Machine learning approach for identification of objective function in production scheduling problems

Yoki Matsuoka, Tatsushi Nishi, Kevin Tiemey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Systems optimization techniques have become increasingly important in recent years. Experience and knowledge from human experts play a critical role in designing optimization tools for practical uses. These system's evaluation criteria should be selected to accurately reflect the intention of the human operators. In this paper, we propose a machine learning approach for the estimation of objective functions for production scheduling problems. We propose a method to identify the objective function of a problem consisting of the weighted sum of the completion time, the sum of the tardiness, the weighted number of tardy jobs, the maximum tardiness or the sum of setup costs. We consider a supervised learning scenario for predicting an objective function and evaluate several techniques, including a three layer neural network, random forest, and k-neighborhood method. We further investigate feature extraction methods to achieve higher identification accuracy. The effectiveness of the proposed method is verified by comparing the results with methods based on a simplified method that does not use machine learning.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages679-684
Number of pages6
ISBN (Electronic)9781728103556
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Country/TerritoryCanada
CityVancouver
Period8/22/198/26/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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