Optimising data-driven network under limited resource: a partial diversification approach

Abstract

This paper describes a cardinality constrained network flow structure whose special characteristics are used to analyse different risk aspects under an environment of uncertainty. The network structure developed is a suitable alternative to support financial planning and many other decision-making problems with limited resources. By setting a diversification level, we can manage systematic and non-systematic risks under a stochastic mixed integer linear programming framework. A dual decomposition method, Progressive Hedging (PH), is applied to more efficiently accommodate instances with large numbers of scenarios. We studied the impact of the level of the diversification on transaction costs and considered different factors that influence the performance of the algorithm. In particular, a Lagrangian bound is embedded to enhance the capacity of the method. Numerical results show the effectiveness of the proposed decision support approach.