An enhanced single objective genetic algorithm scheduling tool for solving very large scheduling problems in the capital goods industry

  1. Lookup NU author(s)
  2. Dr Neo Xie
  3. Professor Christian Hicks
  4. Dr Pupong Pongcharoen
Author(s)Xie W, Hicks C, Pongcharoen P
Editor(s)Grubbstrom,RW; Hinterhuber,HH;
Publication type Conference Proceedings (inc. Abstract)
Conference NameSixteenth International Working Seminar on Production Economics
Conference LocationCongress Innsbruck, Innsbruck, Austria
Year of Conference2010
Legacy Date1st-5th March 2010
Volume3
Number of Volumes4
Pages517-530
Full text is available for this publication:
In this paper, an Enhanced Single Objective Genetic Algorithm Scheduling Tool (ESOGAST) is presented that is capable of solving very large scheduling problems such as those encountered in the capital goods industry. The tool minimises the penalties caused by the early or late delivery of components, assemblies and final products. The tool is optimised for speed; it runs more than 5000 times faster than the tool developed by Pongcharoen et al [1]. It is therefore capable of solving much larger problems within a reasonable amount of time. The ESOGAST includes an enhanced repair process to optimise the performance of the tool for scheduling the production of very complex products with many levels of product structure under finite capacity conditions. This paper describes the Genetic Algorithm and the data structures used in detail. A case study that used data obtained from a collaborating capital goods company is presented. A series of experiments that were used to identify the best combination of genetic operators and parameter settings is described. The work optimised an 18 month schedule for a manufacturing facility with 52 machines that produced several families of complex products with deep product structure.