Job shop scheduling optimization using genetic algorithm with global criterion technique

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Job Shop Scheduling Problem (JSSP) is a method which assigns multiple jobs to various machines. The large dimension of JSSP and the dynamic manufacturing environment have always been a difficult problem to optimize due to its size and complexity. In this study, three objective functions are selected namely, minimizing makespan, minimizing total cost and maximizing machine utilization. Genetic Algorithm (GA) is used to solve this scheduling problem. Lot size optimization technique is investigated for the potential of optimizing the makespan, total cost, and machine utilization objectives. Global Criterion (GC) Technique is implemented which can optimize multiple objectives all at once and obtain the best schedule. Finally, a case study is presented.

Original languageEnglish
Title of host publication39th Computers and Information in Engineering Conference
ISBN (Electronic)9780791859179
DOIs
StatePublished - 2019
EventASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States
Duration: 18 Aug 201921 Aug 2019

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume1

Conference

ConferenceASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Country/TerritoryUnited States
CityAnaheim
Period18/08/1921/08/19

Keywords

  • Genetic Algorithm (GA)
  • Global Criterion (GC)
  • Job Shop Scheduling Problem (JSSP)
  • Lot Size Optimization

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