complete_project

ABSTRACT

The purpose of this research is to determine the performance of a

Logistics Network using the techniques of Artificial Neural Network. By

analyzing the performance and comparing the predicted values to the

industry standards of the company, the success of a Logistics Network

can be predicted. In the first segment we work with a sample Network

matrix and find out the shortest cost-distance path. Then we try to find

any similarity for relationship between them. In the second segment we

work with a more complicated involving large number of parameters

affecting the Logistics Network and try to find the relationships. Once the

network is initialized, prediction is made which is then compared to the

industry data & tradeoffs. The computing is done with MATLAB and

various plots are generated. We also looked in the theoretical aspects

involving this project research while studying neural networks in detail.

The project completed with a sense that given sufficient historical data

neural networks can be used to make intelligent predictions for various

purposes including Logistics Network.

KEYWORDS

Logistics Network

Artificial Neural Network

Artificial Intelligence

Business Predictions

MATLAB

Supply Chain Management

Feed-forward Back-propagation Algorithm

PREFACE

This project report is being formulated and prepared by me on the basis

of my two year Master in Business Administration ( MBA ) degree course

in Indian Institute of Engineering Science & Technology, Shibpur during

the term 2014 - 2016. This project has been done in requirement to the

partial fulfilment of the Master’s degree. During the period, I had 24

papers with 6 papers per semester in addition to the SIP, Assignments &

other projects. This entire research work is prepared by me with the help

of the experience & knowledge gathered during the course duration.

Besides enhancing the subjective knowledge, the MBA course had helped

me to make out the rules and regulations of the business world, the

industry standards followed by the experts and techniques to manipulate

& run the business world. All these have made the course worthy.

As I am having a technical background of Electronics & Communication

Engineering, I wanted to do my project where I could apply by technical

knowledge as well as implement by newly learnt management

methodologies. Hence, I decided on the topic “ Predicting Success of a

Logistics Network using Artificial Neural Network “ which looks into the

ways we could predict a future outcome based on historical data from

past businesses. This project has been very instrumental in sharpening

my management knowledge and made me feel refreshed with

information and new ideas upon completion.

..............................................................

Date: ...................................................

ANIRBAN ROY

School of Management Sciences

Indian Institute of Engineering Science & Technology

ACKNOWLEDGEMENT

I, the undersigned, student of School of Management Science ( SOMS ) of

Indian Institute of Engineering Science & Technology, Shibpur would like

to thank the institute for giving me the opportunity to study in this

prestigious university.

Besides my parents, I would like to thank my teachers for giving me all the

knowledge, advices & instructions for these two years. I believe that the

knowledge and professional expertise I acquired in these two years would

help me rest of my life to lead a better life. I am indebted to them.

Next, I would also extend my sincere gratitude to library madam,

computer madam and supporting staffs for giving me all the support

necessary for the course duration. Without them, it would not have been

so easy.

Next, I would like to thank our Director, Mr. Ajoy Kumar Ray & SOMS

Director Mr. P.K. Paul for supporting us whenever possible and helping us

achieve best possible education.

Finally, I would like to give sincere respect to all the authors, researchers

& scholars whose work helped me in completing my project work. I would

like to thank Wikipedia for sharing us the vast resource of knowledge and

the creators of MATLAB for giving the world a powerful tool which

simplified my tasks to a great extent.

..............................................................

Date: ...................................................

ANIRBAN ROY

School of Management Sciences

Indian Institute of Engineering Science & Technology

CERTIFICATE

CONTENTS

Title / Subject Matter Page

1. INTRODUCTION ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... 1

2. ARTIFICIAL NEURAL NETWORK ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... . 1

a. Learning Rule 3

b. Neural Network Model 4

c. Neural Networks in Business 6

d. Neural v/s Logistics Network 7

3. LOGISTICS NETWORK ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... . 8

a. Types of Logistics 8

b. Nodes of Logistics Network 8

c. Network Optimization 9

d. Network Optimization Algorithm 9

e. Conclusion 9

4. RESEARCH METHODOLOGY ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 10

5. RESEARCH IMPLEMENTATION ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 11

• Shortest Path Rule 12

• Sample Example 1 13

• Example 1 Coding 15

• Example 1 Neural Network 16

• Sample Example 2 21

• Example 2 Neural Network 23

6. CONCLUSION ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 25

7. RECOMMENDATIONS ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 26

8. REFERENCES ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... . 27

9. APPENDIX ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... . 28

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1 Predicting Success of Logistics Network using Artificial Neural Network

