A hybrid intelligent model for order allocation planning in make-to-order manufacturing

Z. X. Guo, Wai Keung Wong, S. Y.S. Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.
Original languageEnglish
Pages (from-to)1376-1390
Number of pages15
JournalApplied Soft Computing Journal
Issue number3
Publication statusPublished - 1 Jan 2013


  • Hybrid intelligence
  • Mento Carlo simulation
  • Multi-objective memetic algorithm
  • Order allocation planning
  • Pareto optimization

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'A hybrid intelligent model for order allocation planning in make-to-order manufacturing'. Together they form a unique fingerprint.

Cite this