Applying network flow optimisation techniques to minimise cost associated with flood disaster.

Abstract

Flooding disasters in most parts of the world has become worrisome to the government and to the humanitarian emergency organisations. In this article, the authors proffer a mathematical solution to minimise the cost of rescue operations, using stochastic programming of a multicommodity and multimodel network flow. In the formulation, the authors considered four supply depots: national centre depot (NCD), three local distribution centres (LDCs) and six points of distribution (PODs). Two vehicle types were helicopters by air and trucks by land. Three basic types of emergency relief materials include food, water and medical items. Three basic scenarios were mild, medium and severe situations with associated probabilities of 0.25, 0.5 and 0.25, respectively. The formulated model was solved using the LINGO software. The results show that the formulated model effectively reduced the cost of distribution during emergency rescue operation, as there was a thin line between demand and met demand. For the scope of this model, a minimised cost of about $1016673.37 is sufficient to carry out successful rescue operations.

Contribution: The estimated amount of $1016673.37 becomes a benchmark for the government, research agencies and other developmental agencies for the purpose of planning. By using the air and road transport modes, and allowing direct and indirect transportation to the PODs, it saved time, resulting in many lives being saved.