1. INTRODUCTION

In this era of 21st century every business

entity would want to know if their business

plan would succeed in the future. And, the

survival of the company can depend upon

the business plan executed. Not only so,

the quality of execution, the time of

delivery, nature of strategic steps and also

the post plan execution steps are also

important. Logistics and supply chain

management is an integral part of any

business organization. Logistics, in short is

all the operations in the process of flow of

materials from the suppliers to the

customers or the point of origin to the

point of consumption. A good logistics

network ensures that the required

resources are available at the required

place at highest possible quality and at

lowest possible cost. Investors and

businesses alike use several methods and

tools to evaluate a business plan to

measure its future impact even before its

execution.

Integrating a model that takes into

account both the quantitative and

qualitative regression model is quite

tremendous task as there are a lot of

variables that could affect the business

process or the business as a whole. There

is also a limitation to ANN which is that

this model needs sufficient base or

historical data to make appropriate

computations.

In corporate business world, Neural

Networks can be used to study about

stock markets (predictions of stock market),

marketing of a product (acceptance of a

product by customer in a target market,

longevity of a product, product development

cycle), performance appraisal, financial

assessments, etc. The list goes on and on.

In short it can be inferred that ANN model

can be used to solve practically any sort of

complex problems in all the fields of

management sciences starting from

Finance to Human Resource and

Marketing. It can also be employed at

production sites and assembly lines to

increase product quality & reduce wastage

of time and resource. Hence, fairly good

predictions can be made by managers

based on it.

Our objective is to study a logistics

network & analyze it using algorithms to

find shortest cost path and the shortest

distance. We also make predictive

assessment of a multi-variable logistics

network, which is later used to determine

its success or failure. Finally, based on the

analysis it can be understood if the

network needs to be optimized. But, the

study can be further done to discover the

weak links in a network & provide a

mechanism through which automated

adaptation to logistics network takes

place. MATLAB is used for programming

and generating the processed data. The

project work ends with stating the

limitations and illuminating the future

study which can be done.

2. ARTIFICIAL NEURAL NETWORK

To study in detail how is ANN used to

predict possible outcomes, we must first

know perfectly well about ANN. Wikipedia

defines Artificial Neural Network (ANN) as

follows:-

Artificial Neural Network is a model used to

estimate or approximate functions that can

depend on a large number of inputs and are

generally unknown. Artificial neural networks

are generally presented as systems of

interconnected "neurons" which exchange

messages between each other. The connections

have numeric weights that can be tuned based

on experience, making neural nets adaptive to

inputs and capable of learning.

Artificial Neural Network is basically a

model which predicts an outcome based

2 | P a g e


on permutations, combination and

probability. And, with the presence of large

volume of data (which acts as a random

data having high credibility) near perfect

assumptions can be made using this

model. ANN is a continuous learning

model which means that this model

continuously evolves with time thereby

decreasing the deviations.

In terms of logic, ANN consists of

interconnected nodes. Neural Networks

got its name from the biological neural

networks of brains. Just like the neural

chain, ANN consists of node points which

are connected together to form an input-

output relationship. In case of complex

ANN there may be some transient node

which works as interlink between input

and output nodes.

Fig 1. Artificial Neural Network Model

This transient node is hidden from both

the input and output point of view and it

works as a processing or decision making

node. Its called “Artificial” Neural Network

as it has Artificial Intelligence based on

coding as it is capable of altering itself

based on the inputs and situation

presented. As a result it is adaptive in

nature. A neural network is made up of

simple calculating units called Neurons

that locates in different layers and have a

great many internal connections.

