Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Tung Sun Chan, Rahul Swarnkar

Research output: Journal article publicationJournal articleAcademic researchpeer-review

90 Citations (Scopus)

Abstract

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.
Original languageEnglish
Pages (from-to)353-362
Number of pages10
JournalRobotics and Computer-Integrated Manufacturing
Volume22
Issue number4
DOIs
Publication statusPublished - 1 Aug 2006
Externally publishedYes

Keywords

  • Ant colony optimization
  • Flexible manufacturing systems
  • Fuzzy goal programming
  • Machine tool selection
  • Operation allocation
  • Production planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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