  Network models provide an efficient way to represent many real life problems
mathematically. In the last few decades, the field of network optimization has
witnessed an upsurge of interest among researchers and practitioners. The
network models considered in this thesis are broadly classified into four types
including transportation problem, shortest path problem, minimum spanning tree
problem and maximum flow problem. Quite often, we come across situations, when
the decision parameters of network optimization problems are not precise and
characterized by various forms of uncertainties arising from the factors, like
insufficient or incomplete data, lack of evidence, inappropriate judgements and
randomness. Considering the deterministic environment, there exist several
studies on network optimization problems. However, in the literature, not many
investigations on single and multi objective network optimization problems are
observed under diverse uncertain frameworks. This thesis proposes seven
different network models under different uncertain paradigms. Here, the
uncertain programming techniques used to formulate the uncertain network models
are (i) expected value model, (ii) chance constrained model and (iii) dependent
chance constrained model. Subsequently, the corresponding crisp equivalents of
the uncertain network models are solved using different solution methodologies.
The solution methodologies used in this thesis can be broadly categorized as
classical methods and evolutionary algorithms. The classical methods, used in
this thesis, are Dijkstra and Kruskal algorithms, modified rough Dijkstra
algorithm, global criterion method, epsilon constraint method and fuzzy
programming method. Whereas, among the evolutionary algorithms, we have
proposed the varying population genetic algorithm with indeterminate crossover
and considered two multi objective evolutionary algorithms.
