Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241387 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Open Innovation: Technology, Market, and Complexity [ISSN:] 2199-8531 [Volume:] 6 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time.
Subjects: 
cycle time
simple assembly line balancing problem type 2
variable neighborhood strategy adaptive search
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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