Capacitated vehicle routing problem

Carwalo, Tejal; Thankappan, Jerin; Patil, Vandana

doi:10.1109/cscita.2017.8066555

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen, Yang and Wu (2006) created a hybrid of Discrete Particle Swarm Optimization (DPSO) and Simulated Annealing (SA) to obtain acceptable solutions to large scale CVRPs successfully. Carwalo, Thankappan and Patil (2017) developed a partition clustering method followed by Ant Colony Optimization (ACO) to deal with CVRP efficiently. Ruiz et al (2019) proposed using a biased random key GA to solve open CVRP with distance constraints.…”

Section: Cvrpmentioning

confidence: 99%

Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags

Gupta

Govindan

Mehlawat

et al. 2021

International Journal of Production Research

View full text Add to dashboard Cite

Real-life challenges require proactive measures. Good transportation services and greener alternatives demand steadfast research in the area. Here, an attempt has been made to address such a problem. A particular case of VRP, with deliveries split into bags and triangular fuzzy travel times, has been modelled to minimize fuel emissions. The concepts of fuzzy rule-based implication for ranking and for comparing fuzzy numbers with numeric values, an expected value model, have been drawn upon. A discrete fuzzyhybridized GA has been developed. Multiple experiments on data in existing works, parameter tuning, and comparative analysis have been performed, thereby corroborating the model's efficacy.

show abstract

Section: Cvrpmentioning

confidence: 99%

Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags

Gupta

Govindan

Mehlawat

et al. 2021

International Journal of Production Research

View full text Add to dashboard Cite

show abstract

“…In the Precedence Constrained TSP (TSP-PC), an additional constraint is that a city A should not be visited unless the city B was visited first-see [14]. We can also mention the Capacited Vehicle Routing Problem (CVRP) where the vehicle has limited carrying capacity, requiring to go back to the depot when the vehicle is full-see [15]. Another variant is that the cities are not represented by points, i.e., an exact location, but by areas with various shapes called neighborhoods-see [16].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

et al. 2023

View full text Add to dashboard Cite

Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function as the minimal distance, time or energy. However, in some applications, as the one addressed in this paper—namely the data collection of buried sensor nodes by means of an Unmanned Aerial Vehicle— the targets are areas with varying sizes: they are defined with respect to the radio communication range of each node, ranging from a few meters to several hundred meters according to various parameters (e.g., soil moisture, burial depth, transmit power). The Unmanned Aerial Vehicle has to enter successively in these dynamic areas to collect the data, without the need to pass at the vertical of each node. Some areas can obviously intersect. That leads to solve the Close Enough TSP. To determine a sub-optimal trajectory for the Unmanned Aerial Vehicle, this paper presents an original and efficient strategy based on an evolutionary algorithm completed with geometrical heuristics. The performances of the algorithm are highlighted through scenarios with respectively 15 and 50 target locations. The results are analyzed with respect to the total route length. Finally, conclusions and future research directions are discussed.

show abstract

“…Nevertheless, there are many research articles that report their performances in dealing with CVRP [23][24][25][26][27]. It is worth mentioning that swarm optimization techniques are getting popular due to their simplicity and performance to handle optimization problems nowadays [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Ahmed

Sun

2018

Algorithms

View full text Add to dashboard Cite

Abstract:The classical capacitated vehicle routing problem (CVRP) is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO) along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches.

show abstract

Capacitated vehicle routing problem

Cited by 4 publications

References 10 publications

Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags

Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags

Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Contact Info

Product

Resources

About