TransP (version 0.1)

nwc: Implements North-West Corner Algorithm to solve transportation problem

Description

This function implements North-West Corner Algorithm to solve transportation problem by optimized cost matrix and total optimized cost

Usage

# Get optimized cost matrix for input matrix ex_matrix
nwc(ex_matrix)

Arguments

ex_matrix

A cost matrix where last column must be the supply and last row must be the demand. Input matrix should not have any missing values (NA), otherwise function will throw an error.

Value

A List which contrains the Cost allocation matrix and the total optimized cost.

Details

This function takes a cost matrix (with Supply and Demand) and using North-West Corner approach gives the cost allocation matrix as well as the calcualted optimized cost. This function checks for degenerated problem but it can't resolve it. User need to resolve by seeing the cost allocation matrix.

Examples

## Not run: 
# 
# #Input matrix where last row is the Demand and last column is the Supply
# ex_matrix=data.frame(M1=c(13,10,25,17,210),M2=c(25,19,10,24,240),
#                      M3=c(8,18,15,18,110),M4=c(13,5,14,13,80),M5=c(20,12,18,19,170),
#                      Supply=c(430,150,100,130,810),
#                      row.names = c("W1","W2","W3","W4","Demand"))
# 
# ex_matrix
#          M1  M2  M3 M4  M5 Supply
# W1      13  25   8 13  20    430
# W2      10  19  18  5  12    150
# W3      25  10  15 14  18    100
# W4      17  24  18 13  19    130
# Demand 210 240 110 80 170    810
# 
# nwc(ex_matrix)
# $Alloc_Matrix
#     M1  M2  M3 M4  M5
# W1 210 220   0  0   0
# W2   0  20 110 20   0
# W3   0   0   0 60  40
# W4   0   0   0  0 130
# 
# $Total_Cost
# [1] 14720
# 
# ## End(Not run)