Using MLP networks to design a production scheduling system

Authors:
Shan Feng;Ling Li;Ling Cen;Jingping Huang
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;Department of Information Systems and Decision Sciences, College of Business and Public Administration, Old Dominion University, Norfolk, VA;Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;Department of Control Science and Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
Venue:
Computers and Operations Research - Special issue: Emerging economics
Year:
2003

Citing 4
Cited 6

Decision support for fuzzy comprehensive evaluation of urban development

Fuzzy Sets and Systems
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

Computers and Industrial Engineering
Artificial Intelligence in Real Time Control 1997

Artificial Intelligence in Real Time Control 1997
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

Computers and Operations Research

Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks

Computers and Industrial Engineering
A review on evolution of production scheduling with neural networks

Computers and Industrial Engineering
A conceptual framework for product lifecycle modelling

Enterprise Information Systems
Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks

Computers and Industrial Engineering
Machine scheduling in custom furniture industry through neuro-evolutionary hybridization

Applied Soft Computing
Multilayer perceptron for simulation models reduction: Application to a sawmill workshop

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates the application of artificial neural networks to the problem of job shop scheduling with a scope of a deterministic time-varying demand pattern over a fixed planning horizon. The purpose of the research is to design and develop a job shop scheduling system (a scheduling software) that can generate effective job shop schedules using the multi-layered perceptron (MLP) networks. The contributions of this study include designing, developing, and implementing a production activity scheduling system using the MLP networks; developing a method for organizing sample data using a denotation bit to indicate processing sequence and processing time of a job simultaneously; using the back-propagation training process to control local minimal solutions; and developing a heuristics to improve and revise the initial production schedule. The proposed production activity schedule system is tested in a real production environment and illustrated in the paper with a sample case.