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Mixing Grain to Improve Profitability in Winter Wheat Using Evolutionary Algorithms

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Abstract

This paper considers an important component of the wheat distribution problem known as grain mixing (wheat blending). We represent the grain mixing problem as a permutation-based combinatorial optimization problem, which both a genetic algorithm and differential evolution are adapted and applied to solve. The proposed algorithms explore a search space that aims at finding a quality mixing of wheat from grain bins that generate the maximum profit at a grain elevator. The experimental results demonstrate that mixing bins provide more profit than not mixing and that the evolutionary approaches lead to consistently higher profits than the non-evolutionary methods.

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Notes

  1. Due to its length, the full proof can be found in [30].

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  1. Gianforte School of Computing, Montana State University, Bozeman, MT, 59717, USA

    Md Asaduzzaman Noor, John W. Sheppard & Sean Yaw

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  1. Md Asaduzzaman Noor
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  2. John W. Sheppard
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  3. Sean Yaw
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Correspondence to Md Asaduzzaman Noor.

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This article is part of the topical collection “Applications of bioinspired computing (to real world problems)” guest edited by Aniko Ekart, Pedro Castillo and Juanlu Jiménez-Laredo.

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Noor, M.A., Sheppard, J.W. & Yaw, S. Mixing Grain to Improve Profitability in Winter Wheat Using Evolutionary Algorithms. SN COMPUT. SCI. 3, 172 (2022). https://doi.org/10.1007/s42979-022-01062-8

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