Monte-Carlo Tree Search for 3D Packing with Object Orientation

Edelkamp, Stefan; Gath, Max; Rohde, Moritz

doi:10.1007/978-3-319-11206-0_28

Cited by 27 publications

(13 citation statements)

References 11 publications

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“…They have optimized the algorithm for the Traveling Salesman with Time Windows (TSPTW) problem [24,25]. Other applications deal with 3D Packing with Object Orientation [26], the physical traveling salesman problem [27], the Multiple Sequence Alignment problem [28] or Logistics [29]. The principle of NRPA is to adapt the playout policy so as to learn the best sequence of moves found so far at each level.…”

mentioning

confidence: 99%

Playout policy adaptation with move features

Cazenave

2016

Theoretical Computer Science

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confidence: 99%

Playout policy adaptation with move features

Cazenave

2016

Theoretical Computer Science

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“…Moura and Bortfeldt also used a tree search algorithm for filling trucks in the work mentioned earlier [ 27 ] but in this case, the tree search was not Monte Carlo—it was a recursive process to ensure the pallets were stacked in an order suitable for delivery. Edelkamp, Gath and Rohde in [ 32 ] also used a sub-variant of MCTS called Nested Rollout Policy Adaptation (NRPA) which is a variant of Nested Monte Carlo Search (NMCS) for both two- and three-dimensional variants of the container loading problem and reported some success.…”

Section: Related Workmentioning

confidence: 99%

“…The Length-Driven Algorithm is inspired by existing MCTS techniques including the one used in [ 32 ] for the container loading problem. However, it should be noted that in that paper the test set used for the two-dimensional case was strongly heterogeneous.…”

Section: Proposed Algorithmmentioning

confidence: 99%

An Entropy-Guided Monte Carlo Tree Search Approach for Generating Optimal Container Loading Layouts

Cant

Remi-Omosowon²,

Langensiepen

et al. 2018

Entropy

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In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having "consistency" or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.

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“…They have optimized the algorithm for the Traveling Salesman with Time Windows (TSPTW) problem [15,16]. Other applications deal with 3D Packing with Object Orientation [18], the physical traveling salesman problem [19], the Multiple Sequence Alignment problem [20], Logistics [17,11], Graph Coloring [12] and Inverse Folding [10]. The principle of NRPA is to adapt the playout policy so as to learn the best sequence of moves found so far at each level.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Search Algorithms for Network Traffic Engineering

Dang

Bazgan

Cazenave

et al. 2021

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

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The aim of Traffic Engineering is to provide routing configurations in networks such that the used resources are minimized while maintaining a high level of quality of service (QoS). Among the optimization problems arising in this domain, we address in this paper the one related to setting weights in networks that are based on shortest path routing protocols (OSPF, IS-IS). Finding weights that induce efficient routing paths (e.g that minimize the maximum congested link) is a computationally hard problem. We propose to use Monte Carlo Search for the first time for this problem. More specifically we apply Nested Rollout Policy Adaptation (NRPA). We also extend NRPA with the force_exploration algorithm to improve the results. In comparison to other algorithms NRPA scales better with the size of the instance and can be easily extended to take into account additional constraints (cost utilization, delay, . . . ) or linear/non-linear optimization criteria. For difficult instances the optimum is not known but a lower bound can be computed. NRPA gives results close to the lower bound on a standard dataset of telecommunication networks.

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Monte-Carlo Tree Search for 3D Packing with Object Orientation

Cited by 27 publications

References 11 publications

Playout policy adaptation with move features

Playout policy adaptation with move features

An Entropy-Guided Monte Carlo Tree Search Approach for Generating Optimal Container Loading Layouts

Monte Carlo Search Algorithms for Network Traffic Engineering

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