Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules

Research output: Journal contributionsConference article in journalResearchpeer-review


The complexity of flexible job shop problems increases significantly when using autonomous guided vehicles (AGVs) for material handling. In this study, priority rules - commonly known for their simplicity and small computation time - for sequencing operations, routing jobs and dispatching vehicles are applied. Based on a discrete event simulation study with stochastic inter-arrival times, an artificial neural network is trained to learn interaction effects between the combination of different rules for sequencing, dispatching, routing, and the resulting system performance. Based on the trained network the combination of rules is optimized, reducing the mean tardiness of the jobs under varying system performance.
Original languageEnglish
JournalProcedia CIRP
Pages (from-to)1136-1141
Number of pages6
Publication statusPublished - 01.01.2019
EventConference on Manufacturing Systems - CIRP 2019: Manufacturing Systems for the Future Societies - Grand Hotel Union, Ljubljana, Slovenia
Duration: 12.06.201914.06.2019
Conference number: 52

Bibliographical note

Publisher Copyright:
© 2019 The Authors. Published by Elsevier Ltd.

    Research areas

  • Engineering - AGVs, Job shop, Neural network, Regression