Monte caro tree search algorithm for the receiving containers intelligently problem among container shipping terminals

2021 ◽  
pp. 1-8
Author(s):  
Beng Xuan ◽  
Ning Zhao ◽  
Yifan Shen ◽  
Xueqiang Du
Keyword(s):  
Monte Carlo ◽  
Search Algorithm ◽  
Economic Globalization ◽  
Green Energy ◽  
Tree Search ◽  
Container Shipping ◽  
Monte Carlo Tree Search ◽  
Hard Problems ◽  
Terminal Operations ◽  
Tree Search Algorithm

With the development of economic globalization, shipping exchanges between countries are rapidly increasing and the container throughput of shipping ports has increased quickly, and the operation of most seaport terminals in China has reached a bottleneck which puts forward new requirements for the efficiency of container shipping terminal operations. Therefore, locating for export containers intelligently is of great significance for the development of terminals. This paper focuses on receiving containers intelligently, and establishes the export container locating model based on the principle of actual shipping terminal operations. The Monte Carlo Tree Search algorithm for export container locating problem was proposed and constructed. After examples, the algorithm can effectively solve the problem that meet the constraints, which further proves the practicability of the algorithm and the correctness of the model. The research shows that the method of locating for export container based on Monte Carlo Tree Search algorithm can effectively solve the problem and maintain the green, energy-saving and sustainable development of the shipping terminal. The ideas and methods have certain academic value and reference significance for other NP-Hard problems.

scholarly journals ALTERNATIVE SELECTION FUNCTIONS FOR INFORMATION SET MONTE CARLO TREE SEARCH

2014 ◽  
Vol 54 (5) ◽  
pp. 333-340
Author(s):  
Viliam Lisy
Keyword(s):  
Monte Carlo ◽  
Imperfect Information ◽  
Search Algorithm ◽  
Superior Performance ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Information Set ◽  
Imperfect Information Games ◽  
Zero Sum ◽  
Tree Search Algorithm

We evaluate the performance of various selection methods for the Monte Carlo Tree Search algorithm in two-player zero-sum extensive-form games with imperfect information. We compare the standard Upper Confident Bounds applied to Trees (UCT) along with the less common Exponential Weights for Exploration and Exploitation (Exp3) and novel Regret matching (RM) selection in two distinct imperfect information games: Imperfect Information Goofspiel and Phantom Tic-Tac-Toe. We show that UCT after initial fast convergence towards a Nash equilibrium computes increasingly worse strategies after some point in time. This is not the case with Exp3 and RM, which also show superior performance in head-to-head matches.

scholarly journals Monte Carlo Tree Search Algorithm for the Euclidean Steiner Tree Problem

2017 ◽  
Vol 4 ◽  
pp. 71-81
Author(s):  
Michał Bereta
Keyword(s):  
Monte Carlo ◽  
Steiner Tree ◽  
Search Algorithm ◽  
Exact Algorithms ◽  
Greedy Heuristic ◽  
Steiner Tree Problem ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Upper Confidence Bound ◽  
Tree Search Algorithm

This study is concerned with a novel Monte Carlo Tree Search algorithm for the problem of minimal Euclidean Steiner tree on a plane. Given p p p points (terminals) on a plane, the goal is to find a connection between all the points, so that the total sum of the lengths of edges is as low as possible, while an addition of extra points (Steiner points) is allowed. Finding the minimum Steiner tree is known to be np-hard. While exact algorithms exist for this problem in 2D, their efficiency decreases when the number of terminals grows. A novel algorithm based on Upper Confidence Bound for Trees is proposed. It is adapted to the specific characteristics of Steiner trees. A simple heuristic for fast generation of feasible solutions based on Fermat points is proposed together with a correction procedure. By combing Monte Carlo Tree Search and the proposed heuristics, the proposed algorithm is shown to work better than both the greedy heuristic and pure Monte Carlo simulations. Results of numerical experiments for randomly generated and benchmark library problems (from OR-Lib) are presented and discussed.

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