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Artificial Intelligence and Its Application in Optimization under Uncertainty [chapter]

Saeid Sadeghi, Maghsoud Amiri, Farzaneh Mansoori Mooseloo
2021 Artificial Intelligence  
This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning  ...  This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making  ...  In this regard, this chapter reviews recent advances in data-driven optimization that highlight the integration of mathematical programming and ML for decision-making under uncertainty and identifies potential  ... 
doi:10.5772/intechopen.98628 fatcat:mtbuaqghgvha3fa64osyahca2q

Optimization Study of a Parametric Vehicle Bumper Subsystem Under Multiple Load Cases [chapter]

Laszlo Farkas, Cedric Canadas, Stijn Donders, Herman Van der Auweraer, Danny Schildermans
2010 Recent Advances in Optimization and its Applications in Engineering  
To succeed in such a challenging environment, manufacturers address the complex design problems by means of virtual modelling and simulation procedures that enable optimizing the performance of such subsystems  ...  as early as possible in the design timeline.  ...  In this paper, this rationale is worked out for the optimized bumper design with uncertainty in the geometry and shell thickness definitions.  ... 
doi:10.1007/978-3-642-12598-0_42 fatcat:7rb5suy3vrcenhjynnp4lsvxfi

Top 10 reviewers for Environmental Modelling and Software in 2014

2015 Environmental Modelling & Software  
Kathleen Fowler, Clarkson University, USA is a computational Applied Mathematician specializing in simulation-based derivative-free optimization for engineering design problems, developing hybrid-algorithms  ...  Her recent focus has been on developing and evaluating Bayesian network models particularly in the field of ecohydrology.  ...  His primary research interests include multi-objective optimization to explore tradeoffs between water uses, and sensitivity analysis as a tool for decision support under uncertainty.  ... 
doi:10.1016/j.envsoft.2015.09.003 fatcat:ypqpu6ogyffivgfowax6rzb5aa

Hydro-Economic Modelling for Water-Policy Assessment Under Climate Change at a River Basin Scale: A Review

Alfonso Expósito, Felicitas Beier, Julio Berbel
2020 Water  
Additionally, it analyses how uncertainty and risk factors of global CC have been treated in recent HEMs, offering a discussion on these last advances.  ...  In the last decade, HEMs have achieved significant advances regarding the assessment of the impacts of water-policy instruments at a river basin or catchment level in the context of climate change (CC)  ...  Arguably, the recently more commonly applied hybrid approaches combining simulation and optimization network-based HEMs may be especially well suited to analyse water policies under CC at a river-basin  ... 
doi:10.3390/w12061559 fatcat:as4rrdxgi5fq3knjizev3rephq

Recent Trends of Computational Methods in Vibration Problems

Snehashish Chakraverty, Atma Sahu, Choong Kok Keong, Saleh M. Hassan
2012 Advances in Acoustics and Vibration  
In addition to the traditional prediction error method, a new knowledge-based artificial Fish-Swarm optimization algorithm (AFA) with crossover, CAFAC, is proposed to identify the parameters in the new  ...  Mathematical theory, numerical simulation, physical experiments with advanced computational investigations, engineering design, and their various engineering applications are included in the main program  ...  As such, the present issue has addressed recent trends of the computational methods that may be used in the said vibration problems.  ... 
doi:10.1155/2012/645981 fatcat:2ha3uzhabbggra77w6yufrejii

Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty

Ignacio E. Grossmann, Robert M. Apap, Bruno A. Calfa, Pablo García-Herreros, Qi Zhang
2016 Computers and Chemical Engineering  
Optimization under uncertainty has been an active area of research for many years.  ...  In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models.  ...  The authors would like to acknowledge financial support from NSF Grant No. 1159443, Praxair, The Dow Chemical Company, the ExxonMobil Upstream Research Company, and the Center for Advanced Process Decision-making  ... 
doi:10.1016/j.compchemeng.2016.03.002 fatcat:kbdi23aghbh25dnd455t7atcdm

Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach

Changhui Peng, Joel Guiot, Haibin Wu, Hong Jiang, Yiqi Luo
2011 Ecology Letters  
(i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction  ...  Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences.  ...  ACKNOWLEDGEMENTS This work was conducted in China and France during the sabbatical leave of C. Peng.  ... 
doi:10.1111/j.1461-0248.2011.01603.x pmid:21366814 fatcat:7sviglakufaozg67gdlezkbbse

