Systematic Literature Review on Adjustable Robust Shortest Path Problem

In real-world optimization problems, effective path planning is important. The Shortest Path Problem (SPP) model is a classical operations research that can be applied to determine an efficient path from the starting point to the end point in a plan. However, in the real world, uncertainty is often encountered and must be faced. Significant uncertainty factors in the problem of determining the shortest path are problems that are difficult to predict, therefore new criteria and appropriate models are needed to deal with uncertainty along with the required efficient solution. The uncertainty factor can be formulated using an uncertain SPP optimization model, assuming parameters that are not known with certainty but are in an uncertain set. Problems with uncertainty in mathematical optimization can be solved using Robust Optimization (RO). RO is a methodology in dealing with the problem of data uncertainty caused by errors in data measurement. The uncertainty in the linear optimization problem model can be formed by loading the uncertainty that only exists in the constraint function by assuming its uncertainty using the Robust Counterpart (RC) methodology. In this paper, we will review the literature on the two-stage optimization model for the SPP problem using an Adjustable Robust Counterpart (ARC).