Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment

Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting toward...

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Vydané v:Mathematics (Basel) Ročník 12; číslo 1; s. 28
Hlavní autori: Mardešić, Nikola, Erdelić, Tomislav, Carić, Tonči, Đurasević, Marko
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2024
Predmet:
Analysis
approximate dynamic programming
Customer services
Customers
Datasets
Evolution
Literature reviews
Logistics
Logistics services
Markov decision process
reinforcement learning
review
stochastic dynamic vehicle routing problem
Taxonomy
Travel time
Urban areas
Vehicle routing
Vehicles
ISSN:2227-7390, 2227-7390
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Shrnutí:Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, “traditional” deterministic approaches do not adequately fulfil constantly evolving customer expectations. To maintain competitiveness, logistic service providers must adopt proactive and anticipatory systems that dynamically model and evaluate probable (future) events, i.e., stochastic information. These events manifest in problem characteristics such as customer requests, demands, travel times, parking availability, etc. The Stochastic Dynamic Vehicle Routing Problem (SDVRP) addresses the dynamic and stochastic information inherent in urban logistics. This paper aims to analyse the key concepts, challenges, and recent advancements and opportunities in the evolving urban logistics landscape and assess the evolution from classical VRPs, via DVRPs, to state-of-art SDVRPs. Further, coupled with non-reactive techniques, this paper provides an in-depth overview of cutting-edge model-based and model-free reactive solution approaches. Although potent, these approaches become restrictive due to the “curse of dimensionality”. Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. In the scope of this research, we observed that addressing real-world SDVRPs with a comprehensive resolution encounters a set of challenges, emphasising a substantial gap in the research field that warrants further exploration.
Bibliografia:ObjectType-Article-1
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12010028