Simulation-guided beam search for neural combinatorial optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …
discover powerful heuristics for solving complex real-world problems. While neural …
A new class of hard problem instances for the 0–1 knapsack problem
The 0–1 knapsack problem is an important optimization problem, because it arises as a
special case of a wide variety of optimization problems and has been generalized in several …
special case of a wide variety of optimization problems and has been generalized in several …
Method for designing fuel-efficient highway longitudinal slopes for intelligent vehicles in eco-driving scenarios
W Qi, Z Zou, L Ruan, J Wu - Applied Energy, 2024 - Elsevier
Reasonable road design can not only improve traffic efficiency, ensure driving safety, reduce
construction costs, but also reduce the energy consumption of road transportation. However …
construction costs, but also reduce the energy consumption of road transportation. However …
On monte carlo tree search for weighted vertex coloring
This work presents the first study of using the popular Monte Carlo Tree Search (MCTS)
method combined with dedicated heuristics for solving the Weighted Vertex Coloring …
method combined with dedicated heuristics for solving the Weighted Vertex Coloring …
Automated conceptual design of mechanisms based on Thompson Sampling and Monte Carlo Tree Search
J Mao, Y Zhu, G Chen, C Yan, W Zhang - Applied Soft Computing, 2025 - Elsevier
Conceptual design of mechanisms is a crucial part of achieving product innovation as
mechanisms perform the transmission and transformation of specific motions in the machine …
mechanisms perform the transmission and transformation of specific motions in the machine …
A data-driven approach to solving the container relocation problem with uncertainties
Container relocations are inevitable and reduce terminal efficiency, making their
optimization a critical research focus. Scholars have extensively studied the Container …
optimization a critical research focus. Scholars have extensively studied the Container …
A policy-based Monte Carlo tree search method for container pre-marshalling
Z Wang, C Zhou, A Che, J Gao - International Journal of Production …, 2024 - Taylor & Francis
The container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling
moves, ultimately achieving an optimised stacking arrangement in each bay based on the …
moves, ultimately achieving an optimised stacking arrangement in each bay based on the …
Multi-step carbon emissions forecasting model for industrial process based on a new strategy and machine learning methods
The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon
transformation for global sustainability. Accurate multi-step forecasting of industrial carbon …
transformation for global sustainability. Accurate multi-step forecasting of industrial carbon …
Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method
Z Luo, Y Tan, G Zhu, Y **a… - Advances in Mechanical …, 2023 - journals.sagepub.com
For the multi-objective flow shop scheduling problem in the supply chain environment, this
paper proposes the Fuzzy Relevance Entropy method (FREM) to solve the adaptive value …
paper proposes the Fuzzy Relevance Entropy method (FREM) to solve the adaptive value …
Expectation analysis for bounding solutions of the 0–1 knapsack problem
FA Morales, JA Martínez - Computational and Applied Mathematics, 2024 - Springer
In this paper, an entirely novel discrete probabilistic model is presented to generate 0–1
Knapsack Problem instances. We analyze the expected behavior of the greedy algorithm …
Knapsack Problem instances. We analyze the expected behavior of the greedy algorithm …