Fleet size problem for one-way electric carsharing services considering customers’ waiting tolerance and waiting stress

•Electric carsharing service optimization considering customers’ waiting stress.•A mixed-integer nonlinear programming model formulated.•Efficient outer-approximation algorithm proposed to obtain ε-optimal solution.•Numerical experiments on a real-world network of EVCARD conducted. Service operation...

Full description

Saved in:
Bibliographic Details
Published in:Computers & industrial engineering Vol. 200; p. 110784
Main Authors: Wu, Ting, Xu, Min, Eltoukhy, Abdelrahman E.E.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2025
Subjects:
Electric carsharing
Outer-approximation algorithm
Vehicle relocation
Waiting stress
Waiting tolerance
Waiting stress
Outer-approximation algorithm
Electric carsharing
Waiting tolerance
Vehicle relocation
ISSN:0360-8352
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Electric carsharing service optimization considering customers’ waiting stress.•A mixed-integer nonlinear programming model formulated.•Efficient outer-approximation algorithm proposed to obtain ε-optimal solution.•Numerical experiments on a real-world network of EVCARD conducted. Service operation problems arising from electric carsharing services have been the research subject of many scholars in the past few years. Previous studies did not consider customers’ psychological waiting stress in the decision-making of electric carsharing services. This study addresses a fleet size problem for one-way electric carsharing services while considering vehicle relocation, vehicle charging, and customers’ waiting tolerance as well as psychological waiting stress. A mixed-integer nonlinear programming (MINLP) model is first developed for the problem. By exploring the model convexity, we put forward an effective outer-approximation algorithm such that the ε-optimal solution can be obtained. Numerical experiments are conducted to demonstrate the efficacy of the proposed model and solution method. We also analyze how the consideration of customers’ waiting tolerance and waiting stress influences the fleet size, system profitability, and service level.
ISSN:0360-8352
DOI:10.1016/j.cie.2024.110784