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 …
increasing popularity in resolving different optimization problems and has been widely …
A survey of job shop scheduling problem: The types and models
H Xiong, 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 …
been concerned and studied widely by scholars in engineering and academic fields. This …
Learning to dispatch for job shop scheduling via deep reinforcement learning
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 …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection
The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …
combinatorial optimization problem, which effectively combines the memetic algorithm …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
Energy-optimized partial computation offloading in mobile-edge computing with genetic simulated-annealing-based particle swarm optimization
Smart mobile devices (SMDs) can meet users' high expectations by executing computational
intensive applications but they only have limited resources, including CPU, memory, battery …
intensive applications but they only have limited resources, including CPU, memory, battery …
A learning-based memetic algorithm for energy-efficient flexible job-shop scheduling with type-2 fuzzy processing time
Green flexible job-shop scheduling problem (FJSP) aims to improve profit and reduce
energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy …
energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy …
Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem
Z Zhao, S Liu, MC Zhou… - IEEE/CAA Journal of …, 2020 - ieeexplore.ieee.org
Group scheduling problems have attracted much attention owing to their many practical
applications. This work proposes a new bi-objective serial-batch group scheduling problem …
applications. This work proposes a new bi-objective serial-batch group scheduling problem …
An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling
J Mou, P Duan, L Gao, X Liu, J Li - Future Generation Computer Systems, 2022 - Elsevier
Distributed scheduling problem, a novel model of intelligent manufacturing, urgently needs
new scheduling methods to meet the dynamic market demand. The inverse scheduling in a …
new scheduling methods to meet the dynamic market demand. The inverse scheduling in a …
Two-stage knowledge-driven evolutionary algorithm for distributed green flexible job shop scheduling with type-2 fuzzy processing time
This study is investigated on multi-objective distributed green flexible job shop scheduling
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …