Abstract
Resource allocation is a key decision-making process in project management that assigns resources to activities of a project and determines the timing of the allocation in a cost and time effective manner. In this research, we address the resource allocation for a project, where iterations between activities of the project exist and the crashing, a method to shorten the duration of an activity by incorporating additional resources, is available. Considering the stochastic nature of project execution, we formulate the resource allocation as a Markov decision process and seek the best resource allocation policy using a deep reinforcement learning algorithm. The feasibility and performance of applying the algorithm to the resource allocation is then investigated by comparison with heuristic rules.
Original language | English |
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Book series | IFAC-PapersOnLine |
Volume | 53 |
Issue number | 2 |
Pages (from-to) | 10493-10497 |
Number of pages | 5 |
ISSN | 2405-8963 |
DOIs | |
Publication status | Published - 2020 |
Event | 21th IFAC World Congress - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 |
Conference
Conference | 21th IFAC World Congress |
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Country/Territory | Germany |
City | Berlin |
Period | 12/07/2020 → 17/07/2020 |
Keywords
- Project Management
- Scheduling
- Decision Support Systems
- Resource Allocation
- Reinforcement Learning (RL)
- Deep Q-learning