For several decades, forecasting

and prediction has been largely made by

linear simple approaches which are very

simple to understand and implement. But,

these linear methods fail to grasp the non-

linearity of complex variables and their

limitations as a variable. There can be

different cases with respect to variables

such as:

• Some variables can affect a system

more than other variables. These

variables are called mission critical

variables.

• Some variables have negligible or

no effect on a system.

• Sometimes variables remain

constant throughout a cycle while at

others they are random.

A wide plethora of problems has been

successfully solved by applying Artificial

Neural Networks. ANN mostly consists of a

learning rule which governs the process

and enhances the connections between

the nodes based on a pattern. Just like

human brain learns from experiences;

ANNs do the same. Thete are two major

life-cycle of ANN:

� Training Cycle

The process is implemented in ANN &

is fed with random data. The network

learns from their experience which is

used to enhance the connections.

During this period, output is only error

data which is fed back into system to

make it more accurate.

� Run Cycle

Actual running of the system takes

place. Output acquired is of good

quality but even now there is presence

of feedback mechanism to make the

system more robust to variations.

3 | P a g e


2 (a). Learning Rule

There can be different types of learning

rules of Artificial Neural Network which are

used to teach the network to make near

perfect decisions are given below.

� Unsupervised Learning

In case of Unsupervised learning the

system is fed with raw data so that it could

adjust itself with variations. But, this

system of learning lacks second set of data

which can be compared with the outputs

received. So, errors in the system cannot

be detected. The system has to use the

concepts of probability density function to

replicate what exact value would be in the

presence of sufficient amount of data.

Fig 2. Probability Density Function

Probability Density Function tell us about

the relative likelihood for a random

variable to take on a given value. The

standard normal distribution has

probability density

�� = �√� ��/�

When X (random variable) is given and it

has a probability density function f, then

the value of X can be calculated as follows:

�� = � ��

There are also various approaches to

unsupervised learning such as blind signal

separation, clustering, method of

moments, etc; discussing them is out of

the scope of this article.

� Supervised learning

It is the process of learning from

supervised or labelled data which are

otherwise known as training example.

Each training example consists of a input

vector and estimated output vector. When

this key-value pair is fed into the neural

network and output is compared.

According to the variation in the output

result, the processing function or the

algorithm is modified which can be used to

analyze further examples. A good example

of supervised learning in case of human is

concept learning, where teachers provide

the required input vector. Whenever

student deviates from the logic, the

teacher makes necessary corrections. By

following this way repeatedly, the student

learns the concept or the logic.

� Reinforcement learning

In case of Reinforcement learning, a

input/output pair is provided to the

system. But, the difference between

Reinforcement learning and supervised

learning is that the prior never uses the

correct input/output pair nor the correct

response is given any special care. The

system is based on reward system and the

learning agent interacts with the

environment in discrete time duration.

During these discrete time steps, input is

provided to the system and observation is

made. Reward is also given to

acknowledge the observation. The basic

goal of Reinforcement learning is to collect

as many rewards as possible.

4 | P a g e


After running for sufficient amount

of time frame, the past performance is

compared to the present performance.

Hence, comes the concept of regret which

is used to make necessary adjustments to

the system. The reinforcement learning is

used in case of long-term decision versus

short-term decision trade-off. This system

can be used in the following situations:

� No analytic solution is available

which can be applied to the context

� Only the model of the situation is

known

� Only way to get information about

the environment is by simulation or

interacting with the environment

directly

� Need to evaluate effect of long-

term/short-term decisions

2 (b). Neural Network Model

A Neural Network is basically an inter-

connection of layers of nodes. Each

individual connection has some weight

and when input is provided to it, an output

is passed to the next node based on the

connection known as synapse. These

“weights” are the basis of manipulation of

input. During learning mode, these

weights are regularly changed or updated

to match the required output.

Fig 3. Schematic of a Neuron

In the above figure, x1, x2, .... xn are the

inputs from which signal is entering the

neuron. And, each of these inputs carry

respective synaptic weights of w1, w2, .....

wn. Hence, there are n numbers of total

inputs given to the network. Hence, the

total weighted input from all the inputs is:

� = � ��

��

ξ is the excitation level of the neuron,

gives the output y after reaching excitation

level h, which is the maximum excitation

level of the neuron. The level of excitation

h is achieved by activation function σ (sigma).

