Integration of process planning and production scheduling with particle swarm optimization (PSO) algorithm and fuzzy inference systems

2005 ◽  
Author(s):  
Yahong Yang ◽  
Fuqing Zhao ◽  
Yi Hong ◽  
Dongmei Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Process Planning ◽  
Production Scheduling ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fuzzy Inference Systems ◽  
Swarm Optimization ◽  
Inference Systems

scholarly journals Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jing Li ◽  
Shao-Wu Yin ◽  
Guang-Si Shi ◽  
Li Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Network ◽  
Anfis Model ◽  
Swarm Optimization ◽  
Inference System

The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

scholarly journals Combined particle swarm optimization and fuzzy inference system model for estimation of current-induced scour beneath marine pipelines

2010 ◽  
Vol 13 (3) ◽  
pp. 558-573 ◽  
Author(s):  
M. Zanganeh ◽  
A. Yeganeh-Bakhtiary ◽  
R. Bakhtyar
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Subtractive Clustering ◽  
Scour Depth ◽  
Swarm Optimization ◽  
Inference System ◽  
Input Variables

In this paper the capability of Particle Swarm Optimization (PSO) is employed to deal with an Adaptive Network based Fuzzy Inference System (ANFIS) model's inherent shortcomings to extract optimum fuzzy if–then rules in noisy areas arising from the application of nondimensional variables to estimate scour depth. In the model, a PSO algorithm is employed to optimize the clustering parameters controlling fuzzy if–then rules in subtractive clustering while another PSO algorithm is employed to tune the fuzzy rule parameters associated with the fuzzy if–then rules. The PSO model's objective function is the Root Mean Square (RMSE), by which the model attempts to minimize the error in scour depth estimation with respect to its generalization capability. To evaluate the model's performance, the experimental datasets are used as training, checking and testing datasets. Two-dimensional and nondimensional models are developed such that in the dimensional model the mean current velocity, mean grain size, water depth, pipe diameter and shear boundary velocity are used as input variables while in the nondimensional model the pipe, boundary Reynolds numbers, Froude number and normalized depth of water are set as input variables. The results show that the model provides an alternative approach to the conventional empirical formulae. It is evident that the developed PSO–FIS–PSO is superior to the ANFIS model in the noisy area in which the input and output variables are slightly related to each other.

scholarly journals Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Asif Khan ◽  
Christian Niemann-Delius
Keyword(s):  
Particle Swarm Optimization ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Planning Process ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Open Pit ◽  
Swarm Optimization ◽  
Open Pit Mines ◽  
Hard And Soft Constraints

Determining an optimum long term production schedule is an important part of the planning process of any open pit mine; however, the associated optimization problem is demanding and hard to deal with, as it involves large datasets and multiple hard and soft constraints which makes it a large combinatorial optimization problem. In this paper a procedure has been proposed to apply a relatively new and computationally less expensive metaheuristic technique known as particle swarm optimization (PSO) algorithm to this computationally challenging problem of the open pit mines. The performance of different variants of the PSO algorithm has been studied and the results are presented.

Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

2009 ◽  
Vol 223 (5) ◽  
pp. 485-497 ◽  
Author(s):  
Y W Guo ◽  
A R Mileham ◽  
G W Owen ◽  
P G Maropoulos ◽  
W D Li
Keyword(s):  
Particle Swarm Optimization ◽  
Process Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Operation Sequencing ◽  
Swarm Optimization ◽  
Process Plan ◽  
Five Axis ◽  
Modified Pso

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem.

General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

2010 ◽  
Vol 118-120 ◽  
pp. 409-413
Author(s):  
Shao Tan Xu ◽  
Xin Yu Li ◽  
Liang Gao ◽  
Yi Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Process Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Varying ◽  
Step Size ◽  
Swarm Optimization ◽  
Planning And Scheduling ◽  
System A

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

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