Optimization Method for Robust Continuous Parameter Design in the Target-being-Best Based on Genetic Evolution

2014 ◽  
Vol 1049-1050 ◽  
pp. 1272-1280
Author(s):  
Qiang Zeng ◽  
Ling Shen ◽  
Ze Bin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Calculation ◽  
Optimization Model ◽  
Optimization Method ◽  
Parameter Design ◽  
Genetic Evolution ◽  
Quality Loss ◽  
Average Quality ◽  
Continuous Parameter

Aiming at the problem of robust continuous parameter design in the Target-being-best, in which the output value can be obtained by theoretical calculation, an optimization method based on genetic evolution is proposed. Firstly, the researched problem is described mathematically and an optimization model is established with the objective to minimize the average quality loss of a sample. Secondly, the optimization method based on genetic evolution for the researched problem is proposed. Thirdly, the genetic algorithm for robust continuous parameter design in the Target-being-best is presented and designed. Finally, the effectiveness of the proposed method is validated by case study.

Optimization Method Based on Genetic Evolution for Multiple-Stage Coordinate Mining of Multiple Mining Areas with same Coal

2013 ◽  
Vol 807-809 ◽  
pp. 2460-2469
Author(s):  
Qiang Zeng ◽  
Ze Bin Zhang ◽  
Zu Qiang Xiong ◽  
De Quo Xiong
Keyword(s):  
Optimization Model ◽  
Mathematical Optimization ◽  
Optimization Method ◽  
Great Difficulty ◽  
Simplified Model ◽  
Genetic Evolution ◽  
Total Sale ◽  
Mining Areas ◽  
Mathematical Optimization Model

Aiming at the multiple-stage coordinate mining problem for multiple mining areas with same coal, an optimization method based on genetic evolution is proposed. Firstly, a mathematical optimization model with the objective to maximize the total sale of synthetic coal is established. Then, the model is simplified to another mathematical optimization model with the objective to maximize price of synthetic coal of each stage because of the great difficulty to resolve the model. Thirdly, a genetic algorithm for coordinate mining of multiple mining areas is presented and designed to resolve the simplified model. Finally, the effectiveness of the proposed method is validated by case study.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

scholarly journals Optimizing Bus Line Based on Metro-Bus Integration

2020 ◽  
Vol 12 (4) ◽  
pp. 1493 ◽  
Author(s):  
Junjun Wei ◽  
Kejun Long ◽  
Jian Gu ◽  
Qingling Ju ◽  
Piao Zhu
Keyword(s):  
Resource Allocation ◽  
Optimization Model ◽  
Geographical Location ◽  
Optimization Method ◽  
Line Number ◽  
Competition And Cooperation ◽  
Travel Efficiency ◽  
Geographical Space ◽  
Metro System

Metros are usually built and added on the basis of a completed bus network in Chinese cities. After the metro construction, it is faced with the problem of how to adjust and optimize the original bus lines based on the new metro system. This research mainly proposes a bus line optimization method based on bus and metro integration. In the consideration of the geographical space, the cooperation and competition relationship between bus and metro lines is qualitatively introduced according to the geographical location and service range of metro (800 m radius) and bus (500 m radius) stations. The competition and cooperation indexes are applied to define the co-opetition relationship between bus and metro lines. The bus line optimization model is constructed based on the co-opetition coefficient and Changsha Metro Line Number 2 is chosen as a case study to verify the optimization model. The results show that the positive competition, efficient cooperation, and travel efficiency between metro and bus has been significantly enhanced after optimization. Moreover, this paper provides a reasonable reference for public transport network planning and resource allocation.

Optimization for Mining Material Loading Based on Genetic Algorithm

2012 ◽  
Vol 457-458 ◽  
pp. 1342-1346
Author(s):  
Tong Bin Zhao ◽  
Shan Shan Liu ◽  
Fan Wei Bu
Keyword(s):  
Genetic Algorithm ◽  
Carrying Capacity ◽  
Optimization Method ◽  
Search Performance ◽  
Management Level ◽  
Optimization Calculation ◽  
Loading Problem ◽  
Satisfactory Answer ◽  
Loading Scheme

