A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
Reinforcement learning applied to production planning and control
The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …
(RL) techniques in the production planning and control (PPC) field addressing the following …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
Towards omni-generalizable neural methods for vehicle routing problems
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
Learning to iteratively solve routing problems with dual-aspect collaborative transformer
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …
problems (VRPs). However, it is less effective in learning improvement models for VRP …
Dimes: A differentiable meta solver for combinatorial optimization problems
Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
Deep reinforcement learning for transportation network combinatorial optimization: A survey
Q Wang, C Tang - Knowledge-Based Systems, 2021 - Elsevier
Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …
combinatorial optimization problems, have attracted considerable attention for decades of …
A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times
Y Du, J Li, C Li, P Duan - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …
significantly improve the energy, cost, and time efficiency of production. As one type of …