Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Ziyan Zhao, Shixin Liu, Mengchu Zhou, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

134 Scopus citations


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 considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

Original languageEnglish (US)
Article number9310662
Pages (from-to)1199-1209
Number of pages11
JournalIEEE/CAA Journal of Automatica Sinica
Issue number6
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering


  • Insertion-based local search
  • iterated greedy algorithm
  • machine learning
  • memetic algorithm
  • nondominated sorting genetic algorithm II (NSGA-II)
  • production scheduling


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