A hybrid evolutionary approach for the single-machine total weighted tardiness problem

Junwen Ding, Zhipeng Lü, Edwin Tai Chiu Cheng, Liping Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


HEA solves all the standard benchmark problem instances with 40, 50, and 100 jobs from the literature within 0.04 s. For larger instances with 150, 200, 250, and 300 jobs, HEA obtains the optimal solutions for all of them within four minutes. To the best of our knowledge, HEA is the only metaheuristic algorithm that can obtain the optimal solutions for all the 25 instances with 1000 jobs within an average time of 3.97 h, demonstrating the efficacy of HEA in terms of both solution quality and computational efficiency. Furthermore, some key features of HEA are analyzed to identify its critical success factors.
Original languageEnglish
Pages (from-to)70-80
Number of pages11
JournalComputers and Industrial Engineering
Publication statusPublished - 1 Jun 2017


  • Buffer technique
  • Fast neighbourhood search
  • Heuristics
  • Hybrid evolutionary algorithm
  • Single-machine total weighted tardiness

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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