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ORCIDYingqian Zhang 0001
Person information
- affiliation: Eindhoven University of Technology, The Netherlands
- affiliation: Erasmus University Rotterdam, The Netherlands
- affiliation: Delft University of Technology, The Netherlands
- affiliation (PhD): University of Manchester, School of Computer Science, UK
Other persons with the same name
- Yingqian Zhang 0002
![[0000-0001-9568-0392]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-9568-0392")
(aka: Ying-Qian Zhang 0002) — Xiamen University Malaysia (XMUM), Sepang, Malaysia (and 2 more) - Yingqian Zhang 0003 — PLA General Hospital, Beijing, China
- Yingqian Zhang 0004 — Sichuan University of Science and Engineering, Zigong, China
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Journal Articles
- 2023
[j26]Yingqian Zhang, Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, Ekhine Irurozki:
The first AI4TSP competition: Learning to solve stochastic routing problems. Artif. Intell. 319: 103918 (2023)- [j25]Paulo da Costa, Peter Verleijsdonk, Simon Voorberg, Alp Akcay, Stella Kapodistria, Willem van Jaarsveld, Yingqian Zhang:
Policies for the dynamic traveling maintainer problem with alerts. Eur. J. Oper. Res. 305(3): 1141-1152 (2023) - [j24]Vahideh Reshadat, Alp Akcay, Kalliopi Zervanou, Yingqian Zhang, Eelco de Jong:
SCRE: special cargo relation extraction using representation learning. Neural Comput. Appl. 35(25): 18783-18801 (2023) - [j23]Reza Refaei Afshar, Jason Rhuggenaath, Yingqian Zhang, Uzay Kaymak:
An Automated Deep Reinforcement Learning Pipeline for Dynamic Pricing. IEEE Trans. Artif. Intell. 4(3): 428-437 (2023) - 2022
- [j22]Abdo Abouelrous, Adriana F. Gabor, Yingqian Zhang:
Optimizing the inventory and fulfillment of an omnichannel retailer: a stochastic approach with scenario clustering. Comput. Ind. Eng. 173: 108723 (2022) - [j21]Reza Refaei Afshar, Yingqian Zhang, Uzay Kaymak:
Dynamic Ad Network Ordering Method Using Reinforcement Learning. Int. J. Comput. Intell. Syst. 15(1): 27 (2022) - [j20]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Setting Reserve Prices in Second-Price Auctions with Unobserved Bids. INFORMS J. Comput. 34(6): 2950-2967 (2022) - 2021
- [j19]Yingqian Zhang, Michele Lombardi, Patrick De Causmaecker:
Preface. Ann. Math. Artif. Intell. 89(7): 615-616 (2021) - [j18]Qing Chuan Ye, Jason Rhuggenaath, Yingqian Zhang, Sicco Verwer, Michiel Jurgen Hilgeman:
Data driven design for online industrial auctions. Ann. Math. Artif. Intell. 89(7): 675-691 (2021) - [j17]Bram Cals, Yingqian Zhang, Remco M. Dijkman, Claudy van Dorst:
Solving the online batching problem using deep reinforcement learning. Comput. Ind. Eng. 156: 107221 (2021) - [j16]Jason Rhuggenaath, Reza Refaei Afshar, Alp Akcay, Yingqian Zhang, Uzay Kaymak, Fatih Çolak, Muratcan Tanyerli:
Maximizing revenue for publishers using header bidding and ad exchange auctions. Oper. Res. Lett. 49(2): 250-256 (2021) - [j15]Paulo da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Uzay Kaymak:
Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning. SN Comput. Sci. 2(5): 388 (2021) - 2020
- [j14]Murat Firat, Guillaume Crognier, Adriana F. Gabor, Cor A. J. Hurkens, Yingqian Zhang:
Column generation based heuristic for learning classification trees. Comput. Oper. Res. 116: 104866 (2020) - [j13]Juntao Gao, Yingqian Zhang:
