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

Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time
adjustments to production schedules, thereby enhancing system resilience and promoting …

This paper addresses the challenge of managing uncertainty in the daily capacity planning
of a terminal in a corridor-based logistics system. Corridor-based logistics systems facilitate …

In administrative processes, such as financial or governmental processes, humans typically
do most of the work and must be allocated to tasks in an efficient manner. This allocation is …

This paper presents a systematic review on reinforcement learning approaches for
combinatorial optimization problems based on real-world industrial applications. While this …

In recent years, the imperative for flexible and dynamic production scheduling has
intensified to swiftly adapt to evolving customer demands, ensuring manufacturing …

In administrative processes, such as financial or governmental processes, humans typically
do most of the work and must be allocated to tasks in an efficient manner. This allocation is …

This research explores the application of Deep Reinforcement Learning (DRL) to solve the
Flexible Job-Shop Scheduling Problem (FJSP) in a real-world manufacturing environment at …