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

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