Multi-Objective Optimization of Car Sharing Points Under Uncertainty for Sustainable Transportation

AYDIN N., ŞEKER Ş., Deveci M.

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, vol.71, pp.1959-1968, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 71
  • Publication Date: 2024
  • Doi Number: 10.1109/tem.2022.3171987
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Page Numbers: pp.1959-1968
  • Keywords: Automobiles, Stochastic processes, Costs, Public transportation, Mathematical models, Uncertainty, Car sharing, COVID-19, decision-dependent demand, lexicographic weighted Tchebycheff, multi-objective stochastic programming, sustainable mobility, CONSUMPTION, EXPLORATION, GOVERNANCE, LOCATION, IMPACT, NORMS
  • Yıldız Technical University Affiliated: Yes


As an important part of the sharing economy, the usage of car sharing increases world widely with the help of developments in the technology. Especially after COVID-19 the demand for private car ownership and car sharing systems increased tremendously. Therefore, its market share attracts new investors and causes existing service providers to enlarge their service area. In this article, a novel multi-objective location-dependent two-stage stochastic optimization model is proposed to determine the most appropriate locations for car sharing system and allocate the demand to these locations. The model is applied to determine the best locations among 15 candidates, and three objectives are considered, which are the minimization of total cost that comprises locating costs minus income from satisfying the demand, minimization of CO$_2$ emission occurs by the usage of car sharing system's cars and minimization of average unsatisfied demand. Both location-independent and location-dependent demands are taken into account. The proposed model delivers a more precise decision process framework for problems include stochasticity and multiobjectivity, and it easily can be implemented to any region, providing region sensitive parameters.