A MULTIOBJECTIVE OPTIMIZATION TECHNIQUE FOR CARPOOLING USING GENETIC ALGORITHM ON ANDRIOD

Prof. Laxmi Thakare, Anjali Birajdar, Sayali Kaduskar, Dhanashree Kapoor, Kshitija Kashid

Abstract


Carpooling is means of vehicle sharing in which driver share their car with one or more riders. It increases occupancy rate of cars by decreasing number of empty seats. This project proposes an multiobjective optimization carpooling system.

For an organization, sometimes it may be challenging to solve parking problem as huge number of employees working for organization having their own cars. To solve that parking congestion issue MOCS categorized employee into two level namely as Driver and Seeker. Both having their own constraints. MOCS uses Genetic Algorithm (GA). The genetic algorithm is a process used to solve both constrained and unconstrained optimization problems that is based on natural selection methods, the process that drives biological evolution. GA help to match constraints of seeker with constraints of driver. So that we get best optimized  solution. 

Keywords


Optimization, Genetic Algorithm, Carpooling, Multiobjective Problem, Constraints, traffic congestion

Full Text:

PDF

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2018 INTERNATIONAL EDUCATION AND RESEARCH JOURNAL