Interactive multi-objective vehicle routing via GA-based dynamic programming

Khan, Raza Saleem; Yang, Jian‐Bo; Handl, Julia

doi:10.1109/ictis.2015.7232166

Cited by 3 publications

(2 citation statements)

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“…x i,j ∈ {0, 1}, i, j ∈ A (8) In this formulation, the objective is to minimize the total cost of transportation and c i,j represents cost of arc {i,j}. Formulas ( 2)- (3) show that each customer can be served only once and constraints (4)- (5) requires that each vehicle should start from the deport and end at the deport. Formula(6) states the update process of service amount of the vehicle, where u i represents accumulated service amount of the vehicle after it served customer i and q j indicates the demand of customer j.…”

Section: Stmentioning

confidence: 99%

“…This is mainly because the VRP has a simple basic structure [2] and broad practicality in realworld problems. The VRP variants have appeared in many fields of human life [3,4], especially in the logistic industry that is flourishing with the development of the globalization. Effective routing scheduling can save a lot of cost (generally ranging from 5% to 20%) [5], which is of great significance for improving distribution efficiency [1,[6][7][8][9] and increasing economic benefits of enterprises [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem

Li¹,

Wu²,

He³

et al. 2021

Preprint

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Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve VRP and variants. Although existing approaches has contributed a lot to the development of this field, these approaches either are limited in problem size or need manual intervening in choosing parameters. To tackle these difficulties, many studies consider learning-based optimization algorithms to solve VRP. In this paper we first review recent advances in this field and divides relevant approaches into end-toend approaches and step-by-step approaches. We design three-part experiments to justly evaluate performance of four representative learning-based optimization algorithms and verify that combining heuristic search can effectively improve generalization ability and sampled efficiency of LBO models. Finally we point out that research trend of LBO algorithms is to solve large-scale and multiple constraints problems from real world.

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Section: Stmentioning

confidence: 99%