Modeling and optimization for carsharing services: A literature review

Wu, Ting; Xu, Min

doi:10.1016/j.multra.2022.100028

Cited by 21 publications

(6 citation statements)

References 78 publications

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“…In the realm of maritime transportation, the pursuit of attaining the optimal trajectory, sailing speed, fuel consumption, and other pertinent optimal decisions constitutes the focus in cost minimization. Within this context, the exploration of optimal decisions has been undertaken to curtail operational expenses [22][23][24][25].…”

Section: The Optimal Decisions In Shippingmentioning

confidence: 99%

Optimal Ship Deployment and Sailing Speed under Alternative Fuels

Wang,

Liu,

Wang

et al. 2023

JMSE

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The European Union (EU) has implemented a sub-quota of 2% for renewable marine fuels to be utilized by vessels operating within its jurisdiction, effective starting from 2034. This progressive policy signifies a significant leap towards reducing carbon emissions and promoting sustainable development. However, it also presents notable challenges for shipping companies, particularly in terms of fuel costs. In order to support shipping companies in devising optimal strategies within the framework of this new policy, this study proposes a mixed-integer linear programming model. This model aims to determine the optimal decisions for fuel choice, sailing speed and the number of vessels on various routes. Furthermore, we showcase the adaptability of our model in response to fluctuations in fuel prices, relevant vessel costs, and the total fleet size of vessels. Through its innovative insights, this research provides invaluable guidance for optimal decision-making processes within shipping companies operating under the new EU policy, enabling them to minimize their total costs effectively.

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Section: The Optimal Decisions In Shippingmentioning

confidence: 99%

Optimal Ship Deployment and Sailing Speed under Alternative Fuels

Wang,

Liu,

Wang

et al. 2023

JMSE

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“…The problem of linear programming for maximizing the profits of the carsharing system is described in [13]. An overview of scientific studies on modeling and optimization for the carsharing system is described in [14], where the authors classify the literature into three categories: strategic, tactical, and operational, according to the level of decisions made in optimization tasks. Optimization models and solution methods proposed in existing studies are considered for each category.…”

Section: Literature Reviewmentioning

confidence: 99%

A Dynamic Model of Profit Maximization for Carsharing Services: Astana, Republic of Kazakhstan

et al. 2023

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This study considers building a dynamic model of profit maximization for a carsharing system and its verification based on the case of implementing such a system in Astana, Republic of Kazakhstan. The region, bounded by the administrative boundaries of Astana, was divided into subregions that covered the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of the trips to the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific subregion, for which the quantitative parameters necessary for solving the profit maximization problem in the carsharing system are calculated. Stations with cars are placed in the cells of the grid, which are available to carsharing service customers. Based on the collected data, dynamic (four periods per day) and static profit maximization models in the carsharing system were built. Modeling was carried out based on the built models in the case of Astana. It was established that using a dynamic profit maximization model in the carsharing system increases profit by 3.7%. The obtained results are important for the development of the infrastructure of the capital of Kazakhstan and for finding a solution to the problems of urban science in this region.

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“…Existing literature on the modeling of car-sharing has covered free-floating and peer-to-peer schemes as shown in [6]. A fleet size optimization for station based round-trip and oneway sharing is proposed using MILP in [7].…”

Section: Introductionmentioning

confidence: 99%

Consideration of round-trip electric car-sharing in residential energy system modeling

Mohapatra,

Achatz,

Hamacher

2024

Preprint

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This paper presents a residential, round-trip, mixed ownership, electric car-sharing scheme with an optimized charging schedule, expressed as a mixed integer linear programming model. Representing the given travel demand as a graph, a mixed (shared and owned cars) ownership fleet configuration, with different trip fulfillment possibilities, is determined using maximal cliques. Next, a car-to-trip allocation model for feasible trip plans, with considerations for equal car utilization and charging price, is used to generate charging profiles for the fleet. The model and the outputs can be variously integrated into existing energy system modeling frameworks. A case study, using data from a test building with a residential car-sharing fleet in Munich, demonstrates the potential of the proposed models to derive meaningful operational insights for fleet and energy system planners. 92% of the recorded trips at the test building could have been feasibly accomplished with only 60% of the available cars.

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Modeling and optimization for carsharing services: A literature review

Cited by 21 publications

References 78 publications

Optimal Ship Deployment and Sailing Speed under Alternative Fuels

Optimal Ship Deployment and Sailing Speed under Alternative Fuels

A Dynamic Model of Profit Maximization for Carsharing Services: Astana, Republic of Kazakhstan

Consideration of round-trip electric car-sharing in residential energy system modeling

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