Routing vehicles to minimize fuel consumption

Gaur, Daya Ram; Mudgal, Apurva; Singh, Rishi Ranjan

doi:10.1016/j.orl.2013.07.007

Cited by 58 publications

(27 citation statements)

References 13 publications

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“…A constant-factor approximation algorithm was developed in Gaur, Mudgal, and Singh (2013), and local search algorithms and metaheuristics (Kramer et al 2015;Zachariadis, Tarantilis, and Kiranoudis 2015) were also applied to solve the EMVRP.…”

Section: Introductionmentioning

confidence: 99%

A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem

Fukasawa

Song

2016

Transportation Science

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W e study a variant of the capacitated vehicle routing problem where the cost over each arc is defined as the product of the arc length and the weight of the vehicle when it traverses that arc. We propose two new mixed-integer linear programming formulations for the problem: an arc-load formulation and a set partitioning formulation based on q-routes with additional constraints. A family of cycle elimination constraints are derived for the arc-load formulation. We then compare the linear programming (LP) relaxations of these formulations with the two-index one-commodity flow formulation proposed in the literature. In particular, we show that the arc-load formulation with the new cycle elimination constraints gives the same LP bound as the set partitioning formulation based on 2-cycle-free q-routes, which is stronger than the LP bound given by the two-index one-commodity flow formulation. We propose a branch-and-cut algorithm for the arc-load formulation, and a branch-cut-and-price algorithm for the set partitioning formulation strengthened by additional constraints. Computational results on instances from the literature demonstrate that a significant improvement can be achieved by the branch-cut-and-price algorithm over other methods.

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Section: Introductionmentioning

confidence: 99%

A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem

Fukasawa

Song

2016

Transportation Science

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“…Fuel consumption has traditionally been calculated as a function of the power requirements needed to overcome aerodynamic resistance, rolling resistance, gravitational resistance, and gravitational potential energy [17]. Methods for constructing routes of minimal fuel consumption often consider that fuel consumption varies linearly with a vehicle's weight per unit distance [7], [27]. A study of eco-routing algorithms quantifying the impact of implementing a system in Ohio used fuel consumption to construct optimal routes [9].…”

Section: Vehicle Fuel Economymentioning

confidence: 99%

The Development of a Smart Map for Minimum "Exertion" Routing Applications

Payne

Dror

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Kwon et al [28] developed a Heterogeneous fixed fleet VRP model that considers the carbon emissions trade cost in the objective function. Gaur et al [19] studied the cumulative VRPs with the objective to minimize fuel consumption and proposed an approximation algorithm for cumulative VRPs when vehicles have finite capacity and an arbitrary number of depot offloads are allowed. Demir et al [10] proposed an adaptive large neighborhood search algorithm (ALNS) to minimize the fuel consumption and the driving time with Pareto optimality.…”

Section: Q2mentioning

confidence: 99%