Network optimization models: Applications with python

A network contains a set of points and a set of lines that connect these points pairwise. Networks can emerge in different settings and in different ways. In today's competitive business environment, it is increasingly important for firms to make the most efficient use of their limited resources. This often occurs in decision-making processes to maximize profits or minimize costs. Problems such as ship, building, airplane, vehicle construction; shortest road, cable, line, pipe laying are examples of the kind of models that can be solved by converting them into network models. Such problems can also be solved using linear programming, but it is often more efficient to provide solutions with network models. Network optimization models are classified as minimum cost flow problem, shortest path problem, maximum flow problem, transportation problem, assignment problem, spanning tree problem and CPM/PERT models. The main objective of optimization models is to use operations research techniques to provide participants with the ability to make real-life applications based on mathematical modeling. In this context, topics that are part of operations research techniques, such as transportation and logistics problems, are often classified and studied as network flow problems. The "Literature Review" section of this study summarizes the relevant studies in the literature. The next section, "Materials and Methods", describes the approaches, models and algorithms proposed in this thesis, followed by "Application and Results", which presents the results obtained by applying the proposed approaches. The "Conclusion and Recommendations" section in the last part presents an overall evaluation of the thesis. In this study, Network optimization models will be examined and information about their applicability in different fields will be given. In addition, a large number of applications available in the literature on network optimization are discussed. These applications are also analyzed using the PYTHON programming language and the results are compared and recommendations are made.