Loading [MathJax]/extensions/MathMenu.js
Kim Van Den Houten - IEEE Xplore Author Profile
Access provided by:

MIT Libraries

Sign Out
Access provided by:

MIT Libraries

Sign Out
ADVANCED SEARCH

Author details

< Back

Kim Van Den Houten

Also published under: Kim van den Houten

Publications
2
Citations
1
Publications by Year
20222023
Co-Authors:
Show All Co-Authors (7)
Headshot silhouette indicating that no photo is available for this author

Kim Van Den Houten

Also published under: Kim van den Houten

Affiliation

Delft University of Technology, Delft, Netherlands

Publication Topics

Biography

KIM VAN DEN HOUTEN is an PhD candidate at Delft University of Technology. She holds a Masters Degree in Operations Research. Her research interests are on the intersection of optimization, machine learning, and simulation.(Based on document published on 31 January 2024).
Publications
2
Citations
1
Publications by Year
20222023
Co-Authors:
Show All Co-Authors (7)
Author's Published Works

Showing 1-2 of 2 results

  • Sort

Filter Results

Show

Sort By

Results

Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem

Kim Van Den Houten;Mathijs de Weerdt;David M. J. Tax;Esteban Freydell;Eva Christoupoulou;Alessandro Nati

2023 Winter Simulation Conference (WSC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
We study a highly complex scheduling problem that requires the generation and optimization of production schedules for a multi-product biomanufacturing system with continuous and batch processes. There are two main objectives here; makespan and lateness, which are combined into a cost function that is a weighted sum. An additional complexity comes from long horizons considered (up to a full year),...Show More

Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem

Kim Van Den Houten;Mathijs de Weerdt;David M. J. Tax;Esteban Freydell;Eva Christoupoulou;Alessandro Nati

Year: 2023 | Conference Paper |

Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework

Kim van den Houten;Emile van Krieken;Bernd Heidergott

2022 Winter Simulation Conference (WSC)
Year: 2022 | Conference Paper |
Policy gradient methods are successful for a wide range of reinforcement learning tasks. Traditionally, such methods utilize the score function as stochastic gradient estimator. We investigate the effect of replacing the score function with a measure-valued derivative within an on-policy actor-critic algorithm. The hypothesis is that measure-valued derivatives reduce the need for score function va...Show More

Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework

Kim van den Houten;Emile van Krieken;Bernd Heidergott

Year: 2022 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.