�� = 1 "# � ≥ ℎ 0 "# � < ℎ (

The above sample function can also be

called as limiting function as it provides

output only two discrete units; one or

zero. So, the complete mathematical

formulation of the artificial neuron is given

by the following expression:

�� = 1 "# � ≥ ℎ 0 "# � < ℎ ( �ℎ)*) � = � �"�"+

"=0

Signal propagation through the neural

network and its manipulation can be done

by either changing either the weights of

the synapse connections or the path

followed by the signal itself. The grouping

of the neurons, synaptic weights and the

activation potential is together known as

the configuration of the network. Other

examples of activation function could be

as follows:

� piece-wise linear function

�� = ,1, � > 1�, 0 ≤ � ≤ 10, � < 0(

5 | P a g e


� hyperbolic function

�� = 1 − )�11 + )�1

� sigmoid function

�� = 11 + )�1

There are two states in which the neuron

could stay. Either the electron is in firing

state or it is in state of rest. The two states

of individual neuron can also be visualized

as a vector containing both magnitude and

direction. The weight can be taken as the

magnitude and the state of electron can

be taken as the direction.

Take an example to clearly

understand the above concept.

x1 w1

c1

x2 w2

c1=x1w1 + x2w2

The output electron is fired only when the

value of c1 is greater than the threshold

value of the network. In the same way

larger complex network can be realized.

Now for the sake of example, let the

Neural Network mimic a ‘AND’ gate. A

hypothetical unknown logic is provided to

the neuron. The truth table of a AND gate

is as follows:

x1 x2 c1

0 0 0

0 1 0

1 0 0

1 1 1

A threshold value of q is required so that

the electron is fired. Imagine in the Neural

Network, x1 and x2 are provided with

certain weight and then random states of

electron are observed. Suppose the

desired output of the network is d and the

real output of the network is y. If the Then

the error e of the network would be as

follows:

e = d – y

Then error function of the network in the

long run would be as follow:

= �� − 3��

The goal of the network is to minimize the

error to the maximum extent possible. In

order to do so, the weights of the network

are regularly updated after each cycle till

the error tends to be zero. Suppose in the

sample example above, both w1 and w2 are

random variable. The network is allowed

to run without any learning process. The

following table is acquired:

x1 |x1| x2 |x2| y |y| d e

0.61 1 0.85 1 0.34 0 1 �

0.84 1 0.59 1 0.95 1 1 --

0.20 0 0.09 0 0.56 1 0 �

0.57 1 0.83 1 0.22 0 1 �

0.12 0 0.31 0 0.69 1 0 �

0.29 0 0.60 1 0.59 1 0 �

0.55 1 0.80 1 0.96 1 1 --

0.34 0 0.53 1 0.30 0 0 --

0.50 1 0.48 0 0.10 0 0 --

0.34 0 0.51 1 0.25 0 0 --

0.50 1 0.63 1 0.34 0 1 �

0.34 0 0.26 0 0.49 0 0 --

0.42 0 0.97 1 0.09 0 0 --

0.43 0 0.79 1 0.56 1 0 �

0.26 0 0.54 1 0.84 1 0 �

0.77 1 0.85 1 0.96 1 1 --

0.76 1 0.38 0 0.37 0 0 --

0.88 1 0.11 0 0.63 1 0 �

0.24 0 0.51 1 0.79 1 0 �

� indicates presence of error

Here the sample size, n = 20. Then,

4 = �� − 3��

�5

6��

4 = �� 7�58

6 | P a g e


Hence, Z = 5. And, the probability of

occurrence of error in this case would be

9�:� = ;<=>?@<. <#BCCD*>+C)<#:**<*EF>GH?)F"I)

J� � = �5�5 = 5. K5 = K5%

Hence, from the above data, it can be

inferred that there is 50% chances of

getting incorrect solution. The above

example is based on simple random

weights and it lacked a learning

mechanism which could train the network

to make better choices with the passing

time.

