Exact solutions to a carsharing pricing and relocation problem under uncertainty

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Exact solutions to a carsharing pricing and relocation problem under uncertainty. / Pantuso, Giovanni.

I: Computers and Operations Research, Bind 144, 105802, 2022, s. 1-23.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Pantuso, G 2022, 'Exact solutions to a carsharing pricing and relocation problem under uncertainty', Computers and Operations Research, bind 144, 105802, s. 1-23. https://doi.org/10.1016/j.cor.2022.105802

APA

Pantuso, G. (2022). Exact solutions to a carsharing pricing and relocation problem under uncertainty. Computers and Operations Research, 144, 1-23. [105802]. https://doi.org/10.1016/j.cor.2022.105802

Vancouver

Pantuso G. Exact solutions to a carsharing pricing and relocation problem under uncertainty. Computers and Operations Research. 2022;144:1-23. 105802. https://doi.org/10.1016/j.cor.2022.105802

Author

Pantuso, Giovanni. / Exact solutions to a carsharing pricing and relocation problem under uncertainty. I: Computers and Operations Research. 2022 ; Bind 144. s. 1-23.

Bibtex

@article{0e386854c3a347d8a5c1e8549caafaa1,
title = "Exact solutions to a carsharing pricing and relocation problem under uncertainty",
abstract = "In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in customer preferences, the problem is formulated as a mixed-integer two-stage stochastic program with integer decision variables at both stages. We propose an exact solution method for the problem based on the integer L-Shaped method which exploits an efficient exact algorithm for the solution of the subproblems. Tests on artificial instances based on the city of Milan illustrate that the method can solve, or find good solutions to, moderately sized instances for which a commercial solver fails. Furthermore, our results suggest that, by adjusting prices between different zones of the city, the operator can attract significantly more demand than with a fixed pricing scheme and that such a pricing scheme, coupled with a sufficiently large fleet, significantly reduces the relevance of staff-based relocations. A number of issues, that remain to be addressed in future research, are pointed out in our conclusions.",
keywords = "Carsharing, l-shaped method, Stochastic Programming",
author = "Giovanni Pantuso",
note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
doi = "10.1016/j.cor.2022.105802",
language = "English",
volume = "144",
pages = "1--23",
journal = "Computers & Operations Research",
issn = "0305-0548",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Exact solutions to a carsharing pricing and relocation problem under uncertainty

AU - Pantuso, Giovanni

N1 - Publisher Copyright: © 2022 The Author(s)

PY - 2022

Y1 - 2022

N2 - In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in customer preferences, the problem is formulated as a mixed-integer two-stage stochastic program with integer decision variables at both stages. We propose an exact solution method for the problem based on the integer L-Shaped method which exploits an efficient exact algorithm for the solution of the subproblems. Tests on artificial instances based on the city of Milan illustrate that the method can solve, or find good solutions to, moderately sized instances for which a commercial solver fails. Furthermore, our results suggest that, by adjusting prices between different zones of the city, the operator can attract significantly more demand than with a fixed pricing scheme and that such a pricing scheme, coupled with a sufficiently large fleet, significantly reduces the relevance of staff-based relocations. A number of issues, that remain to be addressed in future research, are pointed out in our conclusions.

AB - In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in customer preferences, the problem is formulated as a mixed-integer two-stage stochastic program with integer decision variables at both stages. We propose an exact solution method for the problem based on the integer L-Shaped method which exploits an efficient exact algorithm for the solution of the subproblems. Tests on artificial instances based on the city of Milan illustrate that the method can solve, or find good solutions to, moderately sized instances for which a commercial solver fails. Furthermore, our results suggest that, by adjusting prices between different zones of the city, the operator can attract significantly more demand than with a fixed pricing scheme and that such a pricing scheme, coupled with a sufficiently large fleet, significantly reduces the relevance of staff-based relocations. A number of issues, that remain to be addressed in future research, are pointed out in our conclusions.

KW - Carsharing

KW - l-shaped method

KW - Stochastic Programming

UR - http://www.scopus.com/inward/record.url?scp=85128228256&partnerID=8YFLogxK

U2 - 10.1016/j.cor.2022.105802

DO - 10.1016/j.cor.2022.105802

M3 - Journal article

AN - SCOPUS:85128228256

VL - 144

SP - 1

EP - 23

JO - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

M1 - 105802

ER -

ID: 308904169