Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study.

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Proposed Model for Optimizing Production System Using CMS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.673 ◽

2013 ◽

Vol 281 ◽

pp. 673-676 ◽

Cited By ~ 2

Author(s):

Pawan Kumar Arora ◽

Abid Haleem ◽

M.K. Singh ◽

Harish Kumar

Keyword(s):

Cell Formation ◽

Flow Time ◽

Mean Flow ◽

Part Families ◽

Mathematical Mode ◽

Mean Flow Time ◽

Proposed Model ◽

The Mean ◽

Machine Parts ◽

Machine Cell

Manufacturing cells are created by grouping the parts that are produced into families. This is based on the operation required by the parts. These cells which consist of machine or workstation are then physically grouped together and dedicated to producing these part families. In this paper a mathematical mode is presented to grouping the machine parts and machine cell. The objective of the proposed model is to minimize the mean flow time and maximize the throughput. This work presents a Genetic Algorithm for the cell formation and part family.Here, the implementation procedure of GA in the CMS problem has been discussed along with the detail of algorithmic parameters used in the algorithm

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Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2012.06.004 ◽

2013 ◽

Vol 32 (1) ◽

pp. 78-84 ◽

Cited By ~ 67

Author(s):

Mohammad Kazem Sayadi ◽

Ashkan Hafezalkotob ◽

Seyed Gholamreza Jalali Naini

Keyword(s):

Discrete Optimization ◽

Optimization Problems ◽

Cell Formation ◽

Manufacturing Cell ◽

Discrete Optimization Problems

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Cell Formation and Part Family Identification by Using Traditional Methods

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.854.121 ◽

2016 ◽

Vol 854 ◽

pp. 121-126 ◽

Cited By ~ 1

Author(s):

M. Shunmuga Sundaram ◽

V. Anbumalar ◽

P. Anand ◽

B. Aswinkumar

Keyword(s):

Manufacturing Systems ◽

Cellular Manufacturing ◽

Rank Order ◽

Cell Formation ◽

Second Phase ◽

Single Linkage ◽

Part Family ◽

Part Families ◽

Machine Cell ◽

Combined Algorithm

A Combined Algorithm is proposed to form the machine cell and part family identification in the cellular manufacturing system. In the first phase, part families identification, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC). In second phase, Machine cell formation, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC), which is to assign machines into machine cells to produce part families. The above Proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is one of the most widely used measures of quality for Cellular Manufacturing Systems.

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A novel hybrid algorithm for manufacturing cell formation problem

Expert Systems with Applications ◽

10.1016/j.eswa.2019.06.019 ◽

2019 ◽

Vol 135 ◽

pp. 327-350 ◽

Cited By ~ 4

Author(s):

Milos Danilovic ◽

Oliver Ilic

Keyword(s):

Hybrid Algorithm ◽

Cell Formation ◽

Cell Formation Problem ◽

Manufacturing Cell ◽

Formation Problem

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Nondegenerate Piecewise Linear Systems: A Finite Newton Algorithm and Applications in Machine Learning

Neural Computation ◽

10.1162/neco_a_00241 ◽

2012 ◽

Vol 24 (4) ◽

pp. 1047-1084 ◽

Cited By ~ 2

Author(s):

Xiao-Tong Yuan ◽

Shuicheng Yan

Keyword(s):

Linear Systems ◽

Optimization Problems ◽

Piecewise Linear ◽

Optimization Methods ◽

Coefficient Matrix ◽

Learning Problems ◽

Support Vector ◽

Data Sets ◽

Piecewise Linear Systems ◽

Vector Machines

We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008 ), and support vector machines (Cortes & Vapnik, 1995 ). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems.

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