DOE-based Enhanced Genetic Algorithm for Unrelated Parallel Machine Scheduling to Minimize Earliness and Tardiness Costs
Kianpour, Parsa Gupta, Deepak P. Krishnan, Krishna K. Gopalakrishnan, Bhaskaran
Kianpour, Parsa
Gupta, Deepak P.
Krishnan, Krishna K.
Gopalakrishnan, Bhaskaran
Authors
Kianpour, Parsa
Gupta, Deepak P.
Krishnan, Krishna K.
Gopalakrishnan, Bhaskaran
Gupta, Deepak P.
Krishnan, Krishna K.
Gopalakrishnan, Bhaskaran
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2023
Type
Article
Genre
Keywords
DOE,Earliness,GA,Heuristic,Job Shop,Tardiness,Unrelated Parallel Machine Scheduling
Subjects (LCSH)
Citation
Kianpour, P., Gupta, D., Krishnan, K., Gopalakrishnan, B. DOE-based Enhanced Genetic Algorithm for Unrelated Parallel Machine Scheduling to Minimize Earliness and Tardiness Costs. (2023). Journal of Optimization in Industrial Engineering, 16 (2), pp. 99-114. DOI: 10.22094/JOIE.2023.1967925.1992
Abstract
This study presents an enhanced genetic algorithm (E-GA) to minimize earliness/tardiness costs in the job shop environment. It considers an unrelated parallel machine scheduling problem with a limit on maximum tardiness levels. This problem is motivated by the experience of one of the authors in a job shop supporting the local aircraft industry that requires strict control on delivery times. Current literature does not consider this critical restriction and unsuccessfully tries to deal with them using higher penalty costs. The proposed method uses the design of experiment (DOE) concept while optimizing the GA operators. Furthermore, it improves the initial solution using a hybrid dispatch rule through a strategic combination of construction and improvement heuristics. The model was applied to a local job shop. The results indicate that E-GA provides a schedule with lower cost and reduced computational time compared to existing dispatch rules in the literature and existing algorithms (OptQuest). © 2023 Qazvin Islamic Azad University. All rights reserved.
Table of Contents
Description
Publisher
Qazvin Islamic Azad University
Journal
Journal of Optimization in Industrial Engineering
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2251-9904