The aim of thesis is to study several new robust variants of network flow problems that differ in the form of initial problem (maximum flow or minimum cost maximum flow) and by some characteristics (in the way how uncertainty in parameters is expressed - in the way how scenarios are given, in the extent of parameter uncertainty or in the way how behavior of a given solution under a given scenario is measured). For each robust variant the aim is to analyze its important properties, for example computational complexity, and to implement at least one efficient algorithm capable of solving relatively large samples in reasonable time. In the first chapter we formulate optimization problem whose instance is described by values of parameters in its objective function and in its constraints that are usually hard to determine, since they depend on possible scenarios, so it is spoken about uncertainty while solving optimization problem. We observe flaws of stochastic approach toward solving problem with uncertainty and recommend robust approach as alternative to stochastic. In the second chapter we formulate robust flow problems and prove that by complexity (general) absolute and deviant robust variants of minimum cost flow problem are NP-hard. Besides minimum cost flow problem, maximum flow problem is also studied in standard literature, but it is shown that it makes no sense to consider its robust variants because they are trivial. In the third chapter we define residual network which is used for searching maximum flow and minimum cost maximum flow in initial network, that is layered residual network which is used for searching maximum flow in initial network. We define terms such as flow augmenting path or cost reducing cycle. We present basic operations with flows which are used as building blocks in (meta)heuristics and analyze their complexity. In the fourth chapter we formulate (general) robust minimum cost flow problem as an integer linear programming problem which is generally solved with exact algorithms for integer linear programming, via branch-and-bound or cutting-plane. In the fifth chapter we present (meta)heuristics as a set of optimization methods that are providing \acceptable" solutions in \reasonable" time upon solving hard and complex problems in science and engineering. As robust variants of minimum cost flow problem are NP-hard, it makes sense to solve them with (meta)heuristics, e.g. by local search. We describe their implementation and analyze their complexity. In the final chapter we choose representative and realistic, and relatively large samples of problem, and by experimental evaluation of (meta)heuristics for solving robust flow problems on such samples for each of considered robust variants, their precision and execution time is measured, and evaluation results are elaborated and compared, and pros and cons are revealed. In the first appendix we formulate related robust problems of linear programming, shortest path, minimum spanning tree and 0/1 knapsack, and prove that by complexity they are NP-hard. As robust variants of mentioned problems are polynomial-time reducible to corresponding robust variants of minimum cost flow problem, any algorithm that efficiently solves robust minimum cost flow problem will also sufficiently efficient solve mentioned related robust problems. For example, according to theorem 2.6, robust variants of shortest path problem are polynomial-time reducible to corresponding robust variants of minimum cost flow problem. In the second appendix standard network problems that appear when solving robust minimum cost flow problem with (meta)heuristics, e.g. by local search or evolution, are solved with classical algorithms. Those algorithms are used as building blocks. Therefore it is important that they are executed as efficiently as possible, i.e. that their complexity is polynomial-time. We describe implementation of classical algorithms for solving problems of minimal cut, maximal flow, shortest path, negative and simple cycle. In the final appendix there is a programming code of application’s configuration and (residual) network class.