Reoptimisation strategies for dynamic vehicle routing problems with proximity-dependent nodes

Abstract Autonomous vehicles create new opportunities as well challenges to dynamic vehicle routing. The introduction of autonomous information-collecting agents results in scenarios, where nodes are found by proximity. This paper presents a novel vehicle-routing problem variant with proximity-dependent nodes. Here, we introduced variable, detectability , which determines whether proximal node will be detected, based on the sight radius vehicle. considered is motivated weed-spraying large agricultural operations. work generalisable many other applications. first step crafting solution approach for decide when reoptimisation should triggered. Two trigger strategies considered—exogenous and endogenous. Computational experiments compared both classical routing variant. Experiments used extensive standardised benchmarks varying degrees dynamism geographical distributions. showed that variant, an endogenous strategy better most cases, while exogenous only suitable low. Furthermore, optimal level was shown dependent combination trigger, degree dynamism, distribution, meaning practitioners may determine required attributes their specific problem.