Hybrid multi-objective evolutionary algorithm for solving RALB-II problem

Venkataramanaiah Saddikuti, Mukund Nilakantan Janardhanan and Vigneshwar Pesaru

International Journal of Operational Research, 2022, vol. 43, issue 1/2, 131-149

Abstract: In this paper, we propose an MIP model for minimisation of cycle time and total assembly line cost simultaneously. Due to NP-hard nature of RALB (Rubinovitz and Bukchin, 1991), and to avoid local minima, a hybrid multi-objective evolutionary (H-MOE) algorithm developed based on the features of NSGA-II and simulated annealing algorithm is used to solve the RALB-II problem. Performance of the proposed algorithm is evaluated using datasets from Mukund et al. (2017b) and it was found that H-MOE algorithm outperformed the algorithm by Mukund et al. (2017b) in five out of seven cases on saving in cycle time and four out of seven in terms of total cost saving. In terms of average improvement, the proposed algorithm outperformed in terms total cost saving and underperformed in terms of time cycle compared with the performance of algorithm by Mukund et al. (2017b). Conclusions and future scope are highlighted.

Keywords: hybrid algorithm; multi-objective; non-dominated sorting genetic algorithm; NSGA; robotic assembly line; RAL; parameter tuning. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=121490 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:43:y:2022:i:1/2:p:131-149

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().