The significant increase in data availability and high-computing power and innovations in
real-time monitoring systems enable the technological transformation of the mining industry …

In this study, a systematic review on production scheduling based on reinforcement learning
(RL) techniques using especially bibliometric analysis has been carried out. The aim of this …

This study addresses the limitations of traditional active and semi-active suspension control
systems in terms of adaptability and nonlinear handling, by exploring the potential of Deep …

The parallel machine scheduling problem (PMSP) involves the optimized assignment of a
set of jobs to a collection of parallel machines, which is a proper formulation for the modern …

The open-pit production system is a highly dynamic and uncertain environment with
complex interactions between haulage and loading equipment on a shared road network …

Reward engineering is a key challenge in reinforcement learning (RL) that can significantly
affect the performance and applicability of RL algorithms. In the field of manufacturing …

Production scheduling is a critical task in the management of manufacturing systems. It is
difficult to derive an optimal schedule due to the problem complexity. Computationally …

This paper proposes a multi-agent reinforcement learning (MARL) approach to learn
dynamic dispatching strategies, which is crucial for optimizing throughput in material …

Material flow control in remanufacturing is an important issue in the field of disassembly.
This paper deals with the potential of autonomous material release decisions for …

The order picking process in a manufacturing supermarket is central in many industrial
productions as it ensures that the items required for production are provided at the right time …