Robust Capacity Control in Revenue Management: A Literature Review

Zheyu Jiang
2018 Open Journal of Business and Management  
In recent 10 years, robust optimization methodology motivated a rapid growing amount of literature on robust revenue management.  ...  structural properties of optimal solution.  ...  In the recent 10 years, one new optimization methodology which can make optimal solutions considering parameter uncertainty arises up.  ... 
doi:10.4236/ojbm.2018.62037 fatcat:bx4k4n2oj5egpcopofgb7yoidy

Recent Trends on Security Constrained Economic Dispatch: A Bibliographic Review

Shewit Tsegaye, Fekadu Shewarega
2019 Zenodo  
This is done to provide an up-to-date review of the recent major advancements in SCED, the state-of-the-art since 2008, identify further challenging developments needed in smarter grids, and indicate ways  ...  The period under consideration is 2008 through 2018.  ...  This is done to provide an up-to-date review of the recent major advancements in the SCED.  ... 
doi:10.5281/zenodo.3300384 fatcat:rkm5f3xpf5dublatnwbdfldnga

Preface of the special issue: the 6th International Conference on Optimization and Control with Applications (6th OCA)

Kok Lay Teo, Adil Bagirov, Wuyi Yue
2018 Optimization Letters  
The papers included recent theoretical and applied contributions in various fields of optimization as well as control theory.  ...  The 6th OCA provided an excellent forum for researchers and practitioners to promote their recent advances in optimization and control to the wider scientific community, to identify new research challenges  ...  Thanks to the Editors-in-Chief of Optimization Letters journal, P. Krokhmal and O.A. Prokopyev, for their interest and continuous support.  ... 
doi:10.1007/s11590-018-1297-z fatcat:xka6keqpazf6vjab6kyxmyvmea

Advances in glacial isostatic adjustment modeling

2019 Past Global Change Magazine  
recent developments towards improving uncertainty estimation in GIA model output which is central to these applications.  ...  We overview two PALSEA-relevant applications of glacial isostatic adjustment modeling and highlight recent advances.  ...  A recent advancement in this area is the development of coupled models that account for feedbacks between GIA-related processes and ice dynamics (see Whitehouse 2018) .  ... 
doi:10.22498/pages.27.1.16 fatcat:o7vtfv3n5rbfpazlmegcpqgbqa

UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem [article]

M. Stoitsov, H. Nam, W. Nazarewicz, A. Bulgac, G. Hagen, M. Kortelainen, J. C. Pei, K. J. Roche, N. Schunck, I. Thompson, J.P. Vary, S. M. Wild
2011 arXiv   pre-print
power with quantified uncertainties.  ...  The UNEDF SciDAC collaboration of nuclear theorists, applied mathematicians, and computer scientists is developing a comprehensive description of nuclei and their reactions that delivers maximum predictive  ...  on the latest advances in nuclear theory and scientific computing.  ... 
arXiv:1107.4925v1 fatcat:yusczxupd5fd3hpoa7xdwtbpwa

Challenges and opportunities at the intersection of model reduction and uncertainty quantification

David Barajas-Solano, Alexandre Tartakovsky, Panagiotis Stinis, Xiu Yang
Whitepaper submitted to the 2017 DOE ASCR Applied Math MeetingChallenges and opportunities at the intersection of model reduction and uncertainty quantificationDavid A.  ...  Recent advances in model reduction and uncertainty quantification allow us to exploit observations of the system's state to obtain low-dimensional representations of responses in random space, which can  ...  Optimization and design under uncertainty requires resolving the map from decision and random spaces to system response.  ... 
doi:10.6084/m9.figshare.5318104 fatcat:dloda2zb2fdcxnxj2ab4n7f6um

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Shaik Rehana, Chandra Rupa Rajulapati, Subimal Ghosh, Subhankar Karmakar, Pradeep Mujumdar
2020 Water  
Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area.  ...  In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models.  ...  Discharger Water Resource Management under Climate Change Induced Uncertainties Water resource systems management models have been advanced in recent years to consider climate change as a driving force  ... 
doi:10.3390/w12061793 fatcat:aaljunhjprfgtit4g2wjnufygi

Uncertainty-Aware Data Aggregation for Deep Imitation Learning [article]

Yuchen Cui, David Isele, Scott Niekum, Kikuo Fujimura
2019 arXiv   pre-print
UAIL applies Monte Carlo Dropout to estimate uncertainty in the control output of end-to-end systems, using states where it is uncertain to selectively acquire new training data.  ...  In contrast to prior data aggregation algorithms that force human experts to visit sub-optimal states at random, UAIL can anticipate its own mistakes and switch control to the expert in order to prevent  ...  RELATED WORK Our work builds on recent advances in predictive uncertainty estimation for deep networks and is closely related to the field of imitation learning. A.  ... 
arXiv:1905.02780v1 fatcat:vmmcu6jxujbq3f5doopej2gcr4
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