ISSN:
1540-5915
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Economics
Notes:
Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory-floor decision making. A high-level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/j.1540-5915.1998.tb01580.x
