A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems

By K. Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yu-yan Han, Quan-Ke Pan. IEEE/CAA Journal of Automatica Sinica, 2019

458 citations recorded by the source corpus.

Flexible job shop scheduling problems ( FJSP ) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence ( SI ) and evolutionary algorithms ( EA ) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm ( GA ) and one newest imperialist competitive algorithm ( ICA ) with variables neighborhood search ( VNS ) for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/cb460e60d1631aa942ffb09ccb61cf26cf9513a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Puay Siew Tan
2. Wen Song
3. Zhiguang Cao
4. Jie Zhang

In Industrial and Manufacturing Engineering

1. Wen Song
2. Qiqiang Li
3. Zhiguang Cao

Citations in the corpus

• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning (2020, background): “Job-shop scheduling problem (JSSP) is a well-known combinatorial optimization problem in computer science and operations research, and is ubiquitous in many industries such as manufacturing and transportation [1, 2].”
• Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning (2023, methodology): “Owing to its important application value, the FJSP receives much attention in the literature [6], [7].”