An implementation of linear and nonlinear multicommodity network flows

This work presents a new code for solving the multicommodity network flow problem with a linear or nonlinear objective function considering additional linear side constraints that link arcs of the same or different commodities. For the multicommodity network flow problem through primal partitioning the code implements a specialization of Murtagh and Saunders' strategy of dividing the set of variables into basic, nonbasic and superbasic. Several tests are reported, using random problems obtained from different network generators and real problems arising from the fields of long and short-term hydrothermal scheduling of electricity generation and traffic assignment, with sizes of up to 150 000 variables and 45 000 constraints. The performance of the code developed is compared to that of alternative methodologies for solving the same problems: a general purpose linear and nonlinear constrained optimization code, a specialised linear multicommodity network flow code and a primal–dual interior point code.