IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation (IEEE ESCO)

Scope and Aims

Evolutionary scheduling and combinatorial optimisation (ESCO) is an important research area at the interface of artificial intelligence (AI) and operations research (OR). ESCO has attracted the attentions of researchers over the years due to its applicability and interesting computational aspects. Evolutionary Computation (EC) techniques are suitable for these problems since they are highly flexible regarding handling constraints, dynamic changes, and multiple conflicting objectives. With the growth of new technologies and business models, researchers in this field have to continuously face new challenges, which required innovated solution methods.

This symposium focuses on both practical and theoretical aspects of Evolutionary Scheduling and Combinatorial Optimisation. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimisation, particle swarm optimisation, evolutionary based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning and evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary algorithms for reinforcement learning and transfer learning.

We welcome the submissions of quality papers that effectively use the power of EC techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimisation problems are also highly encouraged.


Topics of interest include, but not limited to:

  • Machine learning for scheduling and combinatorial optimisation
  • Production scheduling
  • Timetabling
  • Nurse rostering
  • Patient scheduling
  • Vehicle routing
  • Project scheduling
  • Airport runway scheduling
  • Transport scheduling
  • Grid/cloud scheduling and resource allocation
  • Evolutionary scheduling with Big Data
  • Web service composition
  • Wireless networking state location allocation
  • Project scheduling
  • 2D/3D strip packing
  • Space allocation
  • Multi-objective scheduling
  • Metamodel-based Evolutionary Algorithm for scheduling
  • Multiple interdependent decisions
  • Automated heuristic design
  • Innovative applications of evolutionary scheduling and combinatorial optimisation

Symposium chairs

Victoria University of Wellington, New Zealand
Nelishia Pillay
University of Pretoria, South Africa
Liang Gao
Huazhong University of Science and Technology, China
Rong Qu
University of Nottingham, UK

Programme Committee

  • Juergen Branke
  • Alexandre Sawczuk da Silva
  • Mark Johnston
  • Hui Ma
  • Rong Qu
  • Andy Song
  • Kay Tan
  • Jian Xiong
  • Jinghui Zhong
  • Ayad Turky
  • Chuan-Kang Ting
  • Emma Hart
  • Handing Wang
  • Liang Feng
  • Ke Tang
  • Kevin Sim
  • Liang Gao
  • Marko Durasevic
  • Mohamed Bader-El-Den
  • Mustafa Misir
  • Nasser Sabar
  • Nelishia Pillay
  • Ruhul Sarker
  • Tapabrata Ray
  • Wei Fang
  • Xianpeng Wang
  • Yuxin Liu
  • Boxiong Tan
  • Domagoj Jakobovic
  • Rui Wang
  • Hemant Singh
  • Yuping Wang
  • Yuning Chen
  • Xiaoning Shen
  • Xiaodong Li
  • fangfang Zhang
  • Yan Wang
  • Bin Xin
  • Xinye Cai
  • Weineng Chen
  • Zhihui Zhan
  • Yuejiao Gong
  • Changwu Huang
  • Aaron Chen
  • Lining Xing
  • Yinan Guo
  • Gong Dunwei
  • Jeffrey Chan
  • Yahui Jia
  • Sadeghiram Soheila
  • Jialin Liu
  • Ling Wang
  • Tao Shi
  • Ansari Mazhar
  • Gao Guanqiang
  • Ernest Andreas
  • Zhang Yuzhou
  • Dujuan Wang
  • Pan Quanke
  • Guo Qingxin
  • Li Changhe
  • Cai Xiweb
  • Jain Vipul