Stochastic vehicle routing

Gendreau, Michel; Laporte, Gilbert; Séguin, René

doi:10.1016/0377-2217(95)00050-x

Cited by 533 publications

(254 citation statements)

References 31 publications

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“…The SVRP differs from its deterministic counterpart: several fundamental properties of the deterministic VRPs no longer hold in the stochastic case, and solution methodologies are considerably more complicated. A general review on the SVRP appeared in [23]. The Stochastic Vehicle Routing Problem can be broadly classified based on the following criteria:…”

Section: Stochastic Vrpsmentioning

confidence: 99%

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Shen

Ordóñez

Dessouky

2009

Springer Optimization and Its Applications

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Summary. In this paper, we are interested in routing vehicles to minimize unmet demand with uncertain demand and travel time parameters. Such a problem arises in situations with large demand or tight deadlines, so that routes that satisfy all demand points are difficult or impossible to obtain. An important application is the distribution of medical supplies to respond to large-scale emergencies, such as natural disasters or terrorist attacks. We present a chance constrained formulation of the problem that is equivalent to a deterministic problem with modified demand and travel time parameters, under mild assumptions on the distribution of stochastic parameters; and relate it with a robust optimization approach. A tabu heuristic is proposed to solve this MIP and simulations are conducted to evaluate the quality of routes generated from both deterministic and chance constrained formulations. We observe that chance constrained routes can reduce the unmet demand by around 2%-6% for moderately tight deadline and total supply constraints.

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Section: Stochastic Vrpsmentioning

confidence: 99%

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Shen

Ordóñez

Dessouky

2009

Springer Optimization and Its Applications

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“…For reviews and surveys on stochastic vehicle routing problems, the reader is referred to Gendreau et al (1996) and Kenyon (2000).…”

Section: Literature Reviewmentioning

confidence: 99%

Local search heuristics for the probabilistic dial-a-ride problem

Haugland

2009

OR Spectrum

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This paper describes an efficient neighborhood search procedure for the probabilistic dial-a-ride problem. This procedure requires O(n 4 ) computations compared to O(n 6 ) if computed from scratch. Two heuristics with this search procedure embedded are developed for solving the problem: a tabu search heuristic and a hybrid GRASP-tabu search heuristic. Computational results show that tabu search is superior over GRASP-tabu search on the probabilistic dial-a-ride problem, and by incorporating the search technique resulted in faster heuristics.

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“…Therefore, the reliability of transportation plans is progressively considered as a key performance indicator for logistics service providers and freight forwarders. A direct consequence of this changed operating environment is increasing awareness for the new developments in OR literature, such as modeling travel time uncertainties (see e.g., Noland and Small 1995;Gendreau et al 1996;Kok et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

et al. 2016

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The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO 2 e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. DOI 10.1007/s10696-016-9267-1 show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans.

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Stochastic vehicle routing

Cited by 533 publications

References 31 publications

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Local search heuristics for the probabilistic dial-a-ride problem

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

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