The above example can be modified

to work as Artificial Neural Network by

training the network to rectify its strategy

whenever it receives incorrect answer

during the training exercise. We assume

that each sample is obtained from each

instance of time period. We need to obtain

a situation where there is output value,

which we know is possible only when the

output is greater than the threshold value.

Suppose, in the n � 0 instance of

time, we get output across the points as:

x1 |x1| x2 |x2| y |y| d e

0.02 0 0.13 0 0.64 1 0 �

Here the desired output is 0 but we get an

error. So, the learning mechanism teaches

the network in that instance d � 1 for

x1=0.02 and x2=0.13.

In the next round, n � 1 instance of

time, we get another output as follows:

x1 |x1| x2 |x2| y |y| d e

0.25 0 0.36 0 0.52 1 0 �

Here also, the desired output is 0 but we

get an error. So, the learns that in that

instance d � 1 for x1=0.25 and x2=0.36. In

the same way it also learns about the

other logic states of ‘AND’ gate and finally

it learns to mimic the truth table. Slowly

the network learns to accept values from

0.50 and above as logical 1 and values

from 0.49 and below as logical 0. It also

learns about what the output values

should be for respective values of input.

The computer modelling of this sample

example is out of bounds of our work. But,

we can understand from this example that

a more complex network works in the

same principle. In the same way it learns

and this auto learning can be referred to

as Artificial Intelligence in computer

terminology.

2 (c). Neural Networks in Business

To fully understand the practical use of

Artificial Neural Network in the field of

business, we must first keep in mind that

the practical simulation of a business

model may not exactly look like a network

of neurons. But, it follows the same

concept of giving weighted inputs to a

network; where network can be seen as an

algorithm or conditions or limitations, and

getting an appropriate result to satisfy the

situation.

In this piece of work, we would look

upon various models and theories which

are actively used to make real-life

decisions in a business environment.

Some examples of Neural Networks in

Business environment would be as

follows:

� Predicting stock market simulations

� Simulate introduction of a new

product in the market

� Properly analyze seasonal

variations

� Analyze customer related data for

making predictions and finding out

similarities

� Project management & formulating

strategies

� Financial and economic forecasting

7 Predicting Success of Logistics Network using

� Solving transportation problem and

simplifying Supply Chain

� Tourism Demand Forecasting in

Tourism Industry

� To predict bankruptcy of a business

� Inventory management to reduce

both inventory levels

� Risk assessment & prediction

� To analyze proper investments of

shares and funds

� Predict and analyze the efficiency of

a logistics or supply chain network

including its cost benefit

The science of Artificial Neural Network is

evolving and a lot of work in this field has

been experimental. As these Neural

Networks can get really a lot complicated

with each addition of nodes and logic used

in the neurons, these neural networks can

only be tested meaningfully using software

codes and logics. Software that has been

used a lot in this field is MATLAB

saw very well that the practical use of

Neural Network is endless in real business

environment. We take an example of

predicting business success by Neural

Networks using MATLAB

algorithms.

2 (d). Neural v/s Logistics Network

There are lot of similarities to a

Network and a Logistics Network

the network is a multi-nodal network. Just

like a Neural Network which consists of

numerous hidden layer nodes, so does the

Logistics Network. For example, the raw

material which is acquired from the

suppliers travels through various means of

transportation to reach the industry and

from there after the product is made again

it travels through various means of

transportation to reach the end users.

And, there are a lot of factors influencing

these nodal points like storage capacity,

Predicting Success of Logistics Network using Artificial Neural Network

ransportation problem and

Supply Chain

Tourism Demand Forecasting in

To predict bankruptcy of a business

Inventory management to reduce

Risk assessment & prediction

To analyze proper investments of

Predict and analyze the efficiency of

a logistics or supply chain network

including its cost benefit

The science of Artificial Neural Network is

evolving and a lot of work in this field has

been experimental. As these Neural

Networks can get really a lot complicated

with each addition of nodes and logic used

in the neurons, these neural networks can

be tested meaningfully using software

codes and logics. Software that has been

MATLAB. So, we

very well that the practical use of

real business

We take an example of

ess success by Neural

MATLAB software

Neural v/s Logistics Network

There are lot of similarities to a Neural

Logistics Network as both of

nodal network. Just

like a Neural Network which consists of

numerous hidden layer nodes, so does the

Logistics Network. For example, the raw

material which is acquired from the

suppliers travels through various means of

ransportation to reach the industry and

from there after the product is made again

it travels through various means of

transportation to reach the end users.

