A multi-space sampling heuristic for the green vehicle routing problem

Montoya, Alejandro; Guéret, Christelle; Mendoza, Jorge E.; Villegas, Juan G.

doi:10.1016/j.trc.2015.09.009

Cited by 168 publications

(99 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A few papers have addressed the green vehicle routing problem modeled in [10] or slight modifications thereof, and developed heuristic approaches for its solution. Montoya et al [16] address the problem in [10] and develop a two-stage heuristic that first generates a pool of routes by splitting randomized giant tours and then solving a set partitioning model on the pool of routes obtained in the first stage. The authors report improvement of best known solutions to some of the test instances.…”

Section: Green Vehicle Routingmentioning

confidence: 99%

“…Although an external service takes (1 − δ j )ρ time units, for sake of simplicity we consider external service time constant (ρ), which is a commonly used simplification in the GVRP literature (see, [10,16,24] for examples) as well as the preceding VRP with satellite facilities literature. We extend the definition of p j to include time spent at ESSs (p j = ρ, ∀j ∈ E) and the dummy nodes (p 0 = p n+s+1 = 0).…”

Section: Notationmentioning

confidence: 99%

See 1 more Smart Citation

An iterated beam search algorithm for the green vehicle routing problem

Yavuz

2017

Networks

View full text Add to dashboard Cite

Green, or, alternative-fuel vehicles provide opportunities to improve fleet operators' financial and environmental bottom lines. They also present challenges due to limited driving ranges, limited refueling infrastructure, and lengthy refueling times. This paper addresses a green vehicle routing problem of homogeneous service fleets with stated challenges, and develops an Iterated Beam Search algorithm for its solution. The developed algorithm can employ different lower and upper bounding strategies, and can work as an exact or a heuristics algorithm. A dominance strategy is also developed to improve efficiency of the algorithm. Computational experiments on various-size test instances show that the algorithm's performance is comparable to existing approaches.

show abstract

Section: Green Vehicle Routingmentioning

confidence: 99%

Section: Notationmentioning

confidence: 99%

An iterated beam search algorithm for the green vehicle routing problem

Yavuz

2017

Networks

View full text Add to dashboard Cite

show abstract

“…In principle, any exact or heuristic method suitable for solving the G-VRP can be implemented in our solution approach. In this paper, we apply the G-VRP heuristic developed by Montoya et al (2016) to construct vehicle routes. In contrast to the heuristic proposed in Montoya et al (2016), we consider a setting where a transportation service provider has not invested in an alternative fuel installation onsite of its depot-and hence has to rely fully upon the public alternative refueling infrastructure.…”

Section: Solution Approachmentioning

confidence: 99%

“…Those difficulties mainly arise from the shorter driving ranges of AFV in conjunction with a limited number of AFS (Erdogan and Miller-Hooks 2012;Juan et al 2014;Montoya et al 2016). Hence, G-VRP solution methods seek to efficiently incorporate stops at AFS to avoid the risk of running out of fuel.…”

Section: Introductionmentioning

confidence: 99%

A solution approach for deriving alternative fuel station infrastructure requirements

et al. 2017

View full text Add to dashboard Cite

When an alternative fuel is introduced, the infrastructure through which that fuel is made available to the market is often underdeveloped. Transportation service providers relying on such infrastructures are unlikely to adopt alternative fuel vehicles as it may impose long detours for refueling. In this paper, we design and apply a new solution approach to derive minimum infrastructure requirements, in terms of the number of alternative fuel stations. The effectiveness of our approach is demonstrated by applying it to the case of introducing liquefied natural gas (LNG) as a transportation fuel in The Netherlands. From this case, we learn that, depending on the driving range of the LNG trucks and the size of area on which those trucks operate, a minimum of 5-12 LNG fuel stations is necessary to render LNG trucks economically and environmentally beneficial.

show abstract

“…A multi-space sampling heuristic (MSH) for the GVRP was developed recently by Montoya et al (2016). The algorithm alternates between two phases: sampling and assembling.…”

Section: Introductionmentioning

confidence: 99%

Variable neighborhood search algorithm for the green vehicle routing problem

Affi¹,

Derbel²,

Jarboui³

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

This article discusses the ecological vehicle routing problem with a stop at a refueling station titled Green-Vehicle Routing Problem. In this problem, the refueling stations and the limit of fuel tank capacity are considered for the construction of a tour. We propose a variable neighborhood search to solve the problem. We tested and compared the performance of our algorithm intensively on datasets existing in the literature.

show abstract

A multi-space sampling heuristic for the green vehicle routing problem

Cited by 168 publications

References 18 publications

An iterated beam search algorithm for the green vehicle routing problem

An iterated beam search algorithm for the green vehicle routing problem

A solution approach for deriving alternative fuel station infrastructure requirements

Variable neighborhood search algorithm for the green vehicle routing problem

Contact Info

Product

Resources

About