Using genetic algorithms to solve quality-related bin packing problem

Tung Sun Chan, K. C. Au, L. Y. Chan, T. L. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


The Bin Packing Problem is an industrial problem which involves grouping items into appropriate bin to minimize the cost and number of used bins. It provides a solution for assigning parts to optimize some predefined measures of productivity. In this study, Ion Plating (IP) industry requires similar approach on allocating production jobs into batches for producing better quality products and enabling to meet customer deadlines. The aim of this paper is to (i) develop a Bin Packing Genetic Algorithms (BPGA) with different weighting combinations, taking into account the quality of product and service; (ii) improve the production efficiency by reducing the production unit cost in IP. Genetic Algorithm was chosen because it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company was used to simulate the proposed BPGA model, and the computational results were compared with these industrial data. The results from three different weighting combinations demonstrated that fewer resources would be required by applying the proposed model in solving BP problem in the Ion Plating Cell.
Original languageEnglish
Pages (from-to)71-81
Number of pages11
JournalRobotics and Computer-Integrated Manufacturing
Issue number1
Publication statusPublished - 1 Feb 2007
Externally publishedYes


  • Bin packing
  • Genetic algorithms
  • Ion plating
  • Quality

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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