Data-Based Identification Method for Jobshop Scheduling Problems Using Timed Petri Nets

Tatsushi Nishi, Naoki Shimamura

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

Abstract

We address a data-based identification method of machine scheduling problems using timed Petri nets. A general machine scheduling model is represented by timed Petri nets with resource places. Given a set of machines and jobs, and their starting times and completion times of several machines, the objective is to find resource constraints of a given machine scheduling problem from input and output data. The problem is to find the connectivity of each resource place in the operational places. A mixed integer linear programming model is formulated to find an optimal connectivity of resource places to minimize the mean square error of the input and output data. An approximation algorithm is developed to apply larger instances. Numerical examples are provided to show the effectiveness of the proposed approximation algorithm.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
PublisherIEEE Computer Society
Pages1461-1465
Number of pages5
ISBN (Electronic)9781538667866
DOIs
Publication statusPublished - Jan 9 2019
Externally publishedYes
Event2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand
Duration: Dec 16 2018Dec 19 2018

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2019-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
CountryThailand
CityBangkok
Period12/16/1812/19/18

Keywords

  • approximation algorithm
  • model identification
  • Petri nets
  • scheduling

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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