Publication detail

Job Shop Scheduling Problem with Heuristic Genetic Programming Operators

POVODA, L. BURGET, R. MAŠEK, J. DUTTA, M.

Original Title

Job Shop Scheduling Problem with Heuristic Genetic Programming Operators

Type

conference paper

Language

English

Original Abstract

This paper introduces an optimization algorithm for job shop scheduling problem in logistic warehouses. The algorithm is based on genetic programming and uses parallel processing. For better performance a new optimization method called "priority rules" was proposed. We found out that the three proposed priority rules help algorithm to prevent stuck in the local optima and get better results from genetic programming optimization. Algorithm was tested with batch of tests based on data from real warehouse and with synthetic tests generated randomly (inspired by the real world scenarios). The results indicate interesting reduction of time that is necessary to fulfill all tasks in warehouses, reduction in number of collisions and better optimization performance.

Keywords

Process planning; job shop; warehouse optimization; heuristic operators; priority rules

Authors

POVODA, L.; BURGET, R.; MAŠEK, J.; DUTTA, M.

RIV year

2015

Released

19. 2. 2015

Location

Noida, Delhi-NCR, India

ISBN

978-1-4799-5990-7

Book

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

Pages from

702

Pages to

707

Pages count

6

URL

https://ieeexplore.ieee.org/document/7095307

BibTex

@inproceedings{BUT110193,
  author="Lukáš {Povoda} and Radim {Burget} and Jan {Mašek} and Malay Kishore {Dutta}",
  title="Job Shop Scheduling Problem with Heuristic Genetic Programming Operators",
  booktitle="2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)",
  year="2015",
  pages="702--707",
  address="Noida, Delhi-NCR, India",
  doi="10.1109/SPIN.2015.7095307",
  isbn="978-1-4799-5990-7",
  url="https://ieeexplore.ieee.org/document/7095307"
}