Call for Papers

Special Session on Genetic Programming and Machine Learning for Scheduling

SSCI 2022 - IEEE Symposium Series On Computational Intelligence

4 to 7 December 2022, Singapore

Aim and Scope:

Scheduling is an important optimisation problem that reflects the practical and challenging issues in real-world scheduling applications such as order picking in warehouses, the manufacturing industry and grid/cloud computing. Genetic programming, as a hyper-heuristic approach, has been successfully and widely used to learn scheduling heuristics for the scheduling problems. Learning scheduling heuristics with genetic programming has attracted the attention of researchers over the years due to its flexible representation. Other machine learning approaches such as reinforcement learning has also been widely used for scheduling problems. A number of machine learning techniques such as surrogate, feature selection, and multitask learning can be used to improve the quality of learned solutions/heuristics for scheduling. With the growth of new technologies, researchers in this field have to continuously face new challenges, which requires innovative approaches for scheduling.

This special session focuses on both practical and theoretical aspects of genetic programming and machine learning approaches for scheduling. Fundamental theoretical based approaches about genetic operators such as crossover, mutation are welcome. Novel approaches that use machine learning techniques for solving difficult scheduling problems are highly encouraged. Examples of machine learning techniques include surrogate, feature selection, and multitask learning. This special session focuses on using genetic programming and machine learning techniques to learn scheduling heuristics/solutions for the scheduling problems.

We welcome the submissions of quality papers that effectively use genetic programming to solve the scheduling problems. Papers with rigorous analyses of genetic programming, machine learning techniques and innovative solutions to handle challenging issues in scheduling problems are also highly encouraged.

Topics of interest include, but not limited to:

Application tasks:

1. Production scheduling including job shop scheduling, open shop scheduling, flow shop scheduling
2. Project scheduling
3. Tour scheduling
4. Crane scheduling
5. Task scheduling
6. Surgical scheduling
7. Resource allocation in cloud computing
8. Robot scheduling
9. Berth scheduling
10. Electricity distribution scheduling
11. Timetabling
12. Designing water distribution networks
13. Supply chain optimization
14. Travelling salesman problem
15. Knapsack problem
16. Other scheduling problems

Evolutionary machine learning:

1. Genetic programming
2. Evolutionary reinforcement learning
3. Neural network
4. Hyper-heuristics learning, including heuristic generation and heuristic selection
5. Genetic operators of evolutionary machine learning approaches
6. Adaptive machine learning approaches
7. Surrogate-Assisted evolutionary machine learning approaches
8. Feature selection in scheduling
9. Transfer learning in scheduling
10. Multitask scheduling
11. Local search in scheduling
12. Hybridisation of evolutionary algorithms with other machine learning and optimisation techniques for scheduling
13. Other evolutionary machine learning approaches such as artificial immune systems, learning classifier system, particle swarm optimisation, differential evolution, etc.

Submission Guideline:

Please follow the submission guideline from the SSCI 2022 Submission Website. Please specify that your paper is for the Special Session on “Genetic Programming and Machine Learning for Scheduling”. Special session papers are treated the same as regular conference papers.

Important Dates:

1. Paper Submission: Friday, 1st July 2022
2. Paper Acceptance: Thursday, 1st September 2022
3. Full Manuscript Submission: Monday. 19th September 2022
4. Early Registration: Monday, 26th September 2022
5. Conference Dates: 4th - 7th December 2022

Organisers:

Dr. Fangfang Zhang, fangfang.zhang@ecs.vuw.ac.nz, Victoria Univeristy of Wellington
Prof. Mengjie Zhang, mengjie.zhang@ecs.vuw.ac.nz, Victoria Univeristy of Wellington
Prof. Yi Mei, yi.mei@ecs.vuw.ac.nz, Victoria Univeristy of Wellington
Dr. Nguyen Su, P.Nguyen4@latrobe.edu.au, La Trobe University