Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem
NOPR - NISCAIR Online Periodicals Repository
View Archive InfoField | Value | |
Title |
Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem
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Creator |
V, Praveen
P, Keerthika |
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Subject |
Grey wolf optimization
Linear measure Multi-constraint problem Particle swarm optimization Routing distance |
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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 (𝑃𝑆𝑂) 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. |
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Date |
2022-06-06T05:31:18Z
2022-06-06T05:31:18Z 2022-06 |
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Type |
Article
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Identifier |
0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/59853 |
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Language |
en
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Publisher |
NIScPR-CSIR, India
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Source |
JSIR Vol.81(06) [June 2022]
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