On the computational complexity of minimum-concave-cost flow in a two-dimensional grid

TL;DR

This paper analyzes the computational complexity of the minimum-concave-cost flow problem on two-dimensional grids, identifying specific conditions under which the problem is solvable in polynomial time and proving NP-hardness when these conditions are not met.

Contribution

The paper characterizes the complexity of the minimum-concave-cost flow problem on grids and proposes polynomial-time algorithms for certain cases, establishing NP-hardness otherwise.

Findings

Polynomial-time algorithms for specific grid configurations

NP-hardness results for general cases

Application to supply chain optimization models

Abstract

We study the minimum-concave-cost flow problem on a two-dimensional grid. We characterize the computational complexity of this problem based on the number of rows and columns of the grid, the number of different capacities over all arcs, and the location of sources and sinks. The concave cost over each arc is assumed to be evaluated through an oracle machine, i.e., the oracle machine returns the cost over an arc in a single computational step, given the flow value and the arc index. We propose an algorithm whose running time is polynomial in the number of columns of the grid, for the following cases: (1) the grid has a constant number of rows, a constant number of different capacities over all arcs, and sources and sinks in at most two rows; (2) the grid has two rows and a constant number of different capacities over all arcs connecting rows; (3) the grid has a constant number of rows and all sources in one row, with infinite capacity over each arc. These are presumably the most general polynomially solvable cases, since we show the problem becomes NP-hard when any condition in these cases is removed. Our results apply to abundant variants and generalizations of the dynamic lot sizing model, and answer several questions raised in serial supply chain optimization.