Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

Abstract

Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evolve over time, synchronization issues should need to be considered, or a real-time re-optimization of the routing plan can be required as new data become available in a highly dynamic environment. Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios. After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based algorithms that have been designed and employed to solve VRPs with the aforementioned properties. These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements.

Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods

“…For instance, simheuristic can combine simulation and heuristics to model uncertainties and optimize energy exchanges, while learnheuristics use machine learning to predict patterns and improve decision-making. Agile optimization [32] ensures rapid adaptation to real-time data, making V2G systems more responsive and reliable. These techniques optimize energy distribution, support renewable energy integration, and contribute to a more sustainable and resilient power grid.…”

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

“…Routing optimization in the context of EnFVs involves the determination of efficient and optimal routes for message transmission, considering various objectives and constraints [17,18]. The objectives in multi-objective message routing optimization typically include minimizing message delivery time, maximizing network throughput, reducing energy consumption, and ensuring reliable communication.…”

“…These trade-offs arise due to the inherent conflicts and dependencies between objectives, making it challenging to find a single optimal solution that simultaneously satisfies all objectives. For instance, in electric vehicle routing, two primary objectives are minimizing travel time and minimizing energy consumption [17,80]. These objectives are inherently contradictory, as reducing the travel time often requires higher vehicle speeds and increased energy consumption.…”