Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Bukhsh, Zaharah A."'
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. There has been growing interest in using online Reinforcement Learning (RL) for JSSP. While online RL can quickly find acceptable solutions, especially for larger
Externí odkaz:
http://arxiv.org/abs/2409.10589
Autor:
Jimenez-Roa, Lisandro A., Simão, Thiago D., Bukhsh, Zaharah, Tinga, Tiedo, Molegraaf, Hajo, Jansen, Nils, Stoelinga, Marielle
Publikováno v:
Proceedings of the 8th European Conference of The Prognostics and Health Management Society 2024
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various complexities. Our study addresses two challenges within the Prognosti
Externí odkaz:
http://arxiv.org/abs/2407.12894
Autor:
Smit, Igor G., Zhou, Jianan, Reijnen, Robbert, Wu, Yaoxin, Chen, Jian, Zhang, Cong, Bukhsh, Zaharah, Zhang, Yingqian, Nuijten, Wim
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a s
Externí odkaz:
http://arxiv.org/abs/2406.14096
Autor:
van Remmerden, Jesse, Kenter, Maurice, Roijers, Diederik M., Andriotis, Charalampos, Zhang, Yingqian, Bukhsh, Zaharah
In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural maintenance optimization, an area traditionally dominated by single-objectiv
Externí odkaz:
http://arxiv.org/abs/2406.06184
Autor:
Smit, Igor G., Bukhsh, Zaharah, Pechenizkiy, Mykola, Alogariastos, Kostas, Hendriks, Kasper, Zhang, Yingqian
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimizati
Externí odkaz:
http://arxiv.org/abs/2404.08006
Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase. The ever-increasing availability of data, including product, customer, and order information, has made it possible for
Externí odkaz:
http://arxiv.org/abs/2310.16602
We introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembl
Externí odkaz:
http://arxiv.org/abs/2308.12794
Publikováno v:
Journal of Machine Learning Research 25.105 (2024): 1-34
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of datasets, the $k$-thN
Externí odkaz:
http://arxiv.org/abs/2305.00735
Publikováno v:
ICAPS 2024
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS relies on the proper configuration of its selection and acceptance para
Externí odkaz:
http://arxiv.org/abs/2211.00759
Autor:
Bukhsh, Zaharah, Saeed, Aaqib
Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid
Externí odkaz:
http://arxiv.org/abs/2210.15283