Add files via upload

Distance Matrix.csv

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+Source,Destination,Distance
+Liverpool,Newcastle,178
+Liverpool,Birmingham,99
+Liverpool,London,221
+Liverpool,Exeter,251
+Brighton,Newcastle,345
+Brighton,Birmingham,176
+Brighton,London,65
+Brighton,Exeter,206
+Newcastle,Carlisle,60
+Newcastle,Darlington,37
+Newcastle,Sheffield,130
+Newcastle,Cambridge,236
+Newcastle,Oxford,258
+Newcastle,Truro,458
+Birmingham,Carlisle,193
+Birmingham,Darlington,172
+Birmingham,Sheffield,91
+Birmingham,Cambridge,99
+Birmingham,Oxford,79
+Birmingham,Truro,249
+London,Carlisle,307
+London,Darlington,249
+London,Sheffield,168
+London,Cambridge,58
+London,Oxford,56
+London,Truro,284
+Exeter,Carlisle,346
+Exeter,Darlington,354
+Exeter,Sheffield,251
+Exeter,Cambridge,248
+Exeter,Oxford,163
+Exeter,Truro,90

Supply Chain Network Optimization.R

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+#
+# SUPPLY CHAIN NETWORK OPTIMIZATION
+
+# Coursera Guided Project
+# Instructor - Moses Gummadi
+
+# We use R Library "lpSolve API" for Optimization
+# Reference - https://cran.r-project.org/web/packages/lpSolveAPI/index.html
+
+# This is an R Script file
+# You can open this in R Studio
+# Use # symbol for comments in R Script file
+# The script file contains code and comments
+# You can run the code by typing it next to ">" symbol
+#   in the Console window (bottom left)
+# You can also run the code by selecting the code in
+#   the script file and hit "Run" in the top-left pane
+
+# R Studio is already downloaded in your Rhyme Desktop
+# To download on your own desktop
+# Use this link https://rstudio.com/products/rstudio/
+# Choose the RStudio Desktop Free Option
+
+# ---------------------------------------------------------
+# Task 1 - Introduction
+# ---------------------------------------------------------
+
+# R lpSolve API Optmization Steps:
+#    Determine Number of Variables, Assign Names
+#    Define & Add Objective Function 
+#    Determine & Add The Constraints
+#    Solve The Optimization Problem
+#    Obtain & Export The Solution
+
+# Add Libraries
+check = c("lpSolveAPI","data.table") %in% installed.packages()
+if(!check[1]) install.packages("lpSolveAPI")
+if(!check[2]) install.packages("data.table")
+library(lpSolveAPI); library(data.table)
+
+#setwd("~/Documents/Coursera Instructor/Optimisation")
+setwd("C:/Users/r0869080/Downloads")
+
+# Distance Matrix
+Dist = fread("Distance Matrix.csv")
+Dist$Index = 1:nrow(Dist)
+
+# Factories & Capacities
+FT = data.table(Factory = unique(Dist$Source)[1:2],
+                Capacity = c(150000, 200000))
+
+# Distribution Centres & Throughputs
+DC = data.table(
+  DistCentre = unique(Dist$Source)[3:6],
+  Throughput = c(70000, 50000,100000,40000))
+
+# Customers & Demands
+CT = data.table(Customer = unique(Dist$Destination)[5:10],
+                Demand = c(50000,10000,40000,35000,60000,20000))
+
+print(Dist)
+print(FT)
+print(DC)
+print(CT)
+
+# Exercise - run the above code
+
+
+# ---------------------------------------------------------
+# Task 2 - Problem Formulation & Objective Function
+# ---------------------------------------------------------
+
+# lpSolve Object (0 constraints, nrow(Dist) variables)
+m <- make.lp(0,nrow(Dist))
+
+# Cost Function - Minimize Cost = Qty Shipped x Distance
