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ORCIDAndrey Kharitonov
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2020 – today
- 2024
[c11]Maria Chernigovskaya, Andrey Kharitonov, Abdulrahman Nahhas, Klaus Turowski:
Reinforcement Learning for Hyper-Parameter Optimization in the context of Capacity Management of SAP Enterprise Applications. CoDIT 2024: 294-299- [c10]Abdulrahman Nahhas, Andrey Kharitonov, Christian Haertel, Klaus Turowski:
Imitation Learning Based on Deep Reinforcement Learning for Solving Scheduling Problems. HICSS 2024: 1649-1658 - [c9]Andrey Kharitonov, Amro Abdalla, Abdulrahman Nahhas, Daniel Gunnar Staegemann, Christian Haertel, Christian Daase, Klaus Turowski:
A Literature Survey on Pitfalls of Open-Source Dependency Management in Enterprise. ICSOFT 2024: 15-22 - 2023
- [c8]Andrey Kharitonov, Abdulrahman Nahhas, Hendrik Müller, Klaus Turowski:
Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud. CLOSER 2023: 139-146 - [c7]Maria Chernigovskaya, Andrey Kharitonov, Klaus Turowski:
A Recent Publications Survey on Reinforcement Learning for Selecting Parameters of Meta-Heuristic and Machine Learning Algorithms. CLOSER 2023: 236-243 - [c6]Abhijith Remesh, Abdulrahman Nahhas, Andrey Kharitonov, Klaus Turowski:
A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods. HICSS 2023: 1580-1589 - [c5]Andrey Kharitonov, Roheet Rajendran, Hendrik Müller, Klaus Turowski:
Utility of Univariate Forecasting for Workload Metrics Predictions in Enterprise Applications. KMIS 2023: 231-240 - 2022
- [j1]Hendrik Müller
, Andrey Kharitonov, Abdulrahman Nahhas, Sascha Bosse, Klaus Turowski:
Addressing IT Capacity Management Concerns Using Machine Learning Techniques. SN Comput. Sci. 3(1): 26 (2022) - [c4]Abhijith Remesh, Abdulrahman Nahhas, Andrey Kharitonov, Klaus Turowski:
Investigating different optimization criteria for a hybrid job scheduling approach based on heuristics and metaheuristics. ACIS 2022: 48 - [c3]Abdulrahman Nahhas, Andrey Kharitonov, Klaus Turowski:
Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C). HICSS 2022: 1-10 - 2021
- [c2]Andrey Kharitonov, Abdulrahman Nahhas, Matthias Pohl, Klaus Turowski:
Comparative analysis of machine learning models for anomaly detection in manufacturing. ISM 2021: 1288-1297 - [c1]Abdulrahman Nahhas, Andrey Kharitonov, Ahmad Alwadi, Klaus Turowski:
Hybrid Approach for Solving Multi-Objective Hybrid Flow Shop Scheduling Problems with Family Setup Times. ISM 2021: 1685-1694
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last updated on 2024-11-14 00:56 CET by the dblp team
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