Daniele Giovanni Gioia: Mathematical Engineer


Daniele is a Mathematical Engineer pursuing a Ph.D. in dynamic optimization at Politecnico di Torino funded by the Politecnico itself, the Università degli Studi di Torino, the Fondazione CRT as well as other major mathematical associations: the Consorzio Interuniversitario per l’Alta Formazione in Matematica and the Istituto Nazionale di Alta Matematica. His application context deals with decision-making under uncertainty in engineering/management applications. Some examples are retail management and assortment planning, robust machine learning, and stochastic programming.Practically speaking, someone pays him to generate wrong models of real problems. However, this is what people call engineering, and unexpectedly, it (may) work too.Recently, he worked as a visiting researcher at the Technical University of Munich for a collaborative research project in the area of multi-echelon perishable inventory management at the logistics and supply chain management department.Daniele earned his master's degree in mathematical engineering at Politecnico di Torino and Technische Universiteit Eindhoven summa cum laude. During his master, he was a post-graduate research assistant for the development of software for the generation of a polyhedric mesh in domains with randomly generated interfaces for F.E.M. (if you want to compute things, I swear it is better they are convex). Whilst studying, he worked as an academic tutor in various subjects, including programming and scientific computing and numerical methods.When he is not modeling by equations, Daniele plays his trumpet (still trying to play the IV Arban's carnival of Venice variation with no errors) and does fitness (he once got a fitness trainer certificate, nothing serious (he was bored)). He earned a pre-academic certification in trumpet at the superior institute of musical studies of Caltanissetta.


A diverse array of application problems may be tackled within the framework of dynamic optimization under uncertainty, which includes stochastic programming with recourse, adjustable robust optimization and dynamic programming. Some of the topics of Daniele's research are listed below.

Control and design optimization of Wave Energy Converters

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The energy problem related to the pursuit of alternatives to fossil fuels is an active challenge for the entire world. Unlike other types of energy conversion technologies, such as photovoltaic plants or wind turbines, wave energy converters (WECs) have not reached a sufficient level of technological maturity and two of the most crucial issues are: the development of suitable advanced control strategies for WEC devices and the optimal design in terms of cost and extracted energy.
Daniele's research focused on strategies that apply a Gaussian Process Regression (GPR) optimization approach to compute the control action. He is the author of the article: Data-driven control of a Pendulum Wave Energy Converter: A Gaussian Process Regression approach, published by Ocean Engineering. He also lent a hand on techniques to robustify the physical design against mechanical mismatches, co-authoring the conference proceedings Wave Energy Converter Optimal Design Under Parameter Uncertainty presented at the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering.

Assemble-to-Order Problems

Assemble-to-order is a production strategy where components are manufactured under demand uncertainty and end items are assembled only after demand is realized. This strategy is commonly applied to hedge against significant uncertainties in the order of the end items, naturally leading to Two-Stage and Multi-Stage Stochastic Programming formulations.
Daniele's research aims to apply reinforcement learning strategies to reduce the Multi-Stage complexity and study different multistage multi-item models, showing how they behave, based on the usage of the available information.
A book chapter contribution that tackles these problems with risk-averse models in a two-stage stochastic linear programming setup, considering the introduction of a classical risk measure from finance is available in
Optimization and Decision Science: Operations Research, Inclusion and Equity, part of AIRO Springer Series.
A PrePrint that deals with seasonality, bimodality, and correlations in the distribution of end items demand, where the approximation of terminal values and rolling horizon simulations are applied is available on ArXiv. The code is open-source and available on GitHub.

Inventory control of perishable items

Descrizione #1

The new sustainable and development goals are considered a blueprint to achieve a better and more sustainable future for all. Reduction of losses and overstocking on supply chains and retail environments are central topics, even more, when the considered items have a short life (i.e. food, blood platelets). Daniele's current research deals with advanced inventory management control systems that employ different methods, e.g., simple base-stock policies and deep actor-critc reinforcement learning strategies.A study that allows for a multi-item setting with substitution between similar goods, deterministic deterioration, delivery lead times, and seasonality was presented in Nantes at the 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM, with the title Inventory management of vertically differentiated perishable products with stock-out based substitution. This work was extended and presented in more detail and comprehensively in Computer & Operation Research, in the article Simulation-based inventory management of perishable products via linear discrete choice models. An open-source simulation-based framework has been developed, filling the lack of open-source libraries in the perishable inventory literature, and released on GitHub. The GitHub page also contains a corrigendum for a small error in the indexes of a summation of the latter article.

Financial portfolios optimization

The academic world is full of research that aims to optimize financial portfolios in commodity or stock markets. The information sources and the related methodologies to extract insights are countless. Moment-based methods like the well-known Markowitz's model have many model parameters and, in addition to requiring considerable computational effort, raise serious questions about the reliability of these values. Often, distributions are fat-tailed, and pure moment-based solutions completely meaningless. The current research of Daniele undertakes the building of decision support systems able to make usable whatever performance and risk metric the decision-maker consider adequate (e.g., deep learning strategies), allowing for known instruments like technical and fundamental analysis, and attempting to overcome some of the inherent complexities and limitations of the most classical strategy. An academic article titled Early portfolio pruning: a scalable approach to hybrid portfolio selection has been published in Knowledge and Information Systems by Springer Nature. There, a hybrid approach that combines itemset extraction and Markowitz’s model logic, generalizing the idea of balancing profit and risk, but dealing with sets of candidate portfolios rather than with single stocks and allowing for technicals, fundamentals and risk measures not necessarily related with poor estimation is presented.

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If you want to talk about decisions under uncertainty, baroque music or deadlifts.