Production scheduling in the context of Industry 4.0: review and trends

M Parente, G Figueira, P Amorim… - International Journal of …, 2020 - Taylor & Francis
Notwithstanding its disruptive potential, which has been the object of considerable debate,
Industry4. 0 (I4. 0) operationalisation still needs significant study. Specifically, scheduling is …

Genetic programming for production scheduling: a survey with a unified framework

S Nguyen, Y Mei, M Zhang - Complex & Intelligent Systems, 2017 - Springer
Genetic programming has been a powerful technique for automated design of production
scheduling heuristics. Many studies have shown that heuristics evolved by genetic …

Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review

WT de Sousa Junior, JAB Montevechi… - Computers & Industrial …, 2019 - Elsevier
In recent years, some attention has been driven to modeling, simulation, and optimization
techniques capable of representing and improving discrete event systems. These …

Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production

CF Chien, YB Lan - Computers & Industrial Engineering, 2021 - Elsevier
Dynamic scheduling is crucial for semiconductor manufacturing as product-mix is increasing
with shortening product life cycle. However, the present problem is challenging owing to …

A state of the art review of intelligent scheduling

MH Fazel Zarandi, AA Sadat Asl, S Sotudian… - Artificial Intelligence …, 2020 - Springer
Intelligent scheduling covers various tools and techniques for successfully and efficiently
solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling …

Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automated material handling system capability

H Kim, DE Lim, S Lee - IEEE Transactions on Semiconductor …, 2020 - ieeexplore.ieee.org
Recently, the transportation capability of the automated material handling system (AMHS)
has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it …

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

M Khadivi, T Charter, M Yaghoubi, M Jalayer… - Computers & Industrial …, 2025 - Elsevier
Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …

Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics

M Đurasević, D Jakobović - Journal of Computational Science, 2022 - Elsevier
Dispatching rules are fast and simple procedures for creating schedules for various kinds of
scheduling problems. However, manually designing DRs for all possible scheduling …

Discrete event simulation method as a tool for improvement of manufacturing systems

A Kampa, G Gołda, I Paprocka - Computers, 2017 - mdpi.com
The problem of production flow in manufacturing systems is analyzed. The machines can be
operated by workers or by robots, since breakdowns and human factors destabilize the …

Evolving scheduling heuristics with genetic programming for optimization of quality of service in weakly hard real-time systems

K Salamun, I Pavić, H Džapo, M Đurasević - Applied soft computing, 2023 - Elsevier
The weakly hard real-time system model is used for describing the real-time systems that
allow occasional violations of real-time timing constraints. These systems include real-time …