Rebalancing stochastic demands for bike-sharing networks with multi-scenario characteristics

Ma, Guanhua; Zhang, Bowen; Shang, Changjing; Shen, Qiang

doi:10.1016/j.ins.2020.12.044

Cited by 30 publications

(7 citation statements)

References 38 publications

(39 reference statements)

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“…The common way to solve this problem is the operator uses trucks to transport bikes from surplus stations to insufficient stations, which is bike-sharing rebalancing (repositioning) problem (BRP). BRP is a NP-hard problem with different optimization objectives [6]. Most of the previous literatures have usually set a single objective, such as the total repositioning cost, the total time, and the user satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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With the rapid development of information technology, the sharing economy has developed rapidly all over the world as a new mode of distributing business profit, among which the bike-sharing system (BSS) has become popular in many cities because of its low cost, convenience, and environmental protection. The application of the 5th generation mobile communication technology (5G) in BSS makes users to search the bikes more accurately and quickly and enables operators to spot noncompliant bike sharing as soon as possible, significantly improving the efficiency of bike-sharing management. However, one of the thorny issues for operators is the bike-sharing rebalancing problem (BRP). It is the key to improve the efficiency of rebalancing, reduce the rebalancing cost, and realize the sustainable development of BSS on how to excavate the huge amount of customer cycling data, respond quickly to customer demand, and use intelligence optimization algorithm to rebalance bikes among stations. However, most of the previous studies dealt with only one period BRP and rarely considered multiperiod issues. At the same time, most researches have focused on minimizing the total cost or time of rebalancing or customer dissatisfaction, but few have aimed at minimizing the rebalancing amount. In addition, the demand gap can reflect the real rental and returning requirements of customers over a certain period of time, which is rarely considered in solving BRP. First of all, this paper presents a multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP). Secondly, a mathematical model is formulated with the objective of minimizing both the total rebalancing cost and amount. In order to solve MMBRP, an improved multiobjective backtracking search genetic algorithm (IMBSGA) is designed. Finally, the effectiveness and competitiveness of IMBSGA in solving MMBRP are verified by numerous experiments comparing with state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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“…A destroyand-repair algorithm was developed to improve the clusters, and an adaptive variable neighborhood search algorithm was designed to conduct intra-cluster and intercluster vehicle routing optimization. Ma et al (2021) developed an integer-programming model to consider multiple rebalancing vehicles with time-varying rental costs to alleviate the imbalanced bike distribution while also analyzing the intrinsic properties of such a model. They further proposed a chance constraint programming model, optimizing a bike-sharing network by implementing various genetic algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Proposing a Mathematical Model of Balancing the Inventory of Multi-Zone Bicycle Sharing Systems With Mobile Stations and Applying Maintenance Constraints

et al. 2022

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Regarding the necessity of developing transportation infrastructures and its increasing importance in urban issues, nowadays in different cities, bicycles are considered the main and sustainable vehicle along with walking and drawing more attention day by day. The case considers highly paramount since preservation of the environment, natural resources, and energy is one of the significant pillars of sustainable development, and urban transportation intensively influences it. Thus, Bicycle Sharing System (BSS) is recognized as an innovative urban transportation option that meets the citizens’ demand for commuting during the day. The BSS can highly affect the level of citizens’ health, and it can be counted as one of the leading health programs whether it’s added to the public transportation system, it can help the culture to be created to use bicycles instead of cars in most of the internal trips, and also it can be so influential in decreasing the air pollution and in the following its harmful effects on health issues. The mathematical model of rebalancing multi-zone BSS with mobile stations and applying maintenance constraints in a static status is considered in this research. The objective function of this research is a single-objective one, which is modeled with the aims of reducing the costs of traveled distances by the tracks within and outside the zones, reducing the costs of intact and defective bicycles transportation within and outside the zones, and eventually, reducing the costs of surplus bicycles depot at the stations. This issue is a multi-product one that includes different types of bicycles and balancing tracks. Computational results confirm the model’s efficiency. Also, sensitivity analysis has been done to prove that the model is affected by both parameters of storage costs of surplus bicycles and transportation costs within and outside the zones.

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“…End-to-end Shared Services: In recent years, various research efforts have been undertaken to provide E2ESS systems that can serve passengers traveling from his/her origins to the final destinations, such as taxi/car-sharing platforms [8,36], cybercars [21], autonomous vehicles [17], bike-sharing [48,22], shared electric vehicles [30], etc. These shared services reduce travel costs for the passengers and improve per trip revenue for service operators.…”

Section: Related Workmentioning

confidence: 99%

Passenger-centric vehicle routing for first-mile transportation considering request uncertainty

Ning

Jiang

Lam

et al. 2021

Information Sciences

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Rebalancing stochastic demands for bike-sharing networks with multi-scenario characteristics

Cited by 30 publications

References 38 publications

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Proposing a Mathematical Model of Balancing the Inventory of Multi-Zone Bicycle Sharing Systems With Mobile Stations and Applying Maintenance Constraints

Passenger-centric vehicle routing for first-mile transportation considering request uncertainty

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