Optimization models for fair horizontal collaboration in demand-responsive transportation

TL;DR

This paper develops mixed integer programming models and heuristics to optimize route collaboration among demand-responsive taxi companies, balancing workload and reducing costs and environmental impact.

Contribution

It introduces novel optimization models with workload balancing constraints for horizontal collaboration in demand-responsive transportation.

Findings

Collaboration reduces operational costs and environmental impact.

Heuristic algorithms effectively implement the models.

Workload balancing constraints improve fairness among companies.

Abstract

The advances in information and communication technology are changing theway people move. Companies that offer demand-responsive transportation serviceshave the opportunity to reduce their costs and increase their revenues throughcollaboration, while at the same time reducing the environmental impact of theiroperations. We consider the case of companies, offering a shared taxi service, thatare involved in horizontal collaboration and present mixed integer programmingmodels for the optimization of their routes that embed constraints aimed at bal-ancing the workload exchange. These constraints bound the imbalance in terms oftraveled time and/or served customers to be less than thresholds agreed in advanceby the companies. We also present a heuristic algorithm and show the benefits ofthe collaboration.