Novel Learning Algorithm based on a Multi-Agent Structure for Solving Multi-Mode Resource-Constrained Project Scheduling Problem
Year
: 2013
Abstract: The resource-constrained project scheduling problem
(RCPSP) includes activities which have to be scheduled due to
precedence and resource restrictions such that an objective is
satisfied. There are several variants of this problem currently,
and also different objectives are considered with regards to the
specific applications. This paper tries to introduce a new multiagent
learning algorithm (MALA) for solving the multi-mode
resource-constrained project scheduling problem (MMRCPSP),
in which the activities of the project can be performed in multiple
execution modes. This work aims to minimize the total project
duration which is referred to its makespan. The experimental
results show that our method is a new one for this specific
problem and can outperform other algorithms in different areas
(RCPSP) includes activities which have to be scheduled due to
precedence and resource restrictions such that an objective is
satisfied. There are several variants of this problem currently,
and also different objectives are considered with regards to the
specific applications. This paper tries to introduce a new multiagent
learning algorithm (MALA) for solving the multi-mode
resource-constrained project scheduling problem (MMRCPSP),
in which the activities of the project can be performed in multiple
execution modes. This work aims to minimize the total project
duration which is referred to its makespan. The experimental
results show that our method is a new one for this specific
problem and can outperform other algorithms in different areas
Keyword(s): Multi-Agent Systems,Machine Learning,Multi-
Mode Resource-Constrained Project Scheduling Problem,
MMRCPSP
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Novel Learning Algorithm based on a Multi-Agent Structure for Solving Multi-Mode Resource-Constrained Project Scheduling Problem
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| contributor author | Omid Mirzaei | en |
| contributor author | محمدرضا اکبرزاده توتونچی | en |
| contributor author | Mohammad Reza Akbarzadeh Totonchi | fa |
| date accessioned | 2020-06-06T13:13:29Z | |
| date available | 2020-06-06T13:13:29Z | |
| date issued | 2013 | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3346562 | |
| description abstract | The resource-constrained project scheduling problem (RCPSP) includes activities which have to be scheduled due to precedence and resource restrictions such that an objective is satisfied. There are several variants of this problem currently, and also different objectives are considered with regards to the specific applications. This paper tries to introduce a new multiagent learning algorithm (MALA) for solving the multi-mode resource-constrained project scheduling problem (MMRCPSP), in which the activities of the project can be performed in multiple execution modes. This work aims to minimize the total project duration which is referred to its makespan. The experimental results show that our method is a new one for this specific problem and can outperform other algorithms in different areas | en |
| language | English | |
| title | Novel Learning Algorithm based on a Multi-Agent Structure for Solving Multi-Mode Resource-Constrained Project Scheduling Problem | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | Multi-Agent Systems | en |
| subject keywords | Machine Learning | en |
| subject keywords | Multi- Mode Resource-Constrained Project Scheduling Problem | en |
| subject keywords | MMRCPSP | en |
| journal title | Journal of Convergence | fa |
| pages | 47-51 | |
| journal volume | 4 | |
| journal issue | 1 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1035353.html | |
| identifier articleid | 1035353 |