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Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem

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Title Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem
 
Creator V, Praveen
P, Keerthika
 
Subject Grey wolf optimization
Linear measure
Multi-constraint problem
Particle swarm optimization
Routing distance
 
Description 651-662
Vehicle Routing Problem (VRP) like total routing distance, number of serve provisioning vehicles, and vehicles'
waiting time are determined as the multi-objective constraints. Investigators pretend to handle these multi-constraint issues
with the time window and fail to attain a prominent solution. Thus, there is a need for a global multi-objective vehicle
routing solution. Here, a novel Particle Positioning Particle Swarm Optimization (𝑃􀬷𝑆𝑂) approach is designed to predict the
robust route with the elimination of non-linearity measures. The linearity measure includes the movement of the vehicles,
service time, and status of the move towards a particular direction. The lack of exploration and exploitation conditions
during optimization is addressed with the inclusion of Grey Wolf Optimization (GWO). Therefore, the models attain a
global solution with the least error rate. Simulation is done in MATLAB 2016b environment, and the experimental
outcomes are compared with various approaches in large-scale and small-scale instances. The model intends to attain
robustness and stability towards the measure in a linear manner. The model's time consumption and computational
complexity are reduced with the adoption of a global routing-based optimization approach.
 
Date 2022-06-06T05:31:18Z
2022-06-06T05:31:18Z
2022-06
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/59853
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(06) [June 2022]