# Optimization models for fair horizontal collaboration in demand-responsive transportation

arXiv: https://arxiv.org/abs/2105.04815 · HTML: https://mlsanity.com/paper/2105.04815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 1 of 1)

- [Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach](https://mlsanity.com/paper/2310.17485/) (2023)

## Abstract (arXiv)

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.