+# set.objfn(model, vector of coefficients of all Xs)
+set.objfn(m,Dist$Distance)
+
+# Exercise 1: Run the Above Code
+
+# Exercise 2: 
+# Display the Customer Demand (figures only)  
+# Use the $ to get the column from dataframe
+
+CT$Demand
+
+
+# ---------------------------------------------------------
+# Task 3 - Factory Capacity Constraints
+#         Qty leaving Factory <= Capacity
+# ---------------------------------------------------------
+
+# add.constraint(model, coeff, sign, rhs, indexes)
+
+xcoef = rep(1, nrow(Dist[Source == "Liverpool"]))
+xpos   = Dist[Source == "Liverpool"]$Index
+xrhs  = FT[Factory == "Liverpool"]$Capacity
+add.constraint(m,xcoef,"<=", xrhs, xpos)
+
+xcoef = rep(1, nrow(Dist[Source == "Brighton"]))
+xpos   = Dist[Source == "Brighton"]$Index
+xrhs  = FT[Factory == "Brighton"]$Capacity
+add.constraint(m,xcoef,"<=", xrhs, xpos)
+
+
+# Exercise 1: Run the Above Code
+
+# Exercise 2: 
+# Subset "Dist" by Source = "Newcastle"
+# Extract index values for the above
+
+Dist[Source == "Newcastle"]
+Dist[Source == "Newcastle"]$Index
+
+# ---------------------------------------------------------
+# Task 4 - Distribution Centre Constraints 
+#          Qty out of DC - Qty into DC = 0
+#          Qty out of DC <= DC Throughput Limit
+# ---------------------------------------------------------
+
+# Qty out of DC - Qty into DC = 0
+for(i in DC$DistCentre) {
+  xcoef = c(rep(1, nrow(Dist[Source == i])),
+            rep(-1, nrow(Dist[Destination == i])))
+  xpos  = c(Dist[Source == i]$Index,
+            Dist[Destination == i]$Index)
+  add.constraint(m,xcoef,"=", 0, xpos)
+}
+
+# Qty out of DC <= DC Throughput Limit
+# Do it in the exercise
+
+# Exercise 1: Run the Above Code
+
+# Exercise 2: Add the 2nd constraint:
+# Qty out of DC <= DC Throughput Limit
+
+for(i in DC$DistCentre) {
+  xcoef = rep(1, nrow(Dist[Source == i]))
+  xpos   = Dist[Source == i]$Index
+  xrhs  = DC[DistCentre == i]$Throughput
+  add.constraint(m,xcoef,"<=", xrhs, xpos)
+}
+
+
+
+
+# ---------------------------------------------------------
+# Task 5 - Customer Demand Constraints
+#          Qty into Customer = Demand
+#          Integer Constraint
+# ---------------------------------------------------------
+
+# Qty into Customer = Demand
+for(i in CT$Customer) {
+  xcoef = rep(1, nrow(Dist[Destination == i]))
+  xpos   = Dist[Destination == i]$Index
+  xrhs  = CT[Customer == i]$Demand
+  add.constraint(m,xcoef,"=", xrhs, xpos)
+}
+
+# Exercise 1: Run the Above Code
+
+# Exercise 2: Apply "integer" constraints 
+# Use set.type(model, variable indexes, "integer")
+
+set.type(m, Dist$Index,"integer")
+
+# ---------------------------------------------------------
+# Task 6 - Run Optimiser, Obtain & Analyse Solution
+# ---------------------------------------------------------
+
+# Solve & Get Solution
+x = proc.time()
+lp.control(m, timeout = 10)
+
+solve(m)
+proc.time() - x
+
+get.objective(m) 
+
+out = get.variables(m)
+Dist$Qty = out
+
+Dist[Qty != 0]
+
+# Exercise 1: Run the Above Code
+
+# Exercise 2: 
+# Change Factory Capacities To
+# Liverpool = 250,000
+# Brighton = 100,000
+# And run the solver again
+
+
+
+# ---------------------------------------------------------
+# End of Guided Project - Thank You
+# ---------------------------------------------------------