Solving Hot Rolling Batch Planning Problem by Genetic Algorithm

2011 ◽  
Author(s):  
Haitao Li ◽  
Sujian Li ◽  
Di Wu
Keyword(s):  
Genetic Algorithm ◽  
Hot Rolling ◽  
Planning Problem ◽  
Batch Planning

Hot Rolling Batch Planning Problem Model Based on Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 2042-2046
Author(s):  
Hai Tao Li ◽  
Su Jian Li ◽  
Di Wu ◽  
Fang Han ◽  
Fang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hot Rolling ◽  
Production Scheduling ◽  
Steel Production ◽  
Planning Problem ◽  
Planning Model ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Iron And Steel ◽  
Batch Planning

To solve the hot rolling batch planning problem in production scheduling of iron and steel enterprises, a hot rolling batch planning model is formulated based on multiple travelling salesmen problem(MTSP) model. The objective is to minimize the total limit penalty value of adjacent stripped steels in width, thickness and hardness. The main constraints include jumps in width, thickness and hardness between adjacent stripped steels, which are essential for steel production process. An improved genetic algorithm is designed to solve the model. A simulation example shows the reasonability of the model and validity of the algorithm.

Hot-Rolling Batch Planning Problem of Round Bar with Roller Capacity

2014 ◽  
Vol 941-944 ◽  
pp. 2317-2320
Author(s):  
Liang Bai ◽  
Tie Ke Li ◽  
Bai Lin Wang ◽  
Guang Jing Dong ◽  
Shao Yun Xu
Keyword(s):  
Hot Rolling ◽  
Mathematic Model ◽  
Constraint Propagation ◽  
Planning Problem ◽  
Routing Problem ◽  
Selection Rules ◽  
Proposed Model ◽  
Round Bar ◽  
Set Up ◽  
Batch Planning

Considering the feature of roller capacity with dynamics change in round bar production, a hot-rolling batch planning problem is studied. Firstly, the problem is formulated as a different capacities vehicle routing problem (DCVRP). And then, a mathematic model is built with two optimization objectives, minimization for total set-up time and maximization for utilization of roller. Secondly, an algorithm based on constraint satisfaction technique is present with variable selection rules and value selection rules for billet selecting, grouping and sequencing, and constraint propagation for searching-space reducing and rolling-unit partitioning. Meanwhile, the current capacity of online roller is updated dynamically during the solving process of algorithm. Finally, comparing results indicated that the proposed model and algorithm are effective and efficient.

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

Trajectory Planning for Concrete Pump Truck Based on Intelligent Hill Climbing and Genetic Algorithm

2011 ◽  
Vol 127 ◽  
pp. 360-367 ◽  
Author(s):  
Xiao Dong Kang ◽  
Gang Huang ◽  
Xian Li Cao ◽  
Xiang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Performance Comparison ◽  
Hill Climbing ◽  
Planning Problem ◽  
Online Planning ◽  
And Performance ◽  
Concrete Pump Truck ◽  
Concrete Pump ◽  
Constrained Conditions

This paper takes the five –link concrete pump boom as the research object, and transforms its trajectory planning issue into a multi-object optimization problem. Using intelligent hill climbing algorithm and genetic algorithm, and integrating them closely to ensure real-time online planning for the pump truck effectively, and make the planned motion trajectory for the boom is global optimized under particular constrained conditions. Simulation and performance comparison experiments show that this hybrid algorithm is practical and effective, which offers a new approach for the trajectory planning problem of concrete pump truck.

Transmission Network Expansion Planning Considering Optimal Allocation of Series Capacitive Compensation and Active Power Losses

2021 ◽  
Vol 12 (1) ◽  
pp. 388
Author(s):  
Dany H. Huanca ◽  
Luis A. Gallego ◽  
Jesús M. López-Lezama
Keyword(s):  
Genetic Algorithm ◽  
Test System ◽  
Active Power ◽  
Mixed Integer ◽  
Planning Problem ◽  
Transmission Network ◽  
Power Losses ◽  
Network Expansion ◽  
Expansion Planning ◽  
Active Power Losses

This paper presents a modeling and solution approach to the static and multistage transmission network expansion planning problem considering series capacitive compensation and active power losses. The transmission network expansion planning is formulated as a mixed integer nonlinear programming problem and solved through a highly efficient genetic algorithm. Furthermore, the Villasana Garver’s constructive heuristic algorithm is implemented to render the configurations of the genetic algorithm feasible. The installation of series capacitive compensation devices is carried out with the aim of modifying the reactance of the original circuit. The linearization of active power losses is done through piecewise linear functions. The proposed model was implemented in C++ language programming. To show the applicability and effectiveness of the proposed methodology several tests are performed on the 6-bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system, presenting costs reductions in their multi-stage expansion planning of 7.4%, 4.65% and 1.74%, respectively.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

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