And, there are a lot of factors influencing

these nodal points like storage capacity,

transit duration, cost involved in storage

and movement, etc. The main nodal points

of a logistics network can be abbreviated

as follow:

Fig 4. Abbreviated Logistics / Supply Chain

Network

The Logistics network in terms of nodal

points can be visualized as below

hidden layer consisting of warehouses,

retailers, stockers, etc. And, we see that on

the whole it is a N:N relationship matrix

just like a Neural Network.

Fig 5. Nodal points of a Logistics / Supply Chain

Network

Supplier

Retailer

Customer

7 | P a g e

tion, cost involved in storage

and movement, etc. The main nodal points

of a logistics network can be abbreviated

Abbreviated Logistics / Supply Chain

Network

The Logistics network in terms of nodal

points can be visualized as below with the

hidden layer consisting of warehouses,

retailers, stockers, etc. And, we see that on

the whole it is a N:N relationship matrix

just like a Neural Network.

Nodal points of a Logistics / Supply Chain

Network

Manufacturer

Distributor

Customer

8 | P a g e


3. LOGISTICS NETWORK

To start with this topic, our first and

foremost goal must be to understand the

two terms: Logistics & Supply Chain

Management. Both of these terms are

sometimes used interchangeably. But are

these terms really mean the same? It’s

rather confusing. According to Logistics &

Supply Chain Management by Martin

Christopher:

Logistics is the process of managing the

procurement, movement and storage of

materials, parts and finished inventory and its

marketing channels so profitability are

maximised. Supply Chain Management is the

management of upstream and downstream

relationships with suppliers and customers in

order to deliver superior customer value at less

cost.

The following two terms can further

be simplified by stating exactly what

comes under which term. The components

of Logistics are:

• Customer Service

• Purchasing & Procurement

• Warehouse Management

• Transport

• Production Planning

Likewise, the components of Supply Chain

Management include the following:

• Suppliers

• Manufacturer

• Retailer

• Stocker & Wholesaler

So, Logistics is the smaller domain while

Supply Chain Management is the larger

domain. Supply Chain Management is the

integrating factor of Logistics as it

comprises the Logistics part as well as the

management of all supporting

departments to bring competitive

advantage to a business organization. In

terms of mathematics, we can say that

Logistics is a subset of the bigger SCM.

The main aim of Logistics Management is

customer satisfaction by providing

efficient transportation of resources,

people, information or energy between

two points. A robust Logistics network

helps in timely and cheap transportation

facilities. And; in today’s highly globalized

industry, quick and timely delivery can be

the basis of survival of a company.

3 (a). Types of Logistics

There are basically two types of Logistics

segment which can encompass all

Logistics activities. The two major

segments are:

(i) Inbound Logistics

They describe all logistics activity which

focuses on purchasing and subsequent

inbound activity of the raw materials,

resources or other deliverables into the

manufacturing unit, warehouse, plants or

retail stores.

(ii) Outbound Logistics

In contrast, Outbound Logistics are

activities involved in storage and

subsequent outward movement of the

final product to the customers & end users

through the delivery channel.

3 (b). Nodes of Logistics Network

As described previously, a Logistics

Network resembles closely with a Neural

Network. They both contain nodes which

process the information entering it and

based on certain parameters provide

output or forward the information to the

next node. While a package / deliverable

travels from the start point ( supplier ) to

the end point ( customers or end users ), it

travels through various nodes where they

are either simply stored or further

processed and then forwarded again to

the next node. Each node has its own

prerequisites, capacity and cost involved.

