Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem

Velusamy, Praveen ; Keerthika, P

Abstract

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 ( ) 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.


Keyword(s)

Grey wolf optimization, Linear measure, Multi-constraint problem, Particle swarm optimization, Routing distance


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