17 research outputs found
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters and respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than and respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that \lpalg under some choice of can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit
A Unified Model for the Two-stage Offline-then-Online Resource Allocation
With the popularity of the Internet, traditional offline resource allocation
has evolved into a new form, called online resource allocation. It features the
online arrivals of agents in the system and the real-time decision-making
requirement upon the arrival of each online agent. Both offline and online
resource allocation have wide applications in various real-world matching
markets ranging from ridesharing to crowdsourcing. There are some emerging
applications such as rebalancing in bike sharing and trip-vehicle dispatching
in ridesharing, which involve a two-stage resource allocation process. The
process consists of an offline phase and another sequential online phase, and
both phases compete for the same set of resources. In this paper, we propose a
unified model which incorporates both offline and online resource allocation
into a single framework. Our model assumes non-uniform and known arrival
distributions for online agents in the second online phase, which can be
learned from historical data. We propose a parameterized linear programming
(LP)-based algorithm, which is shown to be at most a constant factor of
from the optimal. Experimental results on the real dataset show that our
LP-based approaches outperform the LP-agnostic heuristics in terms of
robustness and effectiveness.Comment: Accepted by IJCAI 2020
(http://static.ijcai.org/2020-accepted_papers.html) and SOLE copyright holder
is IJCAI (International Joint Conferences on Artificial Intelligence), all
rights reserve
Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates
We study a dynamic non-bipartite matching problem. There is a fixed set of
agent types, and agents of a given type arrive and depart according to
type-specific Poisson processes. Agent departures are not announced in advance.
The value of a match is determined by the types of the matched agents. We
present an online algorithm that is (1/8)-competitive with respect to the value
of the optimal-in-hindsight policy, for arbitrary weighted graphs. Our
algorithm treats agents heterogeneously, interpolating between immediate and
delayed matching in order to thicken the market while still matching valuable
agents opportunistically