scholarly journals Dynamic Scheduling Model of Bike-Sharing considering Invalid Demand

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Liu He ◽  
Tangyi Guo ◽  
Kun Tang
Keyword(s):  
Optimization Model ◽  
Dynamic Scheduling ◽  
Demand Forecasting ◽  
Optimal Solution ◽  
Coupling Model ◽  
Service Level ◽  
Demand Prediction ◽  
Dynamic Relationship ◽  
Proposed Model ◽  
Bike Sharing

System resources allocation optimization through dynamic scheduling is key to improving the service level of bike-sharing. This study innovatively introduces three types of invalid demand with negative effect including waiting, transfer, and abandoning, which consists of the total demand of bike-sharing system. Through exploring the dynamic relationship among users’ travel demands, the quantity and capacity of bikes at the rental points, the records of bicycles borrowed and returned, and the vehicle scheduling schemes, a demand forecasting model for bike-sharing is established. According to the predicted bikes and the maximum capacity limit at each rental point, an optimization model of scheduling scheme is proposed to reduce the invalid demand and the total scheduling time. A two-layer dynamic coupling model with iterative feedback is obtained by combining the demand prediction model and scheduling optimization model and is then solved by Nicked Pareto Genetic Algorithm (NPGA). The proposed model is applied to a case study and the optimal solution set and corresponding Pareto front are obtained. The invalid demand is greatly reduced from 1094 to 26 by an effective scheduling of 3 rounds and 96 minutes. Empirical results show that the proposed model is able to optimize the resource allocation of bike-sharing, significantly reduce the invalid demand caused by the absence of bikes at the rental point such as waiting in a place, walking to other rental points, and giving up for other travel modes, and effectively improve the system service level.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

A One to Multiple Order Allocation Optimization Model of Horizontal Manufacturing Collaborative Alliance

2012 ◽  
Vol 201-202 ◽  
pp. 996-999
Author(s):  
Jin Gao
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a multi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yajun Zhou ◽  
Lilei Wang ◽  
Rong Zhong ◽  
Yulong Tan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Prediction Method ◽  
Chain Model ◽  
Daily Production ◽  
Forecasting Accuracy ◽  
Traffic Demand ◽  
Demand Prediction ◽  
Proposed Model ◽  
Bike Sharing

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

scholarly journals Robust Optimization Model with Shared Uncertain Parameters in Multi-Stage Logistics Production and Inventory Process

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 211
Author(s):  
Lijun Xu ◽  
Yijia Zhou ◽  
Bo Yu
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Model ◽  
Multi Stage ◽  
Proposed Model ◽  
Dual Forms

In this paper, we focus on a class of robust optimization problems whose objectives and constraints share the same uncertain parameters. The existing approaches separately address the worst cases of each objective and each constraint, and then reformulate the model by their respective dual forms in their worst cases. These approaches may result in that the value of uncertain parameters in the optimal solution may not be the same one as in the worst case of each constraint, since it is highly improbable to reach their worst cases simultaneously. In terms of being too conservative for this kind of robust model, we propose a new robust optimization model with shared uncertain parameters involving only the worst case of objectives. The proposed model is evaluated for the multi-stage logistics production and inventory process problem. The numerical experiment shows that the proposed robust optimization model can give a valid and reasonable decision in practice.

An Optimization Model for Dynamic Speed Control in Urban Freeway Networks

2014 ◽  
Vol 668-669 ◽  
pp. 1458-1461
Author(s):  
Zhao Hong Zhang ◽  
Da Zhi Sun ◽  
Jin Peng Lv ◽  
Joseph Sai ◽  
M. Faruqi
Keyword(s):  
Optimization Model ◽  
Control Strategies ◽  
Optimal Solution ◽  
Speed Control ◽  
Speed Limits ◽  
Microscopic Traffic Simulation ◽  
Proposed Model ◽  
Average Travel Time ◽  
Freeway Networks ◽  
Urban Freeway

This paper introduced an optimization model to address dynamic speed control strategies for achieving network-wide speed harmonization. Genetic Algorithm (GA) was applied to search the optimal solution of the proposed model. During the search process, a computational fluid dynamics (CFD) based analytical model and microscopic traffic simulation VISSIM were applied to evaluate the performance of possible solutions. The proposed model can be used to determine the deployment of dynamic speed limits, the displayed speed limit, and the timing to change these speed limits. The proposed model was tested using VISSIM in an urban freeway network of about 12 miles long. Different simulation scenarios with varying AADT from 60,000 to 12,000 were tested. It was found that when properly implemented, dynamic speed control can improve traffic flow conditions, reduce congestion and emission, and enhance network throughput. For example, in the selected urban freeway network with the AADT of 80,000, the proposed dynamic speed control strategy can save 5% average travel time, reduce 9% of the vehicles with high collision risk and about 11% emission.

scholarly journals Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

2019 ◽  
Vol 11 (11) ◽  
pp. 3088 ◽  
Author(s):  
Ruijing Wu ◽  
Shaoxuan Liu ◽  
Zhenyang Shi
Keyword(s):  
Urban Areas ◽  
Travel Demand ◽  
Optimal Solution ◽  
Service Level ◽  
Maximization Problem ◽  
Limited Information ◽  
Customer Service Level ◽  
Wide Range ◽  
Bike Sharing ◽  
Incentive Plan