Regular Expression Learning from Positive Examples Based on Integer Programming. Int. J. Softw. Eng. Knowl. Eng. 30(10): 1443-1479 (2020) - [j12]Paulo Roberto de Oliveira da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Remaining useful lifetime prediction via deep domain adaptation. Reliab. Eng. Syst. Saf. 195: 106682 (2020) - 2019
- [j11]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Optimal display-ad allocation with guaranteed contracts and supply side platforms. Comput. Ind. Eng. 137 (2019) - [j10]Rowan Hoogervorst, Yingqian Zhang, Gamze Tillem, Zekeriya Erkin, Sicco Verwer:
Solving bin-packing problems under privacy preservation: Possibilities and trade-offs. Inf. Sci. 500: 203-216 (2019) - 2017
- [j9]Sicco Verwer, Yingqian Zhang, Qing Chuan Ye:
Auction optimization using regression trees and linear models as integer programs. Artif. Intell. 244: 368-395 (2017) - [j8]Jun-Qiang Wang, Guo-qiang Fan, Yingqian Zhang, Cheng-Wu Zhang, Joseph Y.-T. Leung:
Two-agent scheduling on a single parallel-batching machine with equal processing time and non-identical job sizes. Eur. J. Oper. Res. 258(2): 478-490 (2017) - 2016
- [j7]Junqiang Wang, Jian Chen, Yingqian Zhang, George Q. Huang:
Schedule-based execution bottleneck identification in a job shop. Comput. Ind. Eng. 98: 308-322 (2016) - 2014
- [j6]Jelmer P. van der Gaast, Cornelius A. Rietveld, Adriana F. Gabor, Yingqian Zhang:
A Tabu Search Algorithm for application placement in computer clustering. Comput. Oper. Res. 50: 38-46 (2014) - [j5]Bart de Keijzer, Tomas B. Klos, Yingqian Zhang:
Finding Optimal Solutions for Voting Game Design Problems. J. Artif. Intell. Res. 50: 105-140 (2014) - 2012
- [j4]Mathijs de Weerdt, Yingqian Zhang, Tomas Klos:
Multiagent task allocation in social networks. Auton. Agents Multi Agent Syst. 25(1): 46-86 (2012) - 2010
- [j3]Adriaan ter Mors, Chetan Yadati, Cees Witteveen, Yingqian Zhang:
Coordination by design and the price of autonomy. Auton. Agents Multi Agent Syst. 20(3): 308-341 (2010) - 2009
- [j2]Yingqian Zhang, Efrat Manisterski, Sarit Kraus, V. S. Subrahmanian, David Peleg:
Computing the fault tolerance of multi-agent deployment. Artif. Intell. 173(3-4): 437-465 (2009) - 2003
- [j1]Jürgen Dix, Thomas Eiter, Michael Fink, Axel Polleres, Yingqian Zhang:
Monitoring Agents using Declarative Planning. Fundam. Informaticae 57(2-4): 345-370 (2003)
Conference and Workshop Papers
- 2024
- [c61]Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Allah Bukhsh:
Online Control of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning. ICAPS 2024: 475-483 - 2023
- [c60]Luca Begnardi, Hendrik Baier, Willem van Jaarsveld, Yingqian Zhang:
Deep Reinforcement Learning for Two-sided Online Bipartite Matching in Collaborative Order Picking. ACML 2023: 121-136 - [c59]Mohsen Abbaspour Onari, Isel Grau, Marco S. Nobile, Yingqian Zhang:
Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey. CIBCB 2023: 1-8 - [c58]Robbert Reijnen, Yingqian Zhang, Zaharah Allah Bukhsh, Mateusz Guzek:
Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems. GECCO Companion 2023: 315-318 - [c57]Britt Lukassen, Laura Genga, Yingqian Zhang:
Discovering Explicit Scale-Up Criteria in Crisis Response with Decision Mining. LION 2023: 459-474 - [c56]Ligia Maria Moreira Zorello, Kazem Eradatmand, Sebastian Troia, Achille Pattavina, Yingqian Zhang, Guido Maier:
Auction-based network slicing for 5G RAN. NetSoft 2023: 390-395 - 2022
- [c55]Martijn Beeks, Reza Refaei Afshar, Yingqian Zhang, Remco M. Dijkman, Claudy van Dorst, Stijn de Looijer:
Deep Reinforcement Learning for a Multi-Objective Online Order Batching Problem. ICAPS 2022: 435-443 - [c54]Mohsen Abbaspour Onari, Marco S. Nobile, Isel Grau, Caro Fuchs, Yingqian Zhang, Arjen-Kars Boer, Volkher Scharnhorst:
Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19. CIBCB 2022: 1-8 - [c53]David Kerkkamp, Zaharah Allah Bukhsh, Yingqian Zhang, Nils Jansen:
Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks. ICAART (2) 2022: 574-585 - [c52]Robbert Reijnen, Yingqian Zhang, Zaharah Allah Bukhsh, Mateusz Guzek:
Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization. SSCI 2022: 804-811 - 2021
- [c51]Reza Refaei Afshar, Jason Rhuggenaath, Yingqian Zhang, Uzay Kaymak:
A Reward Shaping Approach for Reserve Price Optimization using Deep Reinforcement Learning. IJCNN 2021: 1-8 - [c50]Paulo da Costa, Yingqian Zhang, Alp Akcay, Uzay Kaymak:
Learning 2-opt Local Search from Heuristics as Expert Demonstrations. IJCNN 2021: 1-8 - [c49]Wouter Kool, Laurens Bliek, Danilo Numeroso, Yingqian Zhang, Tom Catshoek, Kevin Tierney, Thibaut Vidal, Joaquim A. S. Gromicho:
The EURO Meets NeurIPS 2022 Vehicle Routing Competition. NeurIPS (Competition and Demos) 2021: 35-49 - [c48]Nikki Theeuwes, Geert-Jan van Houtum, Yingqian Zhang:
Improving Ambulance Dispatching with Machine Learning and Simulation. ECML/PKDD (4) 2021: 302-318 - [c47]Vahideh Reshadat, Alp Akcay, Kalliopi Zervanou, Yingqian Zhang, Eelco de Jong:
Relation Representation Learning for Special Cargo Ontology. SSCI 2021: 1-8 - 2020
- [c46]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning. ACML 2020: 81-96 - [c45]Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay:
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning. ACML 2020: 465-480 - [c44]Herbert van Leeuwen, Yingqian Zhang, Kalliopi Zervanou, Shantanu Mullick, Uzay Kaymak, Tom de Ruijter:
Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled. ICAART (2) 2020: 172-181 - [c43]Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Yingqian Zhang, Alp Akcay, Uzay Kaymak:
Dynamic Pricing Using Thompson Sampling with Fuzzy Events. IPMU (1) 2020: 653-666 - [c42]Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Yingqian Zhang, Alp Akcay, Uzay Kaymak:
Low-Regret Algorithms for Strategic Buyers with Unknown Valuations in Repeated Posted-Price Auctions. ECML/PKDD (2) 2020: 416-436 - [c41]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak, Ali Izzet Metin, Gönenç Seçil Tarakçioglu, Cosku Bas:
Reserve price optimization with header bidding and Ad Exchange. SMC 2020: 830-835 - [c40]Jeroen Verheugd, Paulo Roberto de Oliveira da Costa, Reza Refaei Afshar, Yingqian Zhang, Sjoerd Boersma:
Predicting Water Pipe Failures with a Recurrent Neural Hawkes Process Model. SMC 2020: 2628-2633 - [c39]Robbert Reijnen, Yingqian Zhang, Wim Nuijten, Çaglar Senaras, Mariana Goldak-Altgassen:
Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts. SSCI 2020: 2647-2654 - 2019
- [c38]Sicco Verwer, Yingqian Zhang:
Learning Optimal Classification Trees Using a Binary Linear Program Formulation. AAAI 2019: 1625-1632 - [c37]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
A Reinforcement Learning Method to Select Ad Networks in Waterfall Strategy. BNAIC/BENELEARN 2019 - [c36]Paulo Roberto de Oliveira da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Wan-Jui Lee, Uzay Kaymak:
Data-Driven Policy on Feasibility Determination for the Train Shunting Problem. BNAIC/BENELEARN 2019 - [c35]Jasper Paalman, Shantanu Mullick, Kalliopi Zervanou, Yingqian Zhang:
Term Based Semantic Clusters for Very Short Text Classification. BNAIC/BENELEARN 2019 - [c34]Nikki Theeuwes, Geert-Jan van Houtum, Yingqian Zhang, Björn Gadet:
Formalization and Improvement of Ambulance Dispatching in Brabant-Zuidoost. BNAIC/BENELEARN 2019 - [c33]Sicco Verwer, Yingqian Zhang:
Learning Optimal Classification Trees Using a Binary Linear Program Formulation. BNAIC/BENELEARN 2019 - [c32]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
A PSO-based Algorithm for Reserve Price Optimization in Online Ad Auctions. CEC 2019: 2611-2619 - [c31]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
A Decision Support Method to Increase the Revenue of Ad Publishers in Waterfall Strategy. CIFEr 2019: 1-8 - [c30]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Optimizing reserve prices for publishers in online ad auctions. CIFEr 2019: 1-8 - [c29]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Fuzzy Logic based Pricing combined with Adaptive Search for Reserve Price Optimization in Online Ad Auctions. FUZZ-IEEE 2019: 1-8 - [c28]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
Reinforcement Learning Method for Ad Networks Ordering in Real-Time Bidding. ICAART (Revised Selected Papers) 2019: 16-36 - [c27]Arno van de Ven, Yingqian Zhang, Wan-Jui Lee:
Boosting Local Search Using Machine Learning: A Study on Improving Local Search by Graph Classification in Determining Capacity of Shunting Yards. ICAART (Revised Selected Papers) 2019: 183-203 - [c26]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
A Reinforcement Learning Method to Select Ad Networks in Waterfall Strategy. ICAART (2) 2019: 256-265 - [c25]Arno van de Ven, Yingqian Zhang, Wan-Jui Lee, Rik Eshuis, Anna Wilbik:
Determining Capacity of Shunting Yards by Combining Graph Classification with Local Search. ICAART (2) 2019: 285-293 - [c24]Paulo Roberto de Oliveira da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Wan-Jui Lee, Uzay Kaymak:
Data-Driven Policy on Feasibility Determination for the Train Shunting Problem. ECML/PKDD (3) 2019: 719-734 - [c23]Jasper Paalman, Shantanu Mullick, Kalliopi Zervanou, Yingqian Zhang:
Term Based Semantic Clusters for Very Short Text Classification. RANLP 2019: 878-887 - [c22]Dylan Rijnen, Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Yingqian Zhang:
Machine Learning based Simulation Optimisation for Trailer Management. SMC 2019: 3687-3692 - [c21]Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
A heuristic policy for dynamic pricing and demand learning with limited price changes and censored demand. SMC 2019: 3693-3698 - 2018
- [c20]Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Uzay Kaymak, Sicco Verwer:
Learning fuzzy decision trees using integer programming. FUZZ-IEEE 2018: 1-8 - [c19]Evertjan Peer, Vlado Menkovski, Yingqian Zhang, Wan-Jui Lee:
Shunting Trains with Deep Reinforcement Learning. SMC 2018: 3063-3068 - 2017
- [c18]Sicco Verwer, Yingqian Zhang:
Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization. CPAIOR 2017: 94-103 - [c17]Qing Chuan Ye, Yingqian Zhang, Uzay Kaymak:
Modeling participation behavior in repeated task allocations with fuzzy connectives. SMC 2017: 3219-3234 - 2016
- [c16]Qing Chuan Ye, Yingqian Zhang:
Participation Behavior and Social Welfare in Repeated Task Allocations. ICA 2016: 94-97 - 2012
- [c15]Sicco Verwer, Yingqian Zhang:
Revenue prediction in budget-constrained sequential auctions with complementarities. AAMAS 2012: 1399-1400 - [c14]Yingqian Zhang, Sicco Verwer:
