On solving periodic re-optimization dynamic vehicle routing problems

Abstract

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. One of its important variants is the Dynamic Vehicle Routing Problem (DVRP) in which not all customers are known in advance, but are revealed as the system progresses. DVRP is a Dynamic Optimization Problem (DOP) that has become a challenging research topic in the past two decades. In DOPs, at least one part of the problem changes as time passes. For DVRP, customers change as a system progresses. Consequently, DVRP applications are seen to operate on a dynamic basis in various real-life systems. To date, a time-based evaluation approach has been used to evaluate periodic re-optimized DVRP systems, which are evaluated by solving while using a specific time budget. In this paper, we solve DVRP while using an enhanced Genetic Algorithm (GA) that tries to increase both diversity and the capability to escape from local optima. Also, we propose a fair weighted fitness evaluation approach as an alternative for the biased time-based approach, regardless of the specifications and power of the running system. The proposed enhanced GA outperformed the previously published algorithms based on both the time-based and weighted fitness evaluation approaches.