Authors:
Rui Zhao
1
;
Zhize Wu
1
;
Daan van den Berg
2
;
Matthias Thürer
3
;
Tianyu Liang
1
;
Ming Tan
1
and
Thomas Weise
1
Affiliations:
1
Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Jinxiu Dadao 99, Hefei, 230601, Anhui, China
;
2
Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, Amsterdam, 1081 HV, The Netherlands
;
3
Professur Fabrikplanung und Intralogistik, TU Chemnitz, Str. der Nationen 62, 09111 Chemnitz, Sachsen, Germany
Keyword(s):
Two-Dimensional Bin Packing, Randomized Local Search, Frequency Fitness Assignment, Cutting Stock Problem.
Abstract:
We consider a two-dimensional orthogonal bin packing problem (2BP) where rectangular items are to be placed into rectangular bins such that their edges are parallel to those of the bins with the aim to require as few bins as possible. Two variants of the problem are analyzed. In the 2BP|O|F, the items have a fixed orientation while in the 2BP|R|F, they can be rotated by 90 degrees. We show that on both variants, a simple randomized local search (RLS) has surprisingly good performance – if the objective function guiding the search is defined suitably. In particular, on the 2BP|O|F, the RLS performs on par with more complicated state-of-the-art metaheuristics. We furthermore investigate plugging Frequency Fitness Assignment (FFA) into the RLS, obtaining the FRLS. FFA has improved the RLS performance on several classical N P-hard optimization problems from operations research, including Max-SAT, the Job Shop Scheduling Problem, and the Traveling Salesperson Problem. This paper is the fi
rst negative result for FFA: it cannot improve algorithm performance on the 2BP variants studied. This can be explained by the fact that RLS already performs very well on the instances of the 2DPackLib benchmark set used as the basis of our experiments.
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