Paşa, Tatiana Solving transportation problems with concave cost functions using genetic algorithms. (English) Zbl 1451.90019 Comput. Sci. J. Mold. 28, No. 2, 140-151 (2020). Summary: In this paper we propose a genetic algorithm for solving the nonlinear transportation problem on a network with concave cost functions and the restriction that the flow must pass through all arcs of the network. We show that the algorithm can be used in solving large-scale problems. We prove that the complexity of a single iteration of the algorithm is \(O(nm)\) and converges to an \(\epsilon \)-optimum solution. We also present some implementation and testing examples of the algorithm using Wolfram Mathematica. MSC: 90B06 Transportation, logistics and supply chain management 90C59 Approximation methods and heuristics in mathematical programming 90B10 Deterministic network models in operations research 90C06 Large-scale problems in mathematical programming Keywords:genetic algorithm; population; minimum cost flow; nonlinear transport problem; large-scale problem; concave function Software:Mathematica PDFBibTeX XMLCite \textit{T. Paşa}, Comput. Sci. J. Mold. 28, No. 2(83), 140--151 (2020; Zbl 1451.90019) Full Text: Link