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A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem
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DOI：10.1016/j.eswa.2024.123515.png)
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Abstract
En 中文
The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real -world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern -based predictions of the distribution of item sizes in online bin packing. In comparison to traditional heuristics like BestFit(BF) and FirstFit(FF), as well as the more recent PatternPack(PaP) and ProfilePacking(PrP) algorithm based on online predictions, FPP demonstrates competitive and superior performance in solving various benchmark problems. Particularly, it excels in addressing problems with evolving distributions, making it a promising solution for real -world applications where the item sizes may change over time. This research unveils the promising potential of employing fuzzy logic to effectively address uncertainty in scheduling and planning problems.
Keywords:
Online bin packing
Planning under uncertainty
Learning for planning and scheduling
Fuzzy logic
Pattern-based planning
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