The Research of First and Last Trip Optimization under the Network of Urban Mass Transit

2013 ◽  
Vol 409-410 ◽  
pp. 1311-1314 ◽  
Author(s):  
Yong Feng Shang ◽  
Lei Shan Zhou ◽  
Lu Tong ◽  
Chang Jun Cai
Keyword(s):  
Optimal Solution ◽  
Mass Transit ◽  
Time Range ◽  
Feasible Region ◽  
Rail Network ◽  
Urban Mass Transit ◽  
Time Quantum ◽  
Network Operation ◽  
Local Optimal Solution ◽  
Transfer Stations

Under the background of the urban mass transit becoming network increasingly, time coordination of the first and last trains becomes the important part of urban mass transit network operation, and has special significance. This paper firstly ascertains the time range of first and last running of trains and analyzes from two aspects. Then find the time quantum that the passengers and companies all accept. The paper analyzes the departed principle of networked first and last running time of trains, the target is the minimum of the passengers total loss of time at the transfer stations, setting up a multiple-direction trains transfer mathematical model that achieves attainability and rationality for transferring among different lines and getting a local optimal solution by using the method of iteration, and the optimal solution is within the feasible region and fits actual situations. Lastly a specific urban rail network is taken as an example to check the model and algorithm.

scholarly journals Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianyuan Guo ◽  
Limin Jia ◽  
Yong Qin ◽  
Huijuan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Mass Transit ◽  
Constrained Optimization Problem ◽  
Swarm Optimization ◽  
Transit Network ◽  
Urban Mass Transit ◽  
Transfer Stations

In urban mass transit network, when passengers’ trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

Modified Sub-Population Based Heat Transfer Search Algorithm for Structural Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ghanshyam Tejani ◽  
Vimal Savsani ◽  
Vivek Patel
Keyword(s):  
Heat Transfer ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Population Based ◽  
Truss Optimization ◽  
Feasible Region ◽  
Frequency Constraints ◽  
Local Optimal Solution ◽  
Better Than

In this study, a modified heat transfer search (MHTS) algorithm is proposed by incorporating sub-population based simultaneous heat transfer modes viz. conduction, convection, and radiation in the basic HTS algorithm. However, the basic HTS algorithm considers only one of the modes of heat transfer for each generation. The multiple natural frequency constraints in truss optimization problems can improve the dynamic behavior of the structure and prevent undesirable vibrations. However, shape and size variables subjected to frequency constraints are difficult to handle due to the complexity of its feasible region, which is non-linear, non-convex, implicit, and often converging to the local optimal solution. The viability and effectiveness of the HTS and MHTS algorithms are investigated by six standard trusses problems. The solutions illustrate that the MHTS algorithm performs better than the HTS algorithm.

Research on Optimization of Urban Mass Transit Network Schedule Based on Coordination of Connecting Time Between Different Lines

2010 ◽  
Author(s):  
Xiaohong Fang ◽  
Leishan Zhou ◽  
Ming Xia
Keyword(s):  
Waiting Times ◽  
Mass Transit ◽  
Service Level ◽  
Small Time ◽  
Optimization Techniques ◽  
Transit System ◽  
Transit Network ◽  
Schedule Coordination ◽  
Urban Mass Transit ◽  
Transfer Stations

On urban mass transit network, some connections inherently exist between trains on different lines through transfer stations. Schedule coordination for different lines, especially optimizing the arriving and departure times at transfer stations, may significantly reduce transfer waiting times at stations where various routes are interconnected, so as to improve the passenger service level. Based on the passengers flow characters of urban mass transit (UMT), both the convenience and rationality of connection between different lines were considered, and then an optimization model, with the aims of the minimal total waiting time of transfer passengers and inboard passengers, was set up. Combining the inner coordination of arriving and departure time sequence of trains in transfer nodes with the exterior coordination of transfer nodes on whole urban mass transit network, a multi-layers coordination policy was proposed, and the integrated optimization of the urban mass transit system was realized through taking some small time shifts of the proposed singleline timetables. In order to verify the validity and feasibility of the model and algorithm, we conducted an experimental study. The result turns out that improvement on UMT network can be determined by such optimization techniques.

scholarly journals An Improved Prim Algorithm for Connection Scheme of Last Train in Urban Mass Transit Network

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 681
Author(s):  
Xiaoxu Zeng ◽  
Qinglei Liu ◽  
Song Yao
Keyword(s):  
Solution Process ◽  
Mass Transit ◽  
Important Work ◽  
Transit Network ◽  
Scheme Optimization ◽  
Passenger Flow ◽  
Urban Mass Transit ◽  
Network Operation ◽  
Data Source ◽  
Accessible Form

In the network operation stage of urban mass transit, the connection relations between lines can effectively improve travel accessibility. As the last opportunity to travel on the same day, whether the last train can achieve transfer will directly affect passengers’ travel experiences. It is an important work to make the connection scheme of the last train, which needs to take the network structure, the trend, and the volume of passenger flow into overall consideration. This paper analyzes the characters of the last train connection, including the connection structure and the accessible form. Then, we establish a scheme model and define the objectives, the constraints, and the decision data source, and transform it into a graph theory problem. Taking an urban mass transit network as an example, we demonstrate the solution process by using an improved Prim algorithm. Finally, the main aspects and methods of initial scheme optimization are proposed.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

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