This REPO contains source code (in -mod--Rank-order-clustering/ROC/R/ROC.R) as well as package file (ROC_0.1.0.zip file in root) for Modified & Rank Order clustering. This package contains two functions ROC() and MROC(). ROC function runs based on algorithm proposed by King in 1980. MROC function is based on the algorithm proposed by Amruthnath in 2016. The package includes documentation.
The following package has been tested by installing them directly through R
# install devtools
install.packages("devtools")
# load library
library(devtools)
# install MROC package
devtools::install_github("nagdevAmruthnath/Modified-Rank-Order-Clustering")
# Load ROC library
library(ROC)
# A function to create a random data matrix
# Reference: http://dirk.eddelbuettel.com/blog/2012/09/02/#faster_binomial_matrix_creation
createData = function(N, K) {
matrix(rbinom(N * K, 1, 0.5), ncol = K, nrow = N)
}
# use above function to create 10 x 10 matrix
data = as.data.frame(createData(10,10))
# view that random data
data
# V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
# 1 0 1 0 0 0 1 1 0 0 1
# 2 0 0 0 0 1 0 0 0 1 0
# 3 0 0 1 0 0 1 0 1 0 1
# 4 1 0 1 1 0 0 0 0 0 1
# 5 0 1 1 0 1 0 1 1 0 1
# 6 1 0 1 1 1 0 1 1 0 1
# 7 1 1 0 0 1 1 0 0 0 0
# 8 0 0 1 0 1 0 1 0 0 0
# 9 1 1 1 0 1 0 1 1 0 1
# 10 0 1 1 0 0 1 1 1 1 0
This performs Rank Order Clustering matrix for production flow analysis. The result matrix can be used to cluster machines and part numbers
ROC(x)
x is a data frame without column names or row names. Columns are partnumbers and rows are machines
This function takes any number of machines or partnumbers to generate a Rank Order Clustering Matrix. This algorithm is based on Kings (1981) algorithm for production flow analysis. Maximum iterations are set to 50.
result is a data frame consisting of final matrix from the algorithm
This algorithm is tested out for upto 100 x 100 matrix
# load ROC library
library(ROC)
# run ROC algorithm
ROC(data)
# 1 5 2 3 10 7 8 6 4 9 V11
# 9 1 1 1 1 1 1 1 0 0 0 1016
# 7 1 1 1 0 0 0 0 1 0 0 900
# 6 1 1 0 1 1 1 1 0 1 0 890
# 4 1 0 0 1 1 0 0 0 1 0 610
# 5 0 1 1 1 1 1 1 0 0 0 504
# 8 0 1 0 1 0 1 0 0 0 0 336
# 2 0 1 0 0 0 0 0 0 0 1 257
# 10 0 0 1 1 0 1 1 1 0 1 221
# 1 0 0 1 0 1 1 0 1 0 0 180
# 3 0 0 0 1 1 0 1 1 0 0 108
# 11 960 952 806 757 739 694 677 263 192 12 NA
This function provides the Modified Rank Order Clustering result matrix. This can be used to cluster the machines and components.
MROC(data, compWeight, machineWeight)
data: a data frame without row names and column names. Just include the machine component matrix
compWeight: a single data frame containing numerical component weighs
machineWeight: a single data frame containing numerical machine weighs
This function is built for product flow in cellular manufacturing. This function is built based on Modified Rank Order Clustering proposed by Amruthnath and Gupta (2016). This algorithm is an extension of Rank Order Clustering but, it uses weight to reorder the initial matrix first.
Value
result: MROC matrix as a data frame
# create random 10 weights for components
compWeight = sample(1:100, 10)
compWeight
# [1] 100 74 50 85 7 46 87 92 36 59
# create random 10 weights for machines
machineWeight = sample(1:100, 10)
machineWeight
# [1] 98 37 48 50 15 23 18 19 77 2
# run MROC algorithm with weights
MROC(data, compWeight , machineWeight)
# X5 X9 X2 X1 X6 X3 X7 X8 X10 X4 V11
# 2 1 1 0 0 0 0 0 0 0 0 768
# 7 1 0 1 1 1 0 0 0 0 0 657
# 9 1 0 1 1 0 1 1 1 1 0 631
# 5 1 0 1 0 0 1 1 1 1 0 630
# 6 1 0 0 1 0 1 1 1 1 1 623
# 8 1 0 0 0 0 1 1 0 0 0 580
# 10 0 1 1 0 1 1 1 1 0 0 470
# 3 0 0 0 0 1 1 0 1 1 0 226
# 1 0 0 1 0 1 0 1 0 1 0 180
# 4 0 0 0 1 0 1 0 0 1 1 105
# 11 1008 520 458 417 270 253 250 236 231 33 NA
J. R. KING (1980), Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm International Journal of Production Research Vol. 18 , Iss. 2,1980
Amruthnath, N., & Gupta, T. (2016). Modified Rank Order Clustering Algorithm Approach by Including Manufacturing Data. IFAC-PapersOnLine, 49(5), 138-142.