. It answers questions such as "Are the results from a particular model more sensitive to changes in the model and the methods used to estimate its parameters, or to changes in the data?" While uncertainty analysis looks at the uncertainty in the model prediction, modern quantitative and global approaches to sensitivity analysis look at which among a set of uncertain input factors or parameters or assumptions has more importance in generating uncertainty in the output.

The thesis will explore new methods for sensitivity analysis, including the use of emulators as well as applications to real life examples of mathematical models used in industrial, regulatory or decision models.

 

Prof Andrea Saltelli 

Dr Samuele Lo Piano

OpenEvidence