A polynomial-time algorithm for user-based relocation in free-floating car sharing systems

•We present a polynomial algorithm for user-based relocation and vehicle dispatching.•We solve instances with 100,000 requests and 10,000 vehicles in less than 4 min.•We show that spatial relocation outperforms temporal relocation.•We analyze the impact of discount strategies on user comfort. Free-f...

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Bibliographic Details
Published in:Transportation research. Part B: methodological Vol. 143; pp. 65 - 85
Main Authors: Schiffer, Maximilian, Hiermann, Gerhard, Rüdel, Fabian, Walther, Grit
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.01.2021
Elsevier Science Ltd
Subjects:
Algorithms
Automobiles
Car sharing
Free-floating car sharing
Graph representations
Graphical representations
Optimization
Polynomial algorithm
Polynomials
Relocation
Shortest-path problems
Traffic volume
Upper bounds
User-based relocation
Polynomial algorithm
Free-floating car sharing
User-based relocation
ISSN:0191-2615, 1879-2367
Online Access:Get full text
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Summary:•We present a polynomial algorithm for user-based relocation and vehicle dispatching.•We solve instances with 100,000 requests and 10,000 vehicles in less than 4 min.•We show that spatial relocation outperforms temporal relocation.•We analyze the impact of discount strategies on user comfort. Free-floating car sharing (FFCS) systems are a promising concept to reduce the traffic volume in cities. However, spatial and temporal mismatches of supply and demand require a relocation of rental cars in order to avoid low degrees of utilization. Here, especially user-based relocation strategies seem to be promising to increase utilization in a cost-efficient manner. However, a thorough optimization-based assessment of user-based relocation strategies for FFCS systems is still missing. In this paper, we introduce an integer program that optimizes the assignment of user-based relocation strategies in FFCS fleets. We develop a graph representation that allows to reformulate the problem as a k-disjoint shortest paths problem and propose an exact algorithm to solve large-size instances. We show that this algorithm can solve real-world instances within a few milliseconds as well as instances with up to 100,000 customers and 10,000 vehicles in a few minutes. Furthermore, we present a case study based on real-world data and derive managerial insights on user-based relocation strategies. Our results reveal an upper bound on the benefit of user-based relocation strategies and demonstrate that the employment of such strategies can increase the number of fulfilled rental requests by 21%, while increasing the operator’s revenue by 10%.
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ISSN:0191-2615
1879-2367
DOI:10.1016/j.trb.2020.11.001