Evaluating Predictive Models in the Orienteering Problem with Stochastic Profits: A Simulation Study

Abstract

Purpose The aim of this paper is to test the effectiveness of statistical model selection measures in terms of decision quality for the orienteering problem with stochastic profits using simulation. Design/methodology/approach – This paper is based on a quantitative numerical approach where various model selection measures are evaluated using computational experiments including model?based computer-generated random data. Findings – The findings of this paper include experimental results showing a deficiency of about 6.5 units of classical selection measures relative to a decision-based selection measure for the Tsiligirides orienteering benchmark instances. Discussion – While classical model selection measures are suitable for accuracy reasons, misspecified models sometimes do lead to better decision outcomes. From a practical perspective, in order to carry out prescriptive analytics for orienteering problems, having access to a reasonable decision algorithm at the prediction stage of data-analysis can be beneficial for downstream realized profit.