Monte Carlo Tree Search Methods for the Earth-Observing Satellite Scheduling Problem

2021 ◽  
pp. 1-13
Author(s):  
Adam P. Herrmann ◽  
Hanspeter Schaub
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Scheduling Problem ◽  
Search Methods ◽  
Monte Carlo Tree Search ◽  
The Earth ◽  
Satellite Scheduling

scholarly journals A Survey of Monte Carlo Tree Search Methods

2012 ◽  
Vol 4 (1) ◽  
pp. 1-43 ◽  
Author(s):  
Cameron B. Browne ◽  
Edward Powley ◽  
Daniel Whitehouse ◽  
Simon M. Lucas ◽  
Peter I. Cowling ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Search Methods ◽  
Monte Carlo Tree Search

scholarly journals Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics

2021 ◽  
Vol 11 (5) ◽  
pp. 2056
Author(s):  
Alba Cotarelo ◽  
Vicente García-Díaz ◽  
Edward Rolando Núñez-Valdez ◽  
Cristian González García ◽  
Alberto Gómez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Tree Search ◽  
Specific Knowledge ◽  
Search Methods ◽  
Monte Carlo Tree Search ◽  
Artificial Neural

Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.

Monte-Carlo Tree Search for a reentrant scheduling problem

2010 ◽  
Author(s):  
Shimpei Matsumoto ◽  
Noriaki Hirosue ◽  
Kyohei Itonaga ◽  
Nobuyuki Ueno ◽  
Hiroaki Ishii
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Scheduling Problem ◽  
Monte Carlo Tree Search

scholarly journals PARALLEL MACHINE SCHEDULING WITH MONTE CARLO TREE SEARCH

2021 ◽  
Vol 61 (2) ◽  
pp. 307-312
Author(s):  
Anita Agárdi ◽  
Károly Nehéz
Keyword(s):  
Monte Carlo ◽  
Production Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Setup Times ◽  
Tree Search ◽  
Scheduling Problem ◽  
Due Dates ◽  
Monte Carlo Tree Search ◽  
Machine Sequence

In this article, a specific production scheduling problem (PSP), the Parallel Machine Scheduling Problem (PMSP) with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. In this article after the introduction and literature review the mathematical model of the Parallel Machines Scheduling Problem with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. After that the Monte Carlo Tree Search and Simulated Annealing are detailed. Our representation technique and its evaluation are also introduced. After that, the efficiency of the algorithms is tested with benchmark data, which result, that algorithms are suitable for solving production scheduling problems. In this article, after the literature review, a suitable mathematical model is presented. The problem is solved with a specific Monte Carlo Tree Search (MCTS) algorithm, which uses a neighbourhood search method (2-opt). In the article, we present the efficiency of our Iterative Monte Carlo Tree Search (IMCTS) algorithm on randomly generated datasets.

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