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Akademický článek

Culturally adapted psychoeducation for bipolar disorder in a low-resource setting: protocol for a multicentre randomized controlled trial

Publikováno v: European Psychiatry, Vol 66, Pp S384-S384 (2023)

Introduction Bipolar disorder (BD) is a source of marked disability, morbidity, and premature death. There is a paucity of research on personalized psychosocial interventions for BD, especially in lowresource settings. A previously published pilot ra

Externí odkaz: https://doaj.org/article/36e90d9f46674ec6a3b945dbf23d87bc

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Akademický článek

Measurement-based care vs. standard care for major depressive disorder in Pakistan: protocol for a randomized control trial

Publikováno v: European Psychiatry, Vol 66, Pp S836-S836 (2023)

Introduction Low and middle-income countries (LMICs) hold the majority of disease burden attributed to major depressive disorder (MDD). Despite this, there remains a substantial gap for access to evidence-based treatments for MDD in LMICs like Pakist

Externí odkaz: https://doaj.org/article/b10168cf944f4910968e08e7f4094ed6

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Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling

Autor: van Remmerden, Jesse, Bukhsh, Zaharah, Zhang, Yingqian

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

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Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning

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

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Graph Neural Networks for Job Shop Scheduling Problems: A Survey

Autor: Smit, Igor G., Zhou, Jianan, Reijnen, Robbert, Wu, Yaoxin, Chen, Jian, Zhang, Cong, Bukhsh, Zaharah, Nuijten, Wim, Zhang, Yingqian

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

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Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization

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

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Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking

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

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Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI

Autor: de Leeuw, Jan, Bukhsh, Zaharah, Zhang, Yingqian

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

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Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods

Autor: Reijnen, Robbert, van Straaten, Kjell, Bukhsh, Zaharah, Zhang, Yingqian

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

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Unsupervised anomaly detection algorithms on real-world data: how many do we need?

Autor: Bouman, Roel, Bukhsh, Zaharah, Heskes, Tom

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

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