Modelling Inventory Grouping Decisions Using Grouping Genetic Algorithms

Michael Mutingi, Charles Mbohwa

Research output: Contribution to journalConference articlepeer-review


Decision makers are often faced with the problem of grouping inventory into categories for cost-effective and efficient management and control of inventory. The classical ABC inventory analysis has been applied widely in industry. However, the approach is associated with practical limitations: the desired service level and budget allocation constraints are not considered simultaneously, there is no guarantee for optimal solutions, and qualitative decision criteria are not modelled explicitly. It is desirable to develop models that can capture quantitative and qualitative criteria, from a multi-criteria optimization view point. In light of these limitations, the purpose of this research is to model the inventory grouping problem using grouping genetic algorithms approach. We first assess the grouping structure of the inventory classification problem, and then model the grouping problem from the grouping genetic algorithm perspective. Further research prospects and applications are evaluated and presented.

Original languageEnglish
Pages (from-to)64-70
Number of pages7
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Publication statusPublished - 2021
Event1st Central American and Caribbean International Conference on Industrial Engineering and Operations Management, IEOM 2021 - Port-au-Prince, Haiti
Duration: 15 Jun 202116 Jun 2021


  • ABC analysis
  • Inventory grouping
  • grouping genetic algorithm
  • inventory decisions

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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