Please use this identifier to cite or link to this item:
Title: Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem
Authors: Janardhanan, MN
Li, Z
Nielsen, P
First Published: 6-Dec-2018
Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals for Springer Berlin Heidelberg
Citation: Soft Computing, 2018, pp. 1-14
Abstract: Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems.
DOI Link: 10.1007/s00500-018-03684-8
ISSN: 1432-7643
eISSN: 1433-7479
Embargo on file until: 6-Dec-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2018. Deposited with reference to the publisher’s open access archiving policy. (
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Engineering

Files in This Item:
File Description SizeFormat 
Revised+Manuscript-SOCO.pdfPost-review (final submitted author manuscript)703.81 kBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.