A look-ahead partial routing framework for the stochastic and dynamic vehicle routing problem

Zou, Han; Dessouky, Maged

doi:10.1007/s41604-018-0006-5

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…A substantial body of literature has been dedicated to addressing the challenges of SDVRPs. For further reference on SDVRP approaches utilising (meta-)heuristics and stochastic sampling, one can refer to the extensive collection of studies within the heuristic approaches domain [50][51][52]57,64,65,71,72,[151][152][153][154][155][156][157][158][159], as well as the metaheuristic domain [48,53,[68][69][70]74,77,78,147,[160][161][162][163][164][165][166][167][168][169][170][171][172][173][174][175].…”

Section: Non-reactive Stochastic Sampling Solution Methodsmentioning

confidence: 99%

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

Mardešić,

Erdelić,

Carić

et al. 2023

Mathematics

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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.

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Section: Non-reactive Stochastic Sampling Solution Methodsmentioning

confidence: 99%

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

Mardešić,

Erdelić,

Carić

et al. 2023

Mathematics

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show abstract

“…In the CVRPSD with correlated demands, some information might not be known during the construction stage of an aprioristic or planned solution, despite this information can significantly influence the total cost (Ritzinger et al, 2015). Among the updated information that could be useful to plan the routes, we could consider travel times, service times, new or canceled customer requests, as well as customers' demands (Zou and Dessouky, 2018). However, this information is not always available at the time, when the aprioristic or planned solution is constructed.…”

Section: Demand Prediction In Vrpsmentioning

confidence: 99%

“…Hence, Markov et al (2016) present a methodology to solve a rich routing problem for collecting recyclable waste, where a daily demand is predicted by the statistical process of historical data of waste level in containers, which was obtained via ultrasonic sensors. Zou and Dessouky (2018) propose a look-ahead dynamic partial routing for the VRP with dynamic customer requests. In particular, it uses historical data to predict if some dynamic customers will request a delivery service once the planning stage has finished.…”

Section: Demand Prediction In Vrpsmentioning

confidence: 99%

Combining simheuristics with Petri nets for solving the stochastic vehicle routing problem with correlated demands

Latorre-Biel

Ferone

Juan

et al. 2021

Expert Systems with Applications

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“…On the other hand, the dynamic stochastic VRP benefits from the problem's prior knowledge. Three strategies, including stochastic modelling [29], sampling method and look-ahead dynamic routing [30] were mainly used in the literature.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Towards Novel Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems

Minku

Menzel

et al. 2020

Lecture Notes in Computer Science

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The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from typical real-world applications, like waste collection and mail delivery. However, few studies considered dynamic changes during the vehicles' service, which can make the original schedule infeasible or obsolete. The few existing studies are limited by dynamic scenarios that can suffer single types of dynamic events, and by algorithms that rely on special operators or representations, being unable to benefit from the wealth of contributions provided by the static CARP literature. Here, we provide the first mathematical formulation for dynamic CARP (DCARP) and design a simulation system to execute the CARP solutions and generate DCARP instances with several common dynamic events. We then propose a novel framework able to generalise all existing static CARP optimisation algorithms so that they can cope with DCARP instances. The framework has the option to enhance optimisation performance for DCARP instances based on a restart strategy that makes no use of past history, and a sequence transfer strategy that benefits from past optimisation experience. Empirical studies are conducted on a wide range of DCARP instances. The results highlight the need for tackling dynamic changes and show that the proposed framework significantly improves over existing algorithms.

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A look-ahead partial routing framework for the stochastic and dynamic vehicle routing problem

Cited by 13 publications

References 32 publications

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

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

Combining simheuristics with Petri nets for solving the stochastic vehicle routing problem with correlated demands

Towards Novel Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems

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