Mechanism for Robust Procurements. PRIMA 2012: 77-91 - 2010
- [c13]Bart de Keijzer, Tomas Klos, Yingqian Zhang:
Enumeration and exact design of weighted voting games. AAMAS 2010: 391-398 - [c12]Chetan Yadati, Cees Witteveen, Yingqian Zhang:
Coordinating Agents - An Analysis of Coordination in Supply-chain Management Tasks. ICAART (2) 2010: 218-223 - 2009
- [c11]Bart de Keijzer, Sylvain Bouveret, Tomas Klos, Yingqian Zhang:
On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences. ADT 2009: 98-110 - [c10]Yingqian Zhang, Mathijs de Weerdt:
Creating Incentives to Prevent Intentional Execution Failures. IAT 2009: 431-434 - 2008
- [c9]Mathijs de Weerdt, Yingqian Zhang:
Preventing Under-Reporting in Social Task Allocation. AMEC/TADA 2008: 1-14 - [c8]Roman van der Krogt, Mathijs de Weerdt, Yingqian Zhang:
Of Mechanism Design Multiagent Planning. ECAI 2008: 423-427 - [c7]Chetan Yadati, Cees Witteveen, Yingqian Zhang, Mengxiao Wu, Han La Poutré:
Autonomous Scheduling. FCS 2008: 73-81 - [c6]Chetan Yadati, Cees Witteveen, Yingqian Zhang, Mengxiao Wu, Han La Poutré:
Autonomous Scheduling with Unbounded and Bounded Agents. MATES 2008: 195-206 - 2007
- [c5]Mathijs de Weerdt, Yingqian Zhang, Tomas Klos:
Distributed task allocation in social networks. AAMAS 2007: 76 - [c4]J. Renze Steenhuisen, Cees Witteveen, Yingqian Zhang:
Plan-Coordination Mechanisms and the Price of Autonomy. CLIMA 2007: 1-21 - 2005
- [c3]Yingqian Zhang:
Towards fault tolerance for multiagent systems. AAMAS 2005: 1387 - 2004
- [c2]V. S. Subrahmanian, Sarit Kraus, Yingqian Zhang:
Distributed Algorithms for Dynamic Survivability of Multiagent Systems. CLIMA 2004: 1-15 - 2003
- [c1]Jürgen Dix, Thomas Eiter, Michael Fink, Axel Polleres, Yingqian Zhang:
Monitoring Agents Using Declarative Planning. KI 2003: 646-660
Parts in Books or Collections
- 2022
- [p2]Vahideh Reshadat, Tess Kolkman, Kalliopi Zervanou, Yingqian Zhang, Alp Akçay, Carlijn Snijder, Ryan McDonnell, Karel Schorer, Casper Wichers, Thomas Koch, Elenna R. Dugundji, Eelco de Jong:
Knowledge Modeling and Incident Analysis for Special Cargo. Technologies and Applications for Big Data Value 2022: 519-544 - 2005
- [p1]Jürgen Dix, Yingqian Zhang:
IMPACT: A Multi-Agent Framework with Declarative Semantics. Multi-Agent Programming 2005: 69-94
Informal and Other Publications
- 2024
- [i26]Igor G. Smit, Zaharah Allah Bukhsh, Mykola Pechenizkiy, Kostas Alogariastos, Kasper Hendriks, Yingqian Zhang:
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking. CoRR abs/2404.08006 (2024) - [i25]Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu:
Cross-Problem Learning for Solving Vehicle Routing Problems. CoRR abs/2404.11677 (2024) - [i24]Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers, Charalampos Andriotis, Yingqian Zhang, Zaharah Bukhsh:
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization. CoRR abs/2406.06184 (2024) - [i23]Igor G. Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu, Jian Chen, Cong Zhang, Zaharah Bukhsh, Wim Nuijten, Yingqian Zhang:
Graph Neural Networks for Job Shop Scheduling Problems: A Survey. CoRR abs/2406.14096 (2024) - 2023
- [i22]Abdo Abouelrous, Laurens Bliek, Yingqian Zhang:
Digital Twin Applications in Urban Logistics: An Overview. CoRR abs/2302.00484 (2023) - [i21]Ya Song, Laurens Bliek, Yingqian Zhang:
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks. CoRR abs/2302.04035 (2023) - [i20]Mohsen Abbaspour Onari, Isel Grau, Marco S. Nobile, Yingqian Zhang:
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model. CoRR abs/2307.11765 (2023) - [i19]Robbert Reijnen, Kjell van Straaten, Zaharah Allah Bukhsh, Yingqian Zhang:
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods. CoRR abs/2308.12794 (2023) - [i18]Jan de Leeuw, Zaharah Allah Bukhsh, Yingqian Zhang:
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI. CoRR abs/2310.16602 (2023) - 2022
- [i17]Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak:
Automated Reinforcement Learning: An Overview. CoRR abs/2201.05000 (2022) - [i16]Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, Ekhine Irurozki:
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems. CoRR abs/2201.10453 (2022) - [i15]Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Allah Bukhsh:
Operator Selection in Adaptive Large Neighborhood Search using Deep Reinforcement Learning. CoRR abs/2211.00759 (2022) - [i14]Remco Coppens, Robbert Reijnen, Yingqian Zhang, Laurens Bliek, Berend Steenhuisen:
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems. CoRR abs/2211.09719 (2022) - 2021
- [i13]Paulo da Costa, Peter Verleijsdonk, Simon Voorberg, Alp Akcay, Stella Kapodistria, Willem van Jaarsveld, Yingqian Zhang:
Policies for the Dynamic Traveling Maintainer Problem with Alerts. CoRR abs/2105.15119 (2021) - 2020
- [i12]Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay:
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning. CoRR abs/2004.01608 (2020) - [i11]Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Algorithms for slate bandits with non-separable reward functions. CoRR abs/2004.09957 (2020) - [i10]Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak:
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning. CoRR abs/2004.12117 (2020) - [i9]Bram Cals, Yingqian Zhang, Remco M. Dijkman, Claudy van Dorst:
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning. CoRR abs/2006.09507 (2020) - 2019
- [i8]Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Wan-Jui Lee, Uzay Kaymak:
Data-driven Policy on Feasibility Determination for the Train Shunting Problem. CoRR abs/1907.04711 (2019) - [i7]Paulo R. de O. da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak:
Remaining Useful Lifetime Prediction via Deep Domain Adaptation. CoRR abs/1907.07480 (2019) - [i6]Dylan Rijnen, Jason Rhuggenaath, Paulo R. de O. da Costa, Yingqian Zhang:
Machine Learning based Simulation Optimisation for Trailer Management. CoRR abs/1907.07568 (2019) - 2018
- [i5]Murat Firat, Guillaume Crognier, Adriana F. Gabor, Yingqian Zhang, Cor A. J. Hurkens:
Constructing classification trees using column generation. CoRR abs/1810.06684 (2018) - 2015
- [i4]Qing Chuan Ye, Yingqian Zhang, Rommert Dekker:
Fair task allocation in transportation. CoRR abs/1505.07434 (2015) - 2014
- [i3]Sicco Verwer, Yingqian Zhang, Qing Chuan Ye:
Learning optimization models in the presence of unknown relations. CoRR abs/1401.1061 (2014) - 2012
- [i2]Bart de Keijzer, Tomas B. Klos, Yingqian Zhang:
Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration. CoRR abs/1204.5213 (2012) - 2008
- [i1]Yingqian Zhang, Mathijs de Weerdt:
Creating incentives to prevent execution failures: an extension of VCG mechanism. Planning in Multiagent Systems 2008
Coauthor Index
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Citation data
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load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2024-08-05 20:18 CEST by the dblp team
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