Fleet sizing for electric car sharing system via closed queueing networks

Fanti, Maria Pia; Mangini, Agostino Marcello; Pedroncelli, Giovanni; Ukovich, Walter

doi:10.1109/smc.2014.6974098

Cited by 23 publications

(14 citation statements)

References 13 publications

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“…Kőchel et al [4] provided a joint simulation optimization idea of fleet size and bike repositioning strategy and proposed an iterative means to obtain the fleet size. Fanti et al [5] viewed an electric car sharing system as a CQN to determine the optimal fleet size and developed an optimization problem to maximize the system revenue aiming to find the optimal fleet size. Zhai et al [6] proposed a sparse matrix construction solution method for determining the fleet size of the DBSS.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Fan

2020

Processes

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The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Fan

2020

Processes

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“…The theory of queueing networks seems to be an adequate tool for analysis of car sharing systems, see [1]. Application of this theory is described, e.g., in [4,5] and references therein. In [4,5], the dynamics of the car sharing system are described by a closed queueing network where the cars are interpreted as the customers which are served by the servers (arriving clients).…”

Section: Introductionmentioning

confidence: 99%

“…Application of this theory is described, e.g., in [4,5] and references therein. In [4,5], the dynamics of the car sharing system are described by a closed queueing network where the cars are interpreted as the customers which are served by the servers (arriving clients). The analysis of the networks implemented in [4,5] is based on mean value analysis or on the assumption about the existence of the product form solution.…”

Section: Introductionmentioning

confidence: 99%

Queueing Network with Moving Servers as a Model of Car Sharing Systems

2019

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We consider a queueing network with a finite number of nodes and servers moving between the nodes as a model of car sharing. The arrival process of customers to various nodes is defined by a marked Markovian arrival process. The customer that arrives at a certain node when there is no idle server (car) is lost. Otherwise, he/she is able to start the service. With known probability, which depends on the node and the number of available cars, this customer can balk the service and leave the system. The service time of a customer has an exponential distribution. Location of the server in the network after service completion is random with the known probability distribution. The behaviour of the network is described by a multi-dimensional continuous-time Markov chain. The generator of this chain is derived which allows us to compute the stationary distribution of the network states. The formulas for computing the key performance indicators of the system are given. Numerical results are presented. They characterize the dependence of some performance measures of the network and the nodes on the total number of cars (fleet size of the car sharing system) and correlation in the arrival process.

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“…Authors prove that the stations should have the same availabilities to meet maximum user satisfaction and they solve the CQN problem with a system of 100 stations. Fanti et al in [32] model an electrical VSS to evaluate the operator revenue. They extend the framework introduced by George and Xia [31] by adding multiple servers queues to illustrate the recharging process.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 shows how these queues 185 are connected together.186 The explicit model of two bike stations.187 shows the explicit model of two interconnected bike stations 1 and 2. Thisfigure 188expands the model of George and Xia[32] by introducing the blocking mechanism. Closed network for two stations BSS, extension of the original model of George and Xia[32] Nodes 1 and 2 in blue represent the two real bike stations (SS nodes).…”

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confidence: 99%

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

et al. 2018

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The Bike Sharing System is a sustainable urban transport solution that consists of a fleet of bikes placed in various stations. Users will be satisfied if they find available bikes at their departure station and free docks at the destination. Despite the regulation operations of the system provider (i.e., redistribution of bikes by truck) deeper modifications (bike fleet size or station capacity) are often necessary to ensure a satisfactory service rate. In this paper, we model a sub-graph of a Bike Sharing System using the closed queuing network with a Repetitive-Service-Random-Destination blocking mechanism. This model is solved using the Maximum Entropy Method. This model faithfully reproduces the system dynamics considering the limited capacity of stations. We analyze the performance, particularly, via an overall performance indicator of the system. The various control and monitoring decisions (fleet-size, capacity of stations, incoming and outgoing flow of bikes) are applied to find out the best performance levels. The results demonstrate that the overall performance is robust enough regarding the fleet size changes but it degrades with the increase of the stations’ capacity. Finally, the arrival and departure flows control is an efficient and powerful operational leverage.

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Fleet sizing for electric car sharing system via closed queueing networks

Cited by 23 publications

References 13 publications

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Queueing Network with Moving Servers as a Model of Car Sharing Systems

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

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