Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

Xiaoting Mo,Wai Kin (Victor) Chan,Xinglu Liu

doi:10.3390/a14020047

Xiaoting Mo, Wai Kin (Victor) Chan + Show 1 more

Open Access

https://doi.org/10.3390/a14020047

Copy DOI

Journal: Algorithms	Publication Date: Jan 30, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: Tsinghua–Berkeley Shenzhen Institute

Abstract

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.

Highlights

The existence of idle resources and people’s willingness to well use them to promote the sharing economy has brought several lifestyle changes, including various traffic modes
The relationship between the steady-state probabilities and the arrival rates at different nodes in the network model with unequal demands is investigated in multiple cases by numerical experiments
The Effect of Relocation-Related Parameters c and μ are two positive parameters which can be regarded as a measure of the effectiveness of an adopted relocation strategy

Summary

Introduction

The existence of idle resources and people’s willingness to well use them to promote the sharing economy has brought several lifestyle changes, including various traffic modes. A typical resource sharing pattern, has become more popular and common, such as bike-sharing [1], ride-sharing [2], car-sharing [2] and electric vehicle sharing [3]. The shared transport emergence contributes to protecting the environment, conserving energy, reducing traffic congestion and improving transportation resource utilization and availability [4]. An optional solution for improving this is to rebalance or reposition shared resources, e.g., reposition shared-bikes by trucks in bike-sharing systems (see [6]) and reposition shared cars in vehicle sharing systems (see [3])

Objectives

Methods

Results

Discussion

Conclusion