An efficient bundle-based approach for the share-a-ride problem

TL;DR

This paper introduces a bundle-based mixed-integer linear programming approach for the share-a-ride problem in ride-hailing, improving solution quality and reducing deadheading distance compared to existing methods.

Contribution

It proposes a novel bundle-based MILP formulation for SARP, outperforming previous freight insertion methods in solution quality and efficiency.

Findings

Outperforms FIP methods in solution quality

Reduces deadheading distance effectively

Handles multi-depot, time window constraints

Abstract

Some of today's most significant challenges in urban environments concern individual mobility and rapid parcel delivery. With the surge of e-commerce and the ever-increasing volume of goods to be handled, new logistic solutions are in high demand. The share-a-ride problem (SARP) was proposed as one such solution, combining people and parcel transportation in taxis. This is an NP-hard problem and thus obtaining optimal solutions can be computationally costly. In this paper, we work with a variation of SARP for ride-hailing systems, which can be formulated as a multi-depot open generalised vehicle routing problem with time windows. We present and solve a mixed-integer linear programming (MILP) formulation for this problem that bundles requests together, and we compare its results to a previously proposed two-stage method. The latter solves the so-called freight insertion problem (FIP) in the second stage, for which we consider two versions, and the problem consists of inserting parcels into predefined passenger routes obtained in the first stage. We tested the methods in three sets of instances. The developed bundle-based approach outperformed both FIP versions in solution quality and in the service of parcels. Our method also compares favourably when it comes to reducing the amount of deadheading distance.