• Generalized and dynamic flow models broaden the classical network flow framework to include set-based (polymatroidal) constraints and time-evolving costs, enabling dynamic planning and convex/concave objectives with polynomial-time solvability [1], [2], [15], [6], [7].

• Algorithmic innovations improved complexity and scalability: from new max-flow formulations and fast bipartite algorithms to reductions and distributed arborescence computations [8], [4], [19], [20], [9].

• Stochastic models for closed Markovian networks with product-form distributions, integral representations, and asymptotic expansions enabling tractable performance analysis of large systems [3], [5], [12].

• Studies of fault-tolerant routings, performance bounds under failures, and strategic attack/defense inform design for resilience under uncertainty [17], [18], [14], [9].