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‎_bibliography/s20250616.bib‎

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@inproceedings{ijcai2023p220,
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title = {A New Variable Ordering for In-processing Bounded Variable Elimination in SAT Solvers},
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author = {Li, Shuolin and Li, Chu-Min and Luo, Mao and Coll, Jordi and Habet, Djamal and Manyà, Felip},
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booktitle = {Proceedings of the Thirty-Second International Joint Conference on
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Artificial Intelligence, {IJCAI-23}},
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publisher = {International Joint Conferences on Artificial Intelligence Organization},
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editor = {Edith Elkind},
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pages = {1979--1987},
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year = {2023},
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month = {8},
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note = {Main Track},
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doi = {10.24963/ijcai.2023/220},
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url = {https://doi.org/10.24963/ijcai.2023/220},
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html = {https://doi.org/10.24963/ijcai.2023/220},
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}

‎_bibliography/s20250623.bib‎

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@article{Bertsimas2020,
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title = {From Predictive to Prescriptive Analytics},
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volume = {66},
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ISSN = {1526-5501},
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url = {http://dx.doi.org/10.1287/mnsc.2018.3253},
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html = {http://dx.doi.org/10.1287/mnsc.2018.3253},
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DOI = {10.1287/mnsc.2018.3253},
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number = {3},
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journal = {Management Science},
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publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},
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author = {Bertsimas, Dimitris and Kallus, Nathan},
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year = {2020},
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month = mar,
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pages = {1025–1044}
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}
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@ARTICLE{9744407,
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author={Guo, Xiaotong and Wang, Qingyi and Zhao, Jinhua},
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journal={IEEE Open Journal of Intelligent Transportation Systems},
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title={Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System},
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year={2022},
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volume={3},
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number={},
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pages={251-266},
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keywords={Predictive models;Optimization;Uncertainty;Adaptation models;Decision making;Prediction algorithms;Data models;Data-driven optimization;predictive prescriptions;ride-hailing system;vehicle Rebalancing},
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doi={10.1109/OJITS.2022.3163180}}

‎_bibliography/s20250630.bib‎

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@article{Beck2007,
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title = {Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations},
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volume = {28},
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ISSN = {1076-9757},
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url = {http://dx.doi.org/10.1613/jair.2080},
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html = {http://dx.doi.org/10.1613/jair.2080},
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DOI = {10.1613/jair.2080},
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journal = {Journal of Artificial Intelligence Research},
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publisher = {AI Access Foundation},
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author = {Beck, J. C. and Wilson, N.},
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year = {2007},
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month = mar,
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pages = {183–232}
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}

‎_sessions/2025-06-16-variable-ordering.md‎

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---
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layout: session
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title: "A New Variable Ordering for In-processing Bounded Variable Elimination in SAT Solvers"
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host: Maarten Flippo
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host_ref: https://maartenflippo.com/
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session_type: paper
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related_papers: s20250616
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---
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Bounded Variable Elimination (BVE) is an important Boolean formula simplification technique in which the variable ordering is crucial. We define a new variable ordering based on variable activity, called ESA (variable Elimination Scheduled by Activity), for in-processing BVE in ConflictDriven Clause Learning (CDCL) SAT solvers, and incorporate it into several state-of-the-art CDCL SAT solvers. Experimental results show that the new ESA ordering consistently makes these solvers solve more instances on the benchmark set including all the 5675 instances used in the Crafted, Application and Main tracks of all SAT Competitions up to 2022. In particular, one of these solvers with ESA, Kissat MAB ESA, won the Anniversary track of the SAT Competition 2022. The behaviour of ESA and the reason of its effectiveness are also analyzed.

‎_sessions/2025-06-23-predictive-prescription.md‎

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---
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layout: session
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title: "Predictive Prescription with a Transportation Application"
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host: Elif Arslan
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session_type: paper
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related_papers: s20250623
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---
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In this session, we will explore how Machine Learning and Operations Research can be combined through Predictive Prescription, and examine an example of its application in Transportation.

‎_sessions/2025-06-30-probabilistic-scheduling.md‎

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---
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layout: session
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title: "Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations"
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host: Konstantin Sidorov
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host_ref: https://ksidorov.nl/
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session_type: paper
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related_papers: s20250630
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---
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We looked at a variety of papers on scheduling in this seminar series; however, the common assumption underpinning them is that the problem data is deterministic, for example, that the task durations do not fluctuate during the plan execution. In this session, I want to review a paper that lifts this assumption and design a branch-and-bound algorithm that uses Monte Carlo estimates in place of “deterministic” pruning.

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