Genetic approach to solve economic lot-scheduling problem

Hing Kai Chan, Sai Ho Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Economic lot-scheduling problem (ELSP) has been studied since the 1950's. ELSP deals with the scheduling of the production of several products on a single machine in a cyclical pattern. The machine can only produce one single product at a time, and there is a set-up cost and set-up time associated with each product. Researchers generally adopted two types of rounding off methods for the production frequency of products, namely, the nearest integer and power-of- Two approaches. Production frequency of products defines the number of times that such product being produced during the cycle. Therefore, different production frequency actually leads to different optimization results. For this reason, this paper proposes a modified hybrid genetic algorithm to deal with this problem. Numerical examples are used to test the performance of the new approach. Results demonstrate the significance of the production frequency to the optimization results.
Original languageEnglish
Title of host publicationProceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology
PublisherSpringer Verlag
Pages885-895
Number of pages11
ISBN (Print)9783642104299
Publication statusPublished - 1 Jan 2010
Event6th CIRP International Conference on Digital Enterprise Technology, DET 2009 - Hong Kong, Hong Kong
Duration: 14 Dec 200916 Dec 2009

Publication series

NameAdvances in Intelligent and Soft Computing
Volume66 AISC
ISSN (Print)1867-5662

Conference

Conference6th CIRP International Conference on Digital Enterprise Technology, DET 2009
Country/TerritoryHong Kong
CityHong Kong
Period14/12/0916/12/09

Keywords

  • Economic lot-scheduling problem
  • Genetic algorithm
  • Inventory management
  • Production scheduling
  • Simulation

ASJC Scopus subject areas

  • Computer Science(all)

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