A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends

J Tang, G Liu, Q Pan - IEEE/CAA Journal of Automatica Sinica, 2021‏ - ieeexplore.ieee.org
Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is
increasing popularity in resolving different optimization problems and has been widely …

A survey of job shop scheduling problem: The types and models

H **ong, S Shi, D Ren, J Hu - Computers & Operations Research, 2022‏ - Elsevier
Job shop scheduling problem (JSSP) is a thriving area of scheduling research, which has
been concerned and studied widely by scholars in engineering and academic fields. This …

Flexible job-shop scheduling via graph neural network and deep reinforcement learning

W Song, X Chen, Q Li, Z Cao - IEEE Transactions on Industrial …, 2022‏ - ieeexplore.ieee.org
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …

Learning to dispatch for job shop scheduling via deep reinforcement learning

C Zhang, W Song, Z Cao, J Zhang… - Advances in neural …, 2020‏ - proceedings.neurips.cc
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …

A learning-based memetic algorithm for energy-efficient flexible job-shop scheduling with type-2 fuzzy processing time

R Li, W Gong, C Lu, L Wang - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Green flexible job-shop scheduling problem (FJSP) aims to improve profit and reduce
energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy …

Surprisingly popular-based adaptive memetic algorithm for energy-efficient distributed flexible job shop scheduling

R Li, W Gong, L Wang, C Lu… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
With the development of the economy, distributed manufacturing has gradually become the
mainstream production mode. This work aims to solve the energy-efficient distributed flexible …

Energy-optimized partial computation offloading in mobile-edge computing with genetic simulated-annealing-based particle swarm optimization

J Bi, H Yuan, S Duanmu, MC Zhou… - IEEE Internet of Things …, 2020‏ - ieeexplore.ieee.org
Smart mobile devices (SMDs) can meet users' high expectations by executing computational
intensive applications but they only have limited resources, including CPU, memory, battery …

Survey on genetic programming and machine learning techniques for heuristic design in job shop scheduling

F Zhang, Y Mei, S Nguyen… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Job shop scheduling (JSS) is a process of optimizing the use of limited resources to improve
the production efficiency. JSS has a wide range of applications, such as order picking in the …

Iterated greedy algorithms for flow-shop scheduling problems: A tutorial

ZY Zhao, MC Zhou, SX Liu - IEEE Transactions on Automation …, 2021‏ - ieeexplore.ieee.org
An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely
used to solve flow-shop scheduling problems (FSPs), an important branch of production …

A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times

Y Du, J Li, C Li, P Duan - IEEE Transactions on Neural …, 2022‏ - ieeexplore.ieee.org
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …