Abstract
This article discusses problems in the context of multicommodity network design where additional constraints (such as capacity), rather than being imposed in a strict manner, are allowed to be violated at the expense of additional penalty costs. Such penalized cost structures allow these constraints to be treated as utilization targets and provide a better modelling framework in terms of strategic or tactical level planning of network design, especially in freight transportation systems. However, due to the penalized costs, these problems are generally in the form of a nonlinear integer multicommodity network design problem. This article presents two algorithms based on Lagrangean relaxation and decomposition for the solution of such problems. The first relies upon dualizing the capacity constraints that results in a flow decomposition, and the second is through relaxing flow constraints that results in an arc decomposition. It is shown that nonlinearities in the decomposed substructures can be handled in a very efficient manner. Arc decomposition is shown, through computational experiments, to have better convergence properties. Through the proposed algorithms, reasonably good solutions can be obtained for these problems where publicly available state-of-the-art nonlinear optimization codes fail to identify feasible solutions. (C) 2009 Wiley Periodicals, Inc. NETWORKS, Vol. 55(3), 171-180 2010