Optimization Algorithms vs. random sampling of entry sources for a deliberate food contamination

Abstract

We focus on a deliberate scenario, where milk producers are used as entry sources for a contamination and where milk consumers are the target of the attack. The aim of this study is to demonstrate how the size of damage differs dependent on the use of an optimization algorithm or a random selection of entry sources. The results indicate that with a random selection of entry sources the same results can be provided with respect to the number of consumers reached, as with the application of the greedy algorithm. However, it should be also noted that with random selection of entry sources there is also a possibility of selecting milk producers, which would not reach any consumer with the hypothetical contaminated milk. The résumé is that by using the greedy algorithm always the “best” suited milk producers will be selected for a maximum spread of contaminated milk in our model. Risk managers can use these results in order to select the sources of entry in a timeand resource efficient manner.