A Petri Net and Genetic Algorithm Based Method for Flexible Manufacturing Cells Modeling and Scheduling

2009 ◽  
Vol 407-408 ◽  
pp. 268-272 ◽  
Author(s):  
Li Hong Qiao ◽  
Yi Xin Zhu ◽  
Jian Jun Yang ◽  
Yang Li
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Petri Net ◽  
Flexible Manufacturing ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Manufacturing Cells ◽  
Flexible Manufacturing Cells ◽  
Annealing Algorithm

The production organized in flexible manufacturing cells (FMC) can be a complicated issue when they are constrained by machines, robots, equipment and some other resources. Since machines and robots are the main bottleneck to the efficiency of FMC, this paper focused on the modeling and scheduling problem constrained by machines and robots. A common model representation, colored timed Petri net (CTPN) was utilized to build a FMC model constrained by robots and machines, which was then transformed to the simulation model. The scheduling problem was studied to establish a mathematical model of the FMC constrained by machines and robots. According to the model, a genetic algorithm was proposed to search an optimal solution by using an indirect coding of scheme. The effectiveness of the proposed algorithm was validated via an instance and the comparison with the result from the solution of simulated annealing algorithm.

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

Research on Parallel Test Task Scheduling Based on Improved Genetic Algorithm and Petri Net

2014 ◽  
Vol 543-547 ◽  
pp. 1119-1122
Author(s):  
Pei Pei Chen ◽  
Bao Mei Qiu ◽  
Hao Ba
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Task Scheduling ◽  
Petri Net ◽  
Simulated Annealing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Test Task ◽  
Parallel Test

Parallel test task scheduling is always complex and difficult to optimize. Aiming at this problem, an improved Genetic Simulated Annealing Algorithm based on Petri net is posed to. At first, a Petri net model is established for the system, then the transition sequence is used as task scheduling sequence set path. Genetic Algorithm is introduced in order to get the optimal path. In the process of search, the sequence will be able to stimulate changes as chromosomes, selection, crossover and mutation. In order to prevent premature convergence of the algorithm appears, into the phenomenon of local optimal solution, the individual needs simulated annealing operation, and finally, we can get the shortest time to complete the test task scheduling sequence.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals A mathematical model and simulated annealing algorithm for solving the cyclic scheduling problem of a flexible robotic cell

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775391 ◽  
Author(s):  
Mazyar Ghadiri Nejad ◽  
Hüseyin Güden ◽  
Béla Vizvári ◽  
Reza Vatankhah Barenji
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Cycle Time ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Scheduling Problem ◽  
Robotic Cell ◽  
First Case ◽  
Annealing Algorithm ◽  
Flexible Robotic Cell

Flexible robotic cells are used to produce standardized items at a high production speed. In this study, the scheduling problem of a flexible robotic cell is considered. Machines are identical and parallel. In the cell, there is an input and an output buffer, wherein the unprocessed and the finished items are kept, respectively. There is a robot performing the loading/unloading operations of the machines and transporting the items. The system repeats a cycle in its long run. It is assumed that each machine processes one part in each cycle. The cycle time depends on the order of the actions. Therefore, determining the order of the actions to minimize the cycle time is an optimization problem. A new mathematical model is presented to solve the problem, and as an alternative, a simulated annealing algorithm is developed for large-size problems. In the simulated annealing algorithm, the objective function value of a given solution is computed by solving a linear programming model which is the first case in the literature to the best of our knowledge. Several numerical examples are solved using the proposed methods, and their performances are evaluated.

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