Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …
Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review
The rise of the damage to energy systems caused by both natural and man-made disruptive
events and the connection between energy systems and socio-economic systems have …
events and the connection between energy systems and socio-economic systems have …
The role of energy security and resilience in the sustainability of green microgrids: Paving the way to sustainable and clean production
M Kiehbadroudinezhad… - Sustainable Energy …, 2023 - Elsevier
Besides having adverse environmental impacts, power production from conventional
sources is severely threatened by uncertainties, such as the COVID-19 pandemic and …
sources is severely threatened by uncertainties, such as the COVID-19 pandemic and …
Multi-objective optimization applications in chemical process engineering: Tutorial and review
GP Rangaiah, Z Feng, AF Hoadley - Processes, 2020 - mdpi.com
This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of
the 5 steps to create, solve, and then select the optimum result. Unlike single-objective …
the 5 steps to create, solve, and then select the optimum result. Unlike single-objective …
Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in chemical engineering applications
Optimization in chemical engineering often involves two or more objectives, which are
conflicting. Multiobjective optimization (MOO) generates a set of equally good solutions from …
conflicting. Multiobjective optimization (MOO) generates a set of equally good solutions from …
The resilience of critical infrastructure systems: A systematic literature review
Risk management is a fundamental approach to improving critical infrastructure systems'
safety against disruptive events. This approach focuses on designing robust critical …
safety against disruptive events. This approach focuses on designing robust critical …
Multi-scale integration for enhanced resilience of sustainable energy supply chains: Perspectives and challenges
The projected increase in global energy consumption, along with recent upheavals such as
extreme weather events due to climate change and geopolitical turmoil, motivates the …
extreme weather events due to climate change and geopolitical turmoil, motivates the …
Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big …
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for
optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty …
optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty …
Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty
Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and
sustainable electricity supply chain network design. Uncertainty is always found in electricity …
sustainable electricity supply chain network design. Uncertainty is always found in electricity …
Establishing a frame of reference for measuring disaster resilience
Due to the increasing occurrence of disruptions across our global society, it has become
critically important to understand the resilience of different socio-economic systems, ie, to …
critically important to understand the resilience of different socio-economic systems, ie, to …