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 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 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.

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.

scholarly journals Flexible Bus Route Optimization for Multitarget Stations

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Sun Ji-yang ◽  
Huang Jian-ling ◽  
Chen Yan-yan ◽  
Wei Pan-yi ◽  
Jia Jian-lin
Keyword(s):  
Travel Time ◽  
Optimization Model ◽  
Transportation Network ◽  
Transportation Systems ◽  
Route Optimization ◽  
Planning Time ◽  
Bus Scheduling ◽  
Proposed Model ◽  
Average Travel Time ◽  
Better Than

This paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. The model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles’ running time and the passengers’ travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. The results show that the total number of vehicles needed to complete the service is 17–21, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passenger’s dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.

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.

AN UNBIASED BI-OBJECTIVE OPTIMIZATION MODEL AND ALGORITHM FOR CONSTRAINED OPTIMIZATION

2014 ◽  
Vol 28 (08) ◽  
pp. 1459008 ◽  
Author(s):  
NING DONG ◽  
YUPING WANG
Keyword(s):  
Constrained Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Selection Criterion ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Optimal Vector ◽  
Pareto Ranking ◽  
Proposed Model ◽  
Objective Model

Transforming a constrained optimization problem (COP) into a bi-objective optimization problem (BOP) is an efficient way to solve the COP. However, how to obtain a good balance between the objective function and the constraint violation function is not easy in BOP. To handle this issue, a novel unbiased bi-objective optimization model is proposed, in which both objective functions are equally treated. Furthermore, the novel model is shown to have the unique Pareto optimal vector under proper condition, and the Pareto optimal vector is exactly corresponding to the optimal solution of COP. Moreover, the relationship between the existing biased bi-objective model and the proposed unbiased one is analyzed in detail. For the unbiased model, a generic multi-objective optimization evolutionary algorithm, i.e. a differential evolution (DE), can be used to solve it, and Pareto ranking is employed as the unique selection criterion. The experiments are conducted on 24 well-known benchmark test instances and the results illustrate that the proposed model is not only effective but also efficient.

Cordon-Based Pricing Schemes for Mixed Urban-Freeway Networks using Macroscopic Fundamental Diagrams

2021 ◽  
pp. 036119812110159
Author(s):  
Xuting Wang ◽  
Vikash V. Gayah
Keyword(s):  
Control Strategies ◽  
User Equilibrium ◽  
Real Time Control ◽  
Urban Networks ◽  
Time Control ◽  
The Difference ◽  
Freeway Networks ◽  
Mixed Networks ◽  
Urban Freeway ◽  
Pricing Schemes

The development of traffic models based on macroscopic fundamental diagrams (MFD) enables many real-time control strategies for urban networks, including cordon-based pricing schemes. However, most existing MFD-based pricing strategies are designed only to optimize the traffic-related performance, without considering the revenue collected by operators. In this study, we investigate cordon-based pricing schemes for mixed networks with urban networks and freeways. In this system, heterogeneous commuters choose their routes based on the user equilibrium principle. There are two types of operational objective for operating urban networks: (1) to optimize the urban network’s performance, that is, to maximize the outflux; and (2) to maximize the revenue for operators. To compare those two objectives, we first apply feedback control to design pricing schemes to optimize the urban network’s performance. Then, we formulate an optimal control problem to obtain the revenue-maximization pricing scheme. With numerical examples, we illustrate the difference between those pricing schemes.

A Review of Classical and Modern Control Techniques Utilized in Modern Microgrids

2021 ◽  
Vol 14 ◽  
Author(s):  
Muhammad Hamza Shahbaz ◽  
Arslan Ahmed Amin
Keyword(s):  
Control Strategies ◽  
Optimal Solution ◽  
Energy Resources ◽  
Control Methods ◽  
Research Gaps ◽  
Control Techniques ◽  
Micro Grid ◽  
Exciting Development ◽  
Micro Grids ◽  
Modern Control

: Because of the consistently expanding energy request, the introduction of a decentralized micro-grid based on energy resources will soon be the most exciting development in the power system. Micro-grids, which are mainly based on inverters, are becoming more popular as they can handle different forms of renewable energy effectively. However, one of the most challenging areas of research is their control. In the last few years, many control strategies have been developed. In this review, different control methods have been discussed that apply to the micro-grid system. Furthermore, the comparative analysis of classical and modern control strategies is also considered. This survey guides the new researchers about all available control strategies and room for improvement towards the optimal solution of the micro-grid control techniques. It also identifies several research gaps and future trends therein as well as provides a solution to manage problems in MGs. The strategies are then compared based on their applicability to different control requirements.

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