Decision Sciences Journal
Volume 29, Number 2
Spring 1998

Learning by Objectives for Adaptive Shop-Floor Scheduling

Siddhartha Bhattacharyya
Department of Information and Decision Sciences, College of Business Administration, University of Illinois at Chicago, 601 S. Morgan Street, Chicago, IL 60607, email: sidb@uic.edu

Gary J. Koehler
Decision and Information Sciences, College of Business Administration, University of Florida, Gainesville, FL 32611, email: koehler@nervm.nerdc.ufl.edu

Abstract: 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.

Subject Areas: Genetic Algorithms, Intelligent Decision Support, Machine Learning, and Production Scheduling.