In order to improve management level of mining materials, optimum loading scheme is important. Based on the analysis of bulk cargo loading problem, taking carrying capacity and effective volume as constraint conditions, maximizing transport benefits as target, mathematical model on the base of optimization method is established. And genetic algorithm is introduced to case study. The result shows that genetic algorithm in solving the optimum loading scheme of mining materials has quick convergence, short term, and higher precision. The better satisfactory answer can be obtained after 100 generations. Before 600 generations optimum loading scheme can be educed. Genetic algorithm, with good adaptability and powerful search performance, is very suitable for optimization calculation of multiple constraints problem. Genetic algorithm can make full use of carrying capacity and volume in the process of bulk cargo loading transport, that promot mining enterprise’s operation efficiency. The study is useful for management work of mining material warehousing, scheduling, transportation etc.

scholarly journals Sensor Networks Hierarchical Optimization Model for Security Monitoring in High-Speed Railway Transport Hub

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhengyu Xie ◽  
Yong Qin
Keyword(s):  
Sensor Networks ◽  
Optimization Model ◽  
High Speed ◽  
Optimization Problem ◽  
Optimization Method ◽  
Hierarchical Optimization ◽  
Railway Transport ◽  
High Speed Railway ◽  
Security Monitoring

We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH). The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.

Study on a Robust Optimization Model for Project Scheduling Policies

2011 ◽  
Vol 110-116 ◽  
pp. 2866-2871
Author(s):  
Yu Zhang ◽  
Teng Fei Yin
Keyword(s):  
Genetic Algorithm ◽  
Robust Optimization ◽  
Objective Function ◽  
Project Scheduling ◽  
Optimization Model ◽  
Optimal Schedule ◽  
Optimization Method ◽  
Construction Process ◽  
Scheduling Policies ◽  
Project Environment

The genetic algorithm discussed in this paper for project scheduling solution to this problem can be obtained the near optimal schedule programs. This has established the objective function and constraints that have a certain scope; it requires the duration of each process that is determined in advance for enterprises. If the project is more familiar with the history with more experience, and more complete database, the project environment can be controlled well. It can accurately determine the time with the construction plan, construction process and the optimization method with the good trial.

scholarly journals Optimization Method of Locomotive Working Diagram Layout

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yajing Zheng ◽  
Wenzhou Jin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimization Method ◽  
The Optimizing Model ◽  
Optimizing Model

Rational scheduling of locomotive paths (the locus of the locomotive point in the train working diagram) is an important step in drawing a locomotive working diagram by a computer. But there are some problems in this process, such as the computer usually drawing a locomotive path that overlaps with another locomotive path (in the circumstances, the actual users of the locomotive working diagrams often misread the locomotive planning). At present, there are many studies about assigning sets of locomotives to each train in a preplanned train schedule; in contrast, the studies of visualizing the locomotive planning are relatively rare. Through investigating the locomotive working diagram users, this paper points out that the layout of locomotive paths should put the distance between lines being as large as possible and should put the number of the intersection between lines being as few as possible as the optimization aim which is based to solve the problem of the lines overlap or the problem of the lines beyond the margins for drawing the locomotive paths. This paper also builds the optimization model of locomotive working diagram layout. Based on determining the position of locomotive paths which can be delineated, a genetic algorithm is used to solve the optimizing model of locomotive working diagram layout in this paper. An example of a train working diagram with 36 trains is given at the end of the paper, which indicates that the optimization model of locomotive working diagram layout can better solve the problem of locomotive planning visualization.

Tool requirement and pre-scheduling optimization model of the tool flow system of a digital workshop

2017 ◽  
Vol 231 (5) ◽  
pp. 838-849
Author(s):  
Qi Lei ◽  
Li Zeng ◽  
Yuchuan Song
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Method ◽  
Double Layer ◽  
Heuristic Algorithm ◽  
Optimization Model ◽  
Optimization Problems ◽  
Flow System ◽  
Scheduling Optimization ◽  
Purchase Cost

A new mathematical method and an optimization model are proposed in this study to solve the tool requirement and pre-scheduling optimization problems involved in the tool flow system of digital workshops. This model aims to minimize the system makespan under the constraint of the tool purchase cost. A double-layer genetic algorithm based on the heuristic algorithm is then developed. This algorithm not only combines the advantages but also avoids the weaknesses of the two algorithms. Finally, a case study is conducted to validate the effectiveness and superiority of the proposed algorithm and the tool-machine dual-resource pre-scheduling optimization model.

Sign in / Sign up

Export Citation Format

Share Document