Approximate Ridesharing of Personal Vehicles Problem

TL;DR

This paper analyzes the computational complexity of approximate ridesharing problems, proving NP-hardness under various conditions and providing approximation algorithms for specific cases.

Contribution

It establishes the NP-hardness of key ridesharing optimization problems and introduces approximation algorithms under certain constraints.

Findings

Both minimization problems are NP-hard.

Approximation within a constant factor is NP-hard without certain conditions.

Provides a $rac{K+2}{2}$-approximation algorithm for specific cases.

Abstract

The ridesharing problem is that given a set of trips, each trip consists of an individual, a vehicle of the individual and some requirements, select a subset of trips and use the vehicles of selected trips to deliver all individuals to their destinations satisfying the requirements. Requirements of trips are specified by parameters including source, destination, vehicle capacity, preferred paths of a driver, detour distance and number of stops a driver is willing to make, and time constraints. We analyze the relations between the time complexity and parameters for two optimization problems: minimizing the number of selected vehicles and minimizing total travel distance of the vehicles. We consider the following conditions: (1) all trips have the same source or same destination, (2) no detour is allowed, (3) each participant has one preferred path, (4) no limit on the number of stops, and (5) all trips have the same departure and same arrival time. It is known that both minimization problems are NP-hard if one of Conditions (1), (2) and (3) is not satisfied. We prove that both problems are NP-hard and further show that it is NP-hard to approximate both problems within a constant factor if Conditions (4) or (5) is not satisfied. We give $\frac{K+2}{2}$-approximation algorithms for minimizing the number of selected vehicles when condition (4) is not satisfied, where $K$ is the largest capacity of all vehicles.