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Wouter van Heeswijk
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2020 – today
- 2024
- [j4]Berry Gerrits, Wouter van Heeswijk, Martijn Mes:
Towards self-organizing logistics in transportation: a literature review and typology. Int. Trans. Oper. Res. 31(3): 1309-1374 (2024) - [c11]Fabian Akkerman, Julius Luy, Wouter van Heeswijk, Maximilian Schiffer:
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces. ICLR 2024 - 2023
- [c10]Robert van Steenbergen, Eduardo Lalla-Ruiz, Wouter van Heeswijk, Martijn Mes:
The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. ICCL 2023: 276-291 - [i8]Fabian Akkerman, Julius Luy, Wouter van Heeswijk, Maximilian Schiffer:
Handling Large Discrete Action Spaces via Dynamic Neighborhood Construction. CoRR abs/2305.19891 (2023) - [i7]Robert van Steenbergen, Wouter van Heeswijk, Martijn Mes:
The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs. CoRR abs/2312.00140 (2023) - 2022
- [c9]Stefani Slavova, Jean Paul Sebastian Piest, Wouter van Heeswijk:
Predicting truck parking occupancy using machine learning. ANT/EDI40 2022: 40-47 - [c8]Jean Paul Sebastian Piest, Stefani Slavova, Wouter van Heeswijk:
A Reference Use Case, Data Space Architecture, and Prototype for Smart Truck Parking. Forum/CIAO!DC@EEWC 2022 - [i6]Wouter J. A. van Heeswijk:
Natural Policy Gradients In Reinforcement Learning Explained. CoRR abs/2209.01820 (2022) - 2021
- [i5]Wouter van Heeswijk:
Strategic bidding in freight transport using deep reinforcement learning. CoRR abs/2102.09253 (2021) - 2020
- [j3]Abhishta Abhishta, Wouter van Heeswijk, Marianne Junger, Lambert J. M. Nieuwenhuis, Reinoud Joosten:
Why would we get attacked? An analysis of attacker's aims behind DDoS attacks. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 11(2): 3-22 (2020) - [j2]Wouter van Heeswijk, Martijn R. K. Mes, J. M. J. Schutten, W. H. M. Zijm:
Evaluating Urban Logistics Schemes Using Agent-based Simulation. Transp. Sci. 54(3): 651-675 (2020) - [c7]Wouter van Heeswijk:
Smart Containers with Bidding Capacity: A Policy Gradient Algorithm for Semi-cooperative Learning. ICCL 2020: 52-67 - [c6]Martijn Mes, Wouter van Heeswijk:
Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. ICCL 2020: 698-714 - [c5]Wouter van Heeswijk, Han La Poutré:
Deep Reinforcement Learning in Linear Discrete Action Spaces. WSC 2020: 1063-1074 - [i4]Wouter J. A. van Heeswijk:
Donald Duck Holiday Game: A numerical analysis of a Game of the Goose role-playing variant. CoRR abs/2001.04513 (2020) - [i3]Wouter van Heeswijk:
Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for Semi-Cooperative Learning. CoRR abs/2005.00565 (2020) - [i2]Wouter J. A. van Heeswijk:
A Gentle Lecture Note on Filtrations in Reinforcement Learning. CoRR abs/2008.02622 (2020)
2010 – 2019
- 2019
- [j1]Wouter J. A. van Heeswijk, Martijn R. K. Mes, J. M. J. Schutten:
The Delivery Dispatching Problem with Time Windows for Urban Consolidation Centers. Transp. Sci. 53(1): 203-221 (2019) - [i1]Wouter van Heeswijk, Han La Poutré:
Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces. CoRR abs/1902.09855 (2019) - 2018
- [c4]Wouter van Heeswijk, Han La Poutré:
Scalability and Performance of Decentralized Planning in Flexible Transport Networks. SMC 2018: 292-297 - 2016
- [c3]Wouter van Heeswijk, Martijn Mes, Marco Schutten:
An Agent-Based Simulation Framework to Evaluate Urban Logistics Schemes. ICCL 2016: 369-383 - [c2]Wouter van Heeswijk, George H. L. Fletcher, Mykola Pechenizkiy:
On structure preserving sampling and approximate partitioning of graphs. SAC 2016: 875-882 - 2015
- [c1]Wouter van Heeswijk, Martijn Mes, Marco Schutten:
An Approximate Dynamic Programming Approach to Urban Freight Distribution with Batch Arrivals. ICCL 2015: 61-75
Coauthor Index
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last updated on 2024-10-07 22:17 CEST by the dblp team
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