A Benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty

Çalık, Hatice; Fortz, Bernard

doi:10.1016/j.trb.2019.05.004

Cited by 54 publications

(19 citation statements)

References 37 publications

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“…Over the past decade, VReP has become the most commonly considered problem [11] - [18]. However, only a little literature has concentrated on strategic decisions involving location problems, as revealed by Çalık and Fortz [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, we focus on the strategic design of one-way stationbased carsharing systems from the vantage point of the system operator. The most related research (e.g., [4], [6], [7]) copes with strategic problems based on the traditional riskneutral two-stage stochastic programming by considering the expectation value as the preference criterion. However, the resulting decisions may be poor under certain realizations of random data.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

et al. 2021

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

et al. 2021

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“…The total number of stations to be laid out in the solved layout plan should not exceed the total number of alternative stations. According to Equation (15), N is the total number of proposed network points.…”

Section: (6) Other Constraintsmentioning

confidence: 99%

Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

Sai

Chai

2020

Algorithms

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The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing companies. On the basis of the current status of carsharing development, this research considers multiple influencing factors of carsharing to meet the maximum user demand. Meanwhile, the constraint of the limited cost of the company is considered to establish a nonlinear integer programming model for station location of carsharing. A genetic algorithm is designed to solve the problem by analyzing the location model of the carsharing network. Finally, the results of a case study of Lanzhou, China show the effectiveness of the establishment and solution of the station location model.Algorithms 2020, 13, 43 2 of 17 current development status and future scale of the major car long-term rental, short-term rental, and online car rental markets. These studies are biased toward policy and market analysis. Notably, carsharing has a great future in the transportation field. However, the development of the carsharing industry is still in its early stages. The problems of few stations, few available cars, and few chargers have become key restricting factors of the development of carsharing companies [10]. Some studies have found that carsharing network settings can affect user willingness and company development. Ciari [11] used a binary logistic model and showed that the location of stations actually affects potential membership. This research used elastic analysis to find the relationship between distance and number of users but did not introduce specific methods for station location optimization. Correia [12] found that financial losses can be reduced through appropriate choices with respect to the number, location, and size of the depots. This research provided the foundation for the necessity of the station location optimization model.Scholars have conducted the following research to address the problems of carsharing station location optimization. Jiang [13] used analytic hierarchy process to calculate the best scheme for carsharing stations. The study identified variables with a significant effect on station location selection but did not build an optimization model for carsharing station location determination. Lu [14] used the interval fuzzy soft set method of risk preference for each carsharing station in Wang Cheng County. The evaluation of the plan has certain reference significance for the location and future planning of carsharing. This method is more suitable for evaluating existing carsharing stations than for planning for new cities. The above-mentioned research methods are relatively subjective. Other methods using mathematical models are presented as follows. Çalık [15] illustrated a carsharing locating recharging station model that operates under demand uncertainty. The research developed a demand forecasting me...

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“…In the study of this article, we set ε as 0.01, that is, stop iteration when the relative gap between the upper and lower bounds is less than 1%. The setting and value of ε is common in other precision algorithms, such as the Benders decomposition algorithm 29,30 and the classic branch and bound algorithm. 31,32…”

Section: Solution Algorithmmentioning

confidence: 99%

Mitigating the vulnerability of an air-high-speed railway transportation network: From the perspective of predisruption response

Feng

Chen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Both the high-speed railway and air transportation network are the backbone of the interregional transport network and cover important cities in a country. Taking cities as nodes, a comprehensive interregional transportation network consisting of high-speed railways and civil aviation can be constructed. This network undertakes a huge passenger transportation task, so the failure of this network will cause serious economic losses and even casualties. In the Air-High-Speed Railway Transportation Network (A-HSRTN), the two transport modes can operate independently and can be alternatives. The analysis of the A-HSRTN helps planners to have a more comprehensive understanding of the vulnerability of the interregional passenger transport system. Mechanical failure, extreme weather and even man-made sabotage can threaten the operation of airports and stations. Optimizing the deployment of prevention resources can avoid or reduce the loss caused by those failure events in the A-HSRTN. This paper establishes a tri-level model to optimize the deployment of prevention resource from the perspective of predisruption response. This model takes the high-speed railway and air transportation system as an integrated transportation network to assign the limited prevention resources. The model aims to minimize the travel demand that cannot be satisfied in the worst failure scenario. Taking the A-HSRTN in mainland China as an example, this paper analyzes the model performance and the defense strategy obtained by this model. These case studies demonstrate that the method and model proposed in this paper can mitigate the vulnerability of the A-HSRTN.

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A Benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty

Cited by 54 publications

References 37 publications

Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

Optimizing the Strategic Decisions for One-Way Station-Based Carsharing Systems: A Mean-CVaR Approach

Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

Mitigating the vulnerability of an air-high-speed railway transportation network: From the perspective of predisruption response

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