Reconfiguration Process Design of a Reconfigurable Manufacturing System Using Particle Swarm Optimization

2006 ◽  
Author(s):  
Yasuhiro Yamada ◽  
Jun Lei
Keyword(s):  
Particle Swarm Optimization ◽  
Process Design ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Reconfigurable Manufacturing ◽  
Reconfigurable Manufacturing System ◽  
Swarm Optimization

scholarly journals Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems

2009 ◽  
Vol 7 (01) ◽  
Author(s):  
H. Rezazadeh ◽  
M. Ghazanfari ◽  
S. J. Sadjadi ◽  
Mir.B. Aryanezhad ◽  
A. Makui
Keyword(s):  
Linear Programming ◽  
Particle Swarm Optimization ◽  
Production Planning ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Particle Swarm ◽  
Cellular Manufacturing System ◽  
Swarm Optimization ◽  
Integer Variables ◽  
Proposed Model

The concept of virtual cellular manufacturing system (VCMS) is finding acceptance among researchers as an extension to group technology. In fact, in order to realize benefits of cellular manufacturing system in the functional layout, the VCMS creates provisional groups of resources (machines, parts and workers) in the production planning and control system. This paper develops a mathematical model to design the VCMS under a dynamic environment with a more integrated approach where production planning, system reconfiguration and workforce requirements decisions are incorporated. The advantages of the proposed model are as follows: considering the operations sequence, alternative process plans for part types, machine timecapacity, worker time‐capacity, cross training, lot splitting, maximal cell size, balanced workload for cells and workers. An efficient linear programming embedded particle swarm optimization algorithm is used to solve the proposed model. The algorithm searches over the 0‐1 integer variables and for each 0‐1 integer solution visited; corresponding values of integer variables are determined by solving a linear programming sub‐problem using the simplex algorithm. Numerical examples show that the proposed method is efficient and effective in searching for near optimal solutions.

Particle Swarm Optimization Using Various Inertia Factor Variants

2011 ◽  
Vol 460-461 ◽  
pp. 54-59
Author(s):  
Jun Tang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of manufacturing systems. Three different inertia factor, a constant inertia factor (CIF), time-varying inertia factor (TVIF), and global-local best inertia factor (GLbestIF), are considered with the particle swarm optimization(PSO) algorithm to analyze the impact of inertia factor on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia factor separately to compute the optimal control of the manufacturing system, and it is observed that the PSO with the proposed inertia factor yields better resultin terms of both optimal solution and faster convergence. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

An Improved Particle Swarm Optimization Approach for Solving Machine Loading Problem in Flexible Manufacturing System

2015 ◽  
Vol 14 (03) ◽  
pp. 167-187 ◽  
Author(s):  
Ruchir Santuka ◽  
Siba Sankar Mahapatra ◽  
Prasant Ranjan Dhal ◽  
Antaryami Mishra
Keyword(s):  
Particle Swarm Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Particle Swarm ◽  
Computational Effort ◽  
Swarm Optimization ◽  
Machine Loading ◽  
Loading Problem ◽  
Chaotic Numbers

Machine loading problem in flexible manufacturing system is considered as a vital pre-release decision. Loading problem is concerned with assignment of necessary operations of the selected jobs to various machines in an optimal manner to minimize system unbalance under technological constraints of limited tool slots and operation time. Such a problem is combinatorial in nature and found to be NP-hard; thus, finding the exact solutions is computationally intractable and becomes impractical as the problem size increases. To alleviate above limitations, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to solve the machine loading problem. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast toward global optimum and hence reduce computational effort further. Twenty benchmark problems available in open literature have been solved using the proposed heuristic. Comparison between the results obtained by the proposed heuristic and the existing methods show that the results obtained are encouraging at significantly less computational effort.

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