To solve the cube packing problem with time schedule, this paper first introduces some concepts such as packing level, space distance and average neighbor birth order and then proposes a greedy algorithm. The algorithm tries every feasible corner greedily to calculate the space utilization, packing ...
importcs.min2phase.Search;importcs.min2phase.Tools;publicclassdemo{publicstaticvoidsimpleSolve(StringscrambledCube) {Stringresult=newSearch().solution(scrambledCube,21,100000000,0,0);System.out.println(result);// R2 U2 B2 L2 F2 U' L2 R2 B2 R2 D B2 F L' F U2 F' R' D' L2 R'}public...
From the well-known connection between separation and optimization [77,45,44], solving the dual separation problem to within a (1+ε) accuracy suffices to solve the configuration LP within (1+ε) accuracy. We refer the readers to [51] for an explicit proof that, for any set family C...
Code for DeepCubeA, a Deep Reinforcement Learning algorithm that can learn to solve the Rubik's cube. - forestagostinelli/DeepCubeA
In this paper, Firefly Algorithm is used to solve 3D packing of arbitrary sized heterogeneous bins into a container of standard size, by considering packing constraints namely placement constraint, overlapping constraint, stability constraint, weight constraint, load bearing constraint and orientation constr...
The RNN was proposed to solve the problem that a DNN has difficulty fitting data that changes temporally. Therefore, RNNs have played an important role in areas such as natural language processing and action recognition (Tang et al., 2018). RNNs are increasingly applied to IDS, whose data ...
Packing problem has been proved to be an NP-hard problem. Many algorithms such as simulation annealing algorithm, genetic algorithm and other heuristic algorithms have been proposed to solve twodimensional and three-dimensional packing problem. To solve the cube packing problem with time schedule, thi...
Finally, a genetic algorithm is designed to solve the model and verify its effectiveness through specific examples, aiming to achieve optimized operational costs and reduced carbon emissions. This paper provides theoretical support for shipping companies in making route allocation decisions under carbon ...
Finally, a genetic algorithm is designed to solve the model and verify its effectiveness through specific examples, aiming to achieve optimized operational costs and reduced carbon emissions. This paper provides theoretical support for shipping companies in making route allocation decisions under carbon ...