scholarly journals An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu ◽  
Yangbing Xu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Fuzzy Process ◽  
Short Period ◽  
Mutation Strategies ◽  
Encoding Method ◽  
Machine Replacement ◽  
Crossover And Mutation

The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).

Integrated Process Planning and Scheduling Based on Genetic Algorithms

2011 ◽  
Vol 291-294 ◽  
pp. 331-334
Author(s):  
Jin Feng Wang ◽  
Shi Jie Li ◽  
Shun Cheng Fan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Process Planning ◽  
Manufacturing System ◽  
Simulation Studies ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Mutation Operators ◽  
Crossover And Mutation

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

scholarly journals Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Yangbing Xu ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Process Planning ◽  
Imperialist Competitive Algorithm ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Machine Breakdown ◽  
Competitive Algorithm ◽  
Effectiveness And Efficiency

Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

2014 ◽  
Vol 538 ◽  
pp. 193-197
Author(s):  
Jian Jiang Su ◽  
Chao Che ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Layout Design ◽  
Adaptive Genetic Algorithm ◽  
Local Optimality ◽  
Mutation Probability ◽  
Evolutionary Genetic ◽  
Local Optimal Solution ◽  
The Moment ◽  
Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

An optimization model for workgroup-based repetitive scheduling

2006 ◽  
Vol 33 (9) ◽  
pp. 1172-1194 ◽  
Author(s):  
Rong-yau Huang ◽  
Kuo-Shun Sun
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Construction Scheduling ◽  
Chromosome Representation ◽  
Scheduling Method ◽  
Model Formation ◽  
System Project

Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently, Huang and Sun (2005) developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.

Genetic Algorithms Study in Switch Electrical Appliances Electric Arc Feature Extraction

2013 ◽  
Vol 365-366 ◽  
pp. 165-169
Author(s):  
Jing Sheng Yu ◽  
Li Li ◽  
Ting Liu
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Optimal Solution ◽  
Electric Arc ◽  
Network Computing ◽  
Electrical Arc ◽  
Pool Formation ◽  
Extraction Model ◽  
Crossover And Mutation

The genetic algorithm applied to switch electrical appliances electric arc feature extraction, based on genetic algorithm, the switch electrical arc feature extraction model was established. The initial pool formation, evaluation individual, reproduction, crossover and mutation have done a detailed representation. This model can eliminate the slow convergence and so easy to fall into the local minimum shortcomings of BP neural network computing graphics weights. The experiment showed that genetic algorithm can better converge to the global optimal solution, more in line with the arc Feature Extraction fact, and more effectively improving the quality of graphics extraction.

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