scholarly journals Study on batching and picking optimization of marine outfitting pallets

2019 ◽  
Vol 272 ◽  
pp. 01015
Author(s):  
D G Zhao ◽  
Y Jiang ◽  
J W Bao ◽  
J Q Wang ◽  
H Jia
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Local Search ◽  
Numerical Experiments ◽  
Due Dates ◽  
Improved Genetic Algorithm ◽  
Due Date ◽  
Production Processes ◽  
Speed Up ◽  
Standard Genetic Algorithm

Outfitting pallet picking involves the retrieval of items from their storage sites in shipbuilding enterprises. A major issue in manual pallet picking operations is the transformation of outfitting pallets into picking batches (pallet batching). Considering the influence to the subsequent distribution and production processes, a mathematical model for the batching and picking problem of outfitting pallets is formulated with the objective of minimizing the total tardiness of all pallets. According to the characteristics of outfitting pallet picking operations, an Improved Genetic Algorithm (IGA) is proposed. A reversal operator is specially introduced to increase the local search ability of the standard genetic algorithm and speed up the evolution. Benchmarked against the solutions produced by the Earliest Due Date (EDD) rule, the performance of IGA is studied under different picking operations with different workloads and tightness of due dates. A series of numerical experiments are carried out to verify the researches. The results clearly show that IGA is competitive since it improves the solutions by 68.5%, on average, relative to the EDD.

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

Intelligent Generating Examination Paper from Item Bank Based on Improved Genetic Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3637-3640
Author(s):  
Li Feng ◽  
Qian Wu ◽  
Jing Shao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Computational Study ◽  
Item Bank ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Fitness Evaluation ◽  
Simulation Results ◽  
The Mathematical Model ◽  
Examination Paper

In this paper, we analyze the disadvantage of common generating test paper algorithm. An improved genetic algorithm (IGA) is proposed and used in auto-generating examination paper algorithm. We design the mathematical model of auto-generating test paper algorithm and improved the traditional GA fitness evaluation form. A computational study is carried out to verify the algorithm. Simulation results demonstrate that the performance of IGA can work efficiently than traditional ones.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

scholarly journals Research on the driving strategy of heavy-haul train based on improved genetic algorithm

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879101 ◽  
Author(s):  
Youneng Huang ◽  
Shuai Bai ◽  
Xianhong Meng ◽  
Huazhen Yu ◽  
Mingzhu Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Driving Safety ◽  
Sufficient Time ◽  
Improved Genetic Algorithm ◽  
Safe Operation ◽  
Multiple Parameters ◽  
Heavy Haul Train ◽  
Heavy Haul ◽  
The Mathematical Model

The driving safety of heavy-haul train is affected by the train’s traction weight, the length of train, the line profile, the line speed limit, and other factors. Generally, when the train is running on a continuously long and steep downgrade line, it needs using the circulating air braking to adjust speed. When it is braking, the brake wave is transmitted non-linearly along the direction of the train. When it is relieved, it must be ensured that there is sufficient time for the train to be inflated. Therefore, it is difficult to ensure the safe operation of the heavy-haul train. In this article, a new method of the train’s driving strategy based on improved genetic algorithm is proposed. First, a mathematical model for the operation of heavy-haul train is established with multiple parameters. Then, according to the improved genetic algorithm and the mathematical model of the heavy-haul train, the driving strategy of the chromosome of the train is studied. Finally, the driving curve which can ensure the safe running of the heavy-haul train can be obtained. By comparing the simulated driving curve with the actual one, the results show the effectiveness of the proposed method.

scholarly journals Improved Genetic Algorithm Tuning Controller Design for Autonomous Hovercraft

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 66 ◽  
Author(s):  
Huu Khoa Tran ◽  
Hoang Hai Son ◽  
Phan Van Duc ◽  
Tran Thanh Trang ◽  
Hoang-Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Fitness Function ◽  
Tracking Error ◽  
Biological Evolution ◽  
Operation Time ◽  
Design Criterion ◽  
Improved Genetic Algorithm ◽  
Fuzzy Pid Control ◽  
Speed Up

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time by neglecting chromosome decoding step is proposed to find the optimized fuzzy-proportional-integral-derivative (fuzzy-PID) control parameters. Due to minimizing tracking error of the controller design criterion, the fitness function integral of square error (ISE) was employed to utilize the advantages of the modified GA. The proposed method was then applied to a novel autonomous hovercraft motion model to display the superiority to the standard GA.

Study of Multi-Project Conflicts by IGA Based on the Improvement of Fitness Function

2013 ◽  
Vol 756-759 ◽  
pp. 2768-2773
Author(s):  
Zhi Feng Lv ◽  
Xiang Dong Ma
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Selection Methods ◽  
Improved Genetic Algorithm ◽  
Resource Conflicts ◽  
Simple Genetic Algorithm ◽  
The Family ◽  
Family Orientation ◽  
Standard Genetic Algorithm ◽  
Mutation And Selection

In the multi-project resource conflicts exist in the application of standard genetic algorithm fitness function exist "premature" problem, Genetic algorithm can not find the convergence of these issue. Based on the above issues ,an improved genetic algorithm (IGA) are appropriate, From the fitness function, mutation and selection methods to improve two aspects are described, the Improved genetic algorithm for simple genetic algorithm has the advantage of generations of each evolution, offspring parent always retains the best individual to the "high-fitness model for the ancestors of the family orientation" search out better samples, and verified through experiments the effectiveness of the algorithm

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

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