Hybrid heuristic algorithms for the multi objective load balancing of 2D bin packing problems

Date

2015-09-23

Author

Muhammet, Beyaz
Dökeroğlu, Tansel
Coşar, Ahmet

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2D Bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem. Multiobjective versions of this well-known industrial engineering problem can occur frequently in real world application. Recently, Hybrid Evolutionary Algorithms have appear as a new area of research with their ability to combine alternative heuristics and local search mechanisms together for higher quality solutions. In this study, we propose a set of novel multiobjective hybrid genetic and memetic algorithms that make use of the state-of-the-art metaheuristics and local search techniques for minimizing the number of bins while also maintaining the load balance. We analyze the optimization time and the resulting solution quality of the proposed algorithms on an offline 2DBPP benchmark problem set with 500 instances. Using these results of exhaustive experiments, we conclude that the proposed hybrid algorithms are robust with their ability to obtain a high percentage of the optimal solutions.

Subject Keywords

Genetic algorithm, Load balance, Memetic algorithm, Multiobjective genetic algorithm, Swap mutation

URI

http://link.springer.com/book/10.1007/978-3-319-22635-4
https://hdl.handle.net/11511/78237

Conference Name

30th International Symposium on Computer and Information Sciences, (23 - 25 Eylül 2015)

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

B. Muhammet, T. Dökeroğlu, and A. Coşar, “Hybrid heuristic algorithms for the multi objective load balancing of 2D bin packing problems,” presented at the 30th International Symposium on Computer and Information Sciences, (23 - 25 Eylül 2015), London, UK, 2015, Accessed: 00, 2021. [Online]. Available: http://link.springer.com/book/10.1007/978-3-319-22635-4.