Fabian Torres

Department of Mathematics and Industrial Engineering at Polytechnique Montreal

January 03, 2022, 16:00

Crowd-shipping: Determining the compensation of crowd-drivers with stochastic route acceptance

E-commerce continues to grow all over the world. The recent pandemic caused by COVID-19 has increased this trend. Concurrently, crowd-shipping is emerging as a viable solution to fulfill last-mile deliveries, with AmazonFlex taking the lead in implementing such distribution models. We look at a problem of crowd-shipping were a crowd-shipping platform must fulfill delivery requests from a central depot with a fleet of professional vehicles and a pool of crowd-drivers. The latter can accept or reject routes based on their preference. The probability of route acceptance is dependent on the set of routes that are offered to crowd-drivers. The best compensated route is the most likely to be accepted. We develop a large neighborhood search heuristic to solve this routing problem. To investigate the practical viability of such distribution models, we show the market equilibrium when no fluctuation in supply is considered, versus the market equilibrium when the stochastic supply of crowd-drivers is considered. The best compensation for crowd-drivers that minimizes the total expected cost of the routing problem is determined. We show in our numerical experiments that a 6% cost reduction can be achieved by adjusting the compensation when we consider stochastic route acceptance.


Fabian Torres is a Ph.D. student in the Department of Mathematics and Industrial Engineering at Polytechnique Montreal. He received his B.S. in Chemical Engineering at the Catholic University of Cuenca and an MBA from the University of Azuay, Ecuador. His research interests include stochastic programming, integer programming, dynamic programming, and combinatorial optimization, with applications in transportation and crowd-shipping.