Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …

The constructive approach within Neural Combinatorial Optimization (NCO) treats a
combinatorial optimization problem as a finite Markov decision process, where solutions are …

We present a neural network-enhanced column generation (CG) approach for a parallel
machine scheduling problem. The proposed approach utilizes an encoder-decoder attention …

This research presents a Reinforcement Learning (RL) framework for the Dynamic Weapon
Target Assignment (DWTA) problem, a combinatorial optimization problem with military …

This paper evaluates four Reinforcement Learning (RL) algorithms, namely, Proximal Policy
Optimization (PPO), Policy Gradient (PG), Advantage Actor-Critic (A2C), and Asynchronous …

The Job Shop Scheduling Problem (JSSP) consists of allocating various tasks to distinct
machines, each of which has a different sequence of operations. This thesis investigates the …