scholar.google.com › citations
Sep 2, 2024 · The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing.
Sep 2, 2024 · In this paper, we propose a novel end-to-end Deep Reinforcement Learning. (DRL) method. We model the IPPS problem as a Markov Decision Process ( ...
Sep 10, 2024 · In this paper, we propose a novel end-to-end Deep Reinforcement Learning (DRL) method. We model the IPPS problem as a Markov Decision Process ( ...
Sep 3, 2024 · This paper proposes a novel approach to solve the integrated process planning and scheduling (IPPS) problem using a graph neural ...
Sep 4, 2024 · The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in ...
This paper presents a novel framework named GraSP-RL, GRAph neural network-based Scheduler for Production planning problems using Reinforcement Learning.
Recently, deep reinforcement learning has been used to automate the process of designing PDRs, where PDRs are formulated as a Markov Decision Process exploiting ...
The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing ...
This paper presents an integrated modelling method based on a graph neural network (GNN) and multi-agent reinforcement learning (MARL) collaborative control
Missing: via | Show results with:via
This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based ...