Zobrazeno 1 - 10
of 343
pro vyhledávání: '"DIJKMAN, REMCO"'
Autor:
Temizöz, Tarkan, Imdahl, Christina, Dijkman, Remco, Lamghari-Idrissi, Douniel, van Jaarsveld, Willem
Deploying deep reinforcement learning (DRL) in real-world inventory management presents challenges, including dynamic environments and uncertain problem parameters, e.g. demand and lead time distributions. These challenges highlight a research gap, s
Externí odkaz:
http://arxiv.org/abs/2411.00515
Autor:
Vazifehdoostirani, Mozhgan, Genga, Laura, Lu, Xixi, Verhoeven, Rob, van Laarhoven, Hanneke, Dijkman, Remco
Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi
Externí odkaz:
http://arxiv.org/abs/2308.14475
Dynamic task assignment involves assigning arriving tasks to a limited number of resources in order to minimize the overall cost of the assignments. To achieve optimal task assignment, it is necessary to model the assignment problem first. While ther
Externí odkaz:
http://arxiv.org/abs/2306.02910
Publikováno v:
In Transportation Research Part E November 2024 191
Autor:
Middelhuis, Jeroen, Bianco, Riccardo Lo, Sherzer, Eliran, Bukhsh, Zaharah, Adan, Ivo, Dijkman, Remco
Publikováno v:
In Information Systems February 2025 128
In this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination. While traditional plann
Externí odkaz:
http://arxiv.org/abs/2105.08374
Predictive business process monitoring focuses on predicting future characteristics of a running process using event logs. The foresight into process execution promises great potentials for efficient operations, better resource management, and effect
Externí odkaz:
http://arxiv.org/abs/2104.00721
Autor:
Temizöz, Tarkan, Imdahl, Christina, Dijkman, Remco, Lamghari-Idrissi, Douniel, van Jaarsveld, Willem
Problem Definition: Are traditional deep reinforcement learning (DRL) algorithms, developed for a broad range of purposes including game-play and robotics, the most suitable machine learning algorithms for applications in inventory control? To what e
Externí odkaz:
http://arxiv.org/abs/2011.15122
Publikováno v:
Computers & Industrial Engineering 156 (2021): 107221
In e-commerce markets, on time delivery is of great importance to customer satisfaction. In this paper, we present a Deep Reinforcement Learning (DRL) approach for deciding how and when orders should be batched and picked in a warehouse to minimize t
Externí odkaz:
http://arxiv.org/abs/2006.09507