A multi-objective optimization framework for on-line ridesharing systems

TL;DR

This paper introduces a biogeography-based optimization algorithm for online ridesharing, effectively balancing multiple objectives like route similarity and earnings, and demonstrates its competitive performance on real-world data.

Contribution

It presents a novel multi-objective optimization framework using biogeography-based optimization specifically for online ridesharing systems.

Findings

Competitive performance on Beijing dataset

Effective multi-objective balancing

Outperforms some existing algorithms

Abstract

The ultimate goal of ridesharing systems is to matchtravelers who do not have a vehicle with those travelers whowant to share their vehicle. A good match can be found amongthose who have similar itineraries and time schedules. In thisway each rider can be served without any delay and also eachdriver can earn as much as possible without having too muchdeviation from their original route. We propose an algorithmthat leverages biogeography-based optimization to solve a multi-objective optimization problem for online ridesharing. It isnecessary to solve the ridesharing problem as a multi-objectiveproblem since there are some important objectives that must beconsidered simultaneously. We test our algorithm by evaluatingperformance on the Beijing ridesharing dataset. The simulationresults indicate that BBO provides competitive performancerelative to state-of-the-art ridesharing optimization algorithms.