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The Optimal Production Transport: Model and Algorithm

The Optimal Production Transport: Model and Algorithm

Year:    2025

Author:    Jie Fan, Tianhao Wu, Hao Wu

East Asian Journal on Applied Mathematics, Vol. 15 (2025), Iss. 4 : pp. 787–816

Abstract

We propose the optimal production transport model, which is an extension of the classical optimal transport model. We observe in economics, the production of the factories can always be adjusted within a certain range, while the classical optimal transport does not take this situation into account. Therefore, differing from the classical optimal transport, one of the marginals is allowed to vary within a certain range in our proposed model. To address this, we introduce a multiple relaxation optimal production transport model and propose the generalized alternating Sinkhorn algorithms, inspired by the Sinkhorn algorithm and the double regularization method. By incorporating multiple relaxation variables and multiple regularization terms, the inequality and capacity constraints in the optimal production transport model are naturally satisfied. Alternating iteration algorithms are derived based on the duality of the regularized model. We also provide a theoretical analysis to guarantee the convergence of our proposed algorithms. Numerical results indicate significant advantages in terms of accuracy and efficiency. Furthermore, we apply the optimal production transport model to the coal production and transport problem. Numerical simulation demonstrates that our proposed model can save the production and transport cost by 13.17%.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/eajam.2024-095.140824

East Asian Journal on Applied Mathematics, Vol. 15 (2025), Iss. 4 : pp. 787–816

Published online:    2025-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    30

Keywords:    Optimal production and transport multiple relaxation generalized alternating Sinkhorn algorithm supply chain.

Author Details

Jie Fan

Tianhao Wu

Hao Wu