Determining departure times in dynamic and stochastic maritime routing and scheduling problems

Tirado, Gregorio; Hvattum, Lars Magnus

doi:10.1007/s10696-016-9242-x

Cited by 3 publications

(2 citation statements)

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“…The authors integrate randomly sampled future requests into Tabu Search metaheuristics. Investigating the maritime SDVRP and the role of scheduled departure times for vessels, the authors in [148] utilised Tabu Search (TS) and future cargo requests via sample scenarios.…”

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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“…The dynamic version of the problem has been less studied in the literature. The only papers that we are aware of that deal with dynamic routing problems in maritime transportation are Tirado et al (2013), Tirado and Hvattum (2016a) and Tirado and Hvattum (2016b). In their problems, stochastic information on the new cargoes are assumed to be known and are utilized in scenario-based heuristics to produce the solution, whereas no stochastic information is assumed in our case.…”

Section: Literature Reviewmentioning

confidence: 99%

Centralised horizontal cooperation and profit sharing in a shipping pool

Wen

Larsen

Røpke

et al. 2018

Journal of the Operational Research Society

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