New Shades of the Vehicle Routing Problem: Emerging Problem Formulations and Computational Intelligence Solution Methods

Mańdziuk, Jacek

doi:10.1109/tetci.2018.2886585

Cited by 44 publications

(22 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 presents a hierarchy of the VRP variants. Among them, the basic version of the VRP is the Capacitated Vehicle Routing Problem (CVRP) [17,18]. Capacity constraint of the CVRP refers to the total demand of a vehicle should not exceed capacity limits and each node's demand served by n same vehicles [19].…”

Section: A Development Process and Mathematical Model Of The Vrpmentioning

confidence: 99%

“…The applications and solutions of Dynamic Vehicle Routing Problem(DVRP) are reviewed by Pillac et al [29] in the same year. Later, Mańdziuk [17] reviewed the high quality papers on solving VRP published between 2015 and 2017, and summarized intelligent optimization algorithms. Adewumi and Adeleke [30] emphasized some key features and recent advances of four VRP variants in 2018, while Dixit et al [31] reviewed some of the recent advancements of meta-heuristic techniques in the VRP with Time Windows (VRPTW) in the next year.…”

Section: B Current Research Of the Vrpmentioning

confidence: 99%

See 1 more Smart Citation

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

Li¹,

Wu²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: A Development Process and Mathematical Model Of The Vrpmentioning

confidence: 99%

Section: B Current Research Of the Vrpmentioning

confidence: 99%

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

Li¹,

Wu²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Capacitated vehicle routing problem (CVRP) [1], which is well known and has high research value in the logistics field, was proposed first time by Dantzig in 1956. Due to the exact solution of the algorithm based on strict mathematical description that cannot satisfy the performance requirement, in the past few decades, scholars from home and abroad have carried out extensive and profound research for this problem, and some heuristic algorithms were proposed to solve CVRP problem [2][3][4][5][6]. In [7], the authors analyzed and compared the performance of multiple heuristic algorithms under the CVRP conditions, according to the genetic algorithm characteristics of simple and high scalability, which can search information based on the objective function, can provide better solutions.…”

Section: Introductionmentioning

confidence: 99%

Solving Capacitated Vehicle Routing Problem by an Improved Genetic Algorithm with Fuzzy C-Means Clustering

Zhu¹

2022

Scientific Programming

View full text Add to dashboard Cite

Aiming at solving the vehicle routing problem, an improved genetic algorithm based on fuzzy C-means clustering (FCM) is proposed to solve the vehicle routing problem with capacity constraints. On the basis of genetic algorithm, the FCM algorithm is used to decompose the large-scale vehicle routing optimization problem into small-scale subproblems, which can effectively improve the efficiency of the algorithm. At the same time, a generation method of the initial solution to CVRP problem is designed. The improved algorithm has good robustness and can also reduce the possibility of falling into local optimization in the search process. Finally, a simulation example is provided to verify the efficiency and superiority of the proposed algorithm.

show abstract

“…Game theory considers the predicted and actual behaviors of individuals in the game and studies their optimization strategies. It is used in traditional traffic revenue distribution [ 11 , 12 , 13 ], and has made achievements in emerging research areas such as internet of vehicles control strategies [ 14 ], path optimization [ 15 ], and system evaluation [ 16 ]. Some studies have also accomplished certain achievements by using game theory in the field of low-carbon transportation [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Chaos Analysis of Urban Low-Carbon Traffic Based on Game Theory

Ren

2021

IJERPH

View full text Add to dashboard Cite

Developing urban low-carbon traffic is an effective measure to reduce traffic carbon emissions, which are important parts of greenhouse gas. In order to understand the development characteristics and regular patterns of urban low-carbon traffic, we present a game model that enables us to predict the possible range of travel mode choice and the impact of low-carbon awareness. Through chaos analysis and simulation of the model, the authors come to realize that the proportions of travel mode choice can reach an equilibrium under a certain urban traffic system. This equilibrium is related to low-carbon awareness and the situation of the urban traffic system. The research we have done suggests that in small cities with undeveloped traffic systems, the most effective measure to achieve urban low-carbon traffic is to increase the comprehensive costs of high-carbon travel. However, in big cities with developed traffic systems, raising low-carbon awareness of residents can greatly increase the proportion of low-carbon travelers and improve the stability of travel mode choice. The results could provide development strategies and policy suggestions for urban low-carbon traffic and reduce the adverse impact of urban traffic emissions on public health.

show abstract

New Shades of the Vehicle Routing Problem: Emerging Problem Formulations and Computational Intelligence Solution Methods

Cited by 44 publications

References 129 publications

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

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

Solving Capacitated Vehicle Routing Problem by an Improved Genetic Algorithm with Fuzzy C-Means Clustering

Chaos Analysis of Urban Low-Carbon Traffic Based on Game Theory

Contact Info

Product

Resources

About