Free-float bike-sharing (FFBS) systems have increased in popularity as a sustainable travel mode in recent years, especially in the urban areas of China. Despite the convenience such systems offer to customers, it is not easy to maintain an effective balance in the distribution of bikes. This study considers the dynamic rebalancing problem for FFBS systems, whereby user-based tactics are employed by incentivizing users to perform repositioning activities. Motivated by the fact that the problem is frequently faced by FFBS system operators entering a new market with limited information on travel demand, we adopt the ranking and selection approach to select the optimal incentive plan. We describe the system dynamics in detail, and formulate a profit maximization problem with a constraint on customer service level. Through numerical studies, we first establish that our procedure can select the optimal incentive plan in a wide range of scenarios. Second, under our incentive plan, the profit and service level can be improved significantly compared with the scenario without incentive provision. Third, in most cases, our procedure can achieve the optimal solution with a reasonable sample size.

scholarly journals Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction

2019 ◽  
Vol 33 ◽  
pp. 1004-1011 ◽  
Author(s):  
Youru Li ◽  
Zhenfeng Zhu ◽  
Deqiang Kong ◽  
Meixiang Xu ◽  
Yao Zhao
Keyword(s):  
New York ◽  
Source Information ◽  
Temporal Modeling ◽  
Demand Prediction ◽  
Temporal Representation ◽  
Word Sequence ◽  
Proposed Model ◽  
Bike Sharing ◽  
Event Flow ◽  
Historical Transition

Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.

scholarly journals Optimization Model for the Supply Volume of Bike-Sharing: Case Study in Nanjing, China

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 182
Author(s):  
Jiajie Yu ◽  
Yanjie Ji ◽  
Chenyu Yi ◽  
Chenchen Kuai ◽  
Dmitry Ivanovich Samal
Keyword(s):  
Optimization Model ◽  
Road Network ◽  
Optimal Solution ◽  
Traffic Analysis ◽  
Urban Traffic ◽  
Mixed Integer ◽  
The Road ◽  
Bike Sharing ◽  
Flow Conservation ◽  
Parking Facilities

In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying capacity (road network and parking facilities carrying capacities), and the flow conservation of shared bikes in each traffic analysis zone. The model was formulated through mixed-integer programming with the aim of minimizing the total costs for users and bike-sharing enterprises (including the travel cost of users, production and maintenance costs of shared bikes, and repositioning costs). CPLEX was used to obtain the optimal solution for the model. Then, the optimization model was applied to 183 traffic analysis zones in Nanjing, China. The results showed that not only were user demands met, but the load ratios of the road network and parking facilities with respect to bike-sharing in each traffic zone were all decreased to lower than 1.0 after the optimization, which established the rationality and effectiveness of the optimization results.

STOCHASTIC SCENARIO-BASED TIME-STAGE OPTIMIZATION MODEL FOR THE LEAST EXPECTED TIME SHORTEST PATH PROBLEM

2013 ◽  
Vol 21 (supp01) ◽  
pp. 17-33 ◽  
Author(s):  
LIXING YANG ◽  
XIAOFEI YANG ◽  
CUILIAN YOU
Keyword(s):  
Shortest Path ◽  
Optimization Model ◽  
Optimal Solution ◽  
Shortest Path Problem ◽  
Travel Times ◽  
Expected Time ◽  
Second Stage ◽  
Time Threshold ◽  
Proposed Model ◽  
Time Invariant

Focusing on finding a pre-specified basis path in a network, this research formulates a two-stage stochastic optimization model for the least expected time shortest path problem, in which random scenario-based time-invariant link travel times are utilized to capture the uncertainty of the realworld traffic network. In this model, the first stage aims to find a basis path for the trip over all the scenarios, and the second stage intends to generate the remainder path adaptively when the realizations of random link travel times are updated after a pre-specified time threshold. The GAMS optimization software is introduced to find the optimal solution of the proposed model. The numerical experiments demonstrate the performance of the proposed approaches.

An Interval Optimization Model of Multi-Criteria Parameters in the Process of Selecting Manufacturing Suppliers

2012 ◽  
Vol 601 ◽  
pp. 570-575
Author(s):  
Hong Fei Wang
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Optimization Model ◽  
Euclidean Distance ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Final Decision ◽  
Interval Optimization ◽  
Proposed Model ◽  
Decision Making Problem

In the process of selecting manufacturing suppliers, most research works focus on the final decision making problem. However, the process is a dynamic and rapidly changing evolving one and it is necessary to gradually adjust and optimize multi-criteria parameters such as price, time and quality and so on. The objective of this paper is to propose an interval optimization model of multi-criteria parameters and introduce the distance metric of Euclidean distance and vector theories to construct the model. An adaptive genetic algorithm based on real encoding is applied to obtain the optimal solution. Finally, a practical example is implemented to verify the validity of the proposed model and approach.

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