EURO Award for the Best EJOR Paper
Sponsored by 
In 2012 EURO introduced 3 new annual awards for papers published by EJOR: best survey paper; best application paper; and best theory/methodology. Winners for each category will be selected by a jury and an award of €1,000 will be presented at the following EURO-k or IFORS conference. EURO will pay the registration fee for one representative of each paper.
Purpose
The purpose of the prize is to distinguish the best papers published in EJOR. General criteria for the evaluation of the papers are:
- the scientific quality of the paper;
- the originality of the paper;
- the relevance to Operational Research.
Detailed information is given in the guidelines.
The winners of the 2022 edition of the EABEP are:
Innovative Applications of OR: Feng Mai, Shaonan Tian, Chihoon Lee and Ling Ma. Deep learning models for bankruptcy prediction using textual disclosures.
Website Access: https://www.sciencedirect.com/science/article/abs/pii/S0377221718308774
Review: Nils Boysen, René de Koster, and Felix Weidinger. Warehousing in the e-commerce era: A survey.
Website Access: https://www.sciencedirect.com/science/article/abs/pii/S0377221718307185
Theory and Methodology: Truong Van Nguyen, Li Zhou, Alain Yee Loong Chong, Boying Li and Xiaodie Pu. Predicting customer demand for remanufactured products: A data-mining approach.
Website Access: https://www.sciencedirect.com/science/article/abs/pii/S037722171930668X
The EABEP 2022 jury consisted of Veronica Piccialli (chair), Judit Lienert, Lars-Magnus Hvattum, Mike Yearworth [EJOR rep] and Mustafa Çelebi Pınar.
The awards were announced at the closing session of the EURO 2022 conference in Espoo (Finland)
Jury of the EURO Award for the Best EJOR Paper 2023
Sponsored by
- Judit Lienert (Switzerland) - chair
- Lars-Magnus Hvattum (Norway)
- Bernardetta Addis (France)
- Roman Słowiński (Poland) [EJOR rep]
- Ceyda Oğuz (Turkey)
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 International License and the GNU Free Documentation License (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
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