Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

Alfian, Ganjar; Rhee, Jongtae; Ijaz, Muhammad Fazal; Syafrudin, Muhammad; Fitriyani, Norma Latif

doi:10.3390/app7060598

Cited by 17 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adequate fleet size is estimated through a Monte Carlo simulation that includes factors such as vehicle types (electric or gasoline vehicle), charger types for electric vehicles (level 2 or level 3 chargers) that influence charging time, arrival rates, travel distance, and travel time based on the time intervals (peak or nonpeak hours). Alfian et al [23] proposed forecasting relocation to solve car distribution imbalances for one-way car sharing services. Real case data sets have been used to find the best simulation results.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

Zhong

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

Zhong

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Efficient management of operations in manufacturing industries and services is important [19][20][21][22][23] and requires an accurate prediction of customer demand as this directly influences strategic, tactical and operational decisions. More specifically, a higher level of forecasting accuracy aids the manufacturer in formulating the appropriate production plan, restoring balance, scheduling the production line and maintaining proper inventory level as well as satisfying customer demand on time [24].…”

Section: Literature Reviewmentioning

confidence: 99%

A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component

et al. 2018

View full text Add to dashboard Cite

The adoption of forecasting approaches such as the multiplicative Holt-Winters (MHW) model is preferred in business, especially for the prediction of future events having seasonal and other causal variations. However, in the MHW model the initial values of the time-series parameters and smoothing constants are incorporated by a recursion process to estimate and update the level (L T), growth rate (b T) and seasonal component (SN T). The current practice of integrating and/or determining the initial value of L T is a stationary process, as it restricts the scope of adjustment with the progression of time and, thereby, the forecasting accuracy is compromised, while the periodic updating of L T is avoided, presumably due to the computational complexity. To overcome this obstacle, a fuzzy logic-based prediction model is developed to evaluate L T dynamically and to embed its value into the conventional MHW approach. The developed model is implemented in the MATLAB Fuzzy Logic Toolbox along with an optimal smoothing constant-seeking program. The new model, proposed as a collaborative approach, is tested with real-life data gathered from a local manufacturer and also for two industrial cases extracted from literature. In all cases, a significant improvement in forecasting accuracy is achieved.

show abstract

“…In discrete event simulations, system states are scheduled dynamically as the simulation proceeds. Various modeling schemes (e.g., finite state machine, Petri-net, DEVS, or Markov chain) have been used for discrete event simulations [24][25][26][27][28]. Among them, the DEVS is a modular and hierarchical formalism for object-oriented modeling and analysis [2], which is our approach for modeling the discrete event system.…”

Section: Discrete Event Systemmentioning

confidence: 99%

Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework

Seo

Kim

2017

Applied Sciences

View full text Add to dashboard Cite

Simulation analysis has been performed for simulation experiments of all possible input combinations as a "what-if" analysis, which causes the simulation to be extremely time-consuming. To resolve this problem, this paper proposes a multi-fidelity modeling framework for enhancing simulation speed while minimizing simulation accuracy loss. A target system for this framework is a discrete event dynamic system. The dynamic property of the system facilitates the development of variable fidelity models for the target system due to its high computational cost; and the discrete event property allows for determining when to change the fidelity within a simulation scenario. For formal representation, the paper defines several key concepts such as an interest region, a fidelity change condition, and a selection model. These concepts are integrated into the framework to allow for the achievement of a condition-based disjunction of high-and low-fidelity simulations within a scenario. The proposed framework is applied to two case studies: unmanned underwater and urban transportation vehicles. The results show that simulation speed increases at least 1.21 times with a 5% accuracy loss. We expect that the proposed framework will resolve a computationally expensive problem in the simulation analysis of discrete event dynamic systems.

show abstract

Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

Cited by 17 publications

References 31 publications

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component

Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework

Contact Info

Product

Resources

About