Author
Şahin, Ahmet, Sevim, İ., Albey, Erinç, Güler, M. G.
Publication Date
2022-04
Publication Place
-
Elsevier
Subject
Binary programming, Machine learning, Nonlinear bilevel programming, Rank aggregation, Ride matching
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0305-0548
Record ID
b26547e3-5912-4632-9136-0f7e4e2c0500
Library Location
Industrial Engineering
Date
2022-04
Sample Text
This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company's current approach is considered as benchmark. Results reveal that proposed algorithm correctly predicts the first choice of riders 17% to 28% better compared to the benchmark in different scenarios. Similarly, proposed algorithm offers recommendation lists in which the preferred driver is ranked 0.38 to 1.12 person closer (to the rider's actual choice) compared to the benchmark.
DOI
10.1016/j.cor.2021.105666
Cilt
140