9 | P a g e


In some cases there can be more than one

available routes for the transportation and

it becomes necessary for the business to

opt for most advantageous path for either

quick delivery or cost effective or both. The

nodes of a Logistics network can be:

a) Suppliers of goods or information

b) Factories or plants

c) Transit points

d) Warehouses & Storage Facilities

e) Distribution centers

f) Sales Agents / Brokers

g) Retail points

h) Disposal Centers

i) Customers or End Users

3 (c). Network Optimization

A network be it a Logistics Network or a

Neural Network is optimized to increase its

performance and reduce wastage of

resources whenever possible.

Optimization also leads to better decision

making and agile to fluctuations. Neurons

in our brain optimize themselves by

making new connections based on some

logic and train it for various scenarios.

Likewise a Logistics Network optimizes

itself by using various statistical tools

available to weigh the result in various

scenarios. The parameters of optimization

can be different in different cases such as

cost, distance, probability of fulfilling its

quota, etc.

3 (d). Network Optimization Algorithm

There are various algorithms and

methodologies available to optimize a

network. One of the most commonly used

algorithms is the Minimum Cost Rule

which takes into account the cost

associated in transporting from one

location to another and the path where

the cost is minimum is selected. In this

case, the path selected may be even

longer than some of the other paths or the

time taken by it larger. But, given the

company has sufficient time, it’s the most

cost effective algorithm.

One such other algorithm is the

Shortest Path Rule which takes into

account the shortest path between two

points or nodes. This algorithm is

preferred when the company is working

on tight deadline.

3 (e). Conclusion

It is evident from the from the literature

that to survive the competitive world of

business it is imperative to work on

Logistics network to make process more

cost effective and agile. However, the

Logistics can sometimes be simple costing

of only two nodes: starting node & finish

node. But sometimes it can be very

complex where network analysis becomes

very important and it is where Neural

Network and other algorithms come to

rescue. The parameters affecting the

network and the nodes can be bounded /

unbounded, simple / complex, single /

multi – dimension or small / large. And, as

the number of parameters increase, we

start to get the real picture. The size and

volume of realization becomes impossible

to work out using pen and paper.

Sometimes it becomes even hard to solve

it using traditional computing. It is where

MATLAB comes as a solution of all

problems. It is software which supports

Neural computing by default and easy to

program and create graphical

representation of analyzed data.

The detailed research analysis and

implementation is done in the following

sections working on a sample random

data generated by the software itself

based on a virtual Logistics Network. The

random data can also be replaced by

actual data it the necessity comes. The

paper goes on analyzing the network in

every aspect possible.

10 | P a g e


4. RESEARCH METHODOLOGY

It is known to everyone that the most

important aspect of any Supply Chain as

well as of Procurement process is the

“Delivery Network”, the network of nodes

through which transportation of goods &

materials take place. So by choosing a

network path where either the cost or the

distance is minimum half of the job is

already done. But it lacks the dynamic

character which exists in the real world

(such as fluctuations due to financial /

economic / political environment). So, in

the second part of the problem we work

with approximated dynamic setup with

several input parameters.

Data Source

A research is as good as its data. So, every

possible action was taken to keep the data

authentic without the presence of any

error. The data used in this project work is

randomly generated by the system, i.e.,

the data may be factually incorrect. It is

due to lack of that particular confidential

company data. Utmost care has been

given to keep the values of data within

bounds of the real world. But, these data

can be easily replaced by real world data

values keeping the algorithm and process

exactly the same. Since, the project work

only looks at the basic logistics setup and

only aims at providing working details

rather than specific analysis on a particular

company; so the data generated by the

system is sufficient to study and analyze

the neural network from educational point

of view.

The graphs and illustrations are

generated with the help of MATLAB

software which is best suited for use in

neural networks. The particular module

used is called Neural Network Tool. Only a

few available graph types are generated,

whereas people have the ability to

generate even more types of graphs and

various others cross combinations of data.

Each type of graph has their own

advantages and disadvantages.

Research Approach

Research is totally based on neural

network computing algorithms which are

an aspect of Management Information

System (MIS). MIS uses various categories

of analytical tools to take decisions in a

management situation. The project also

uses software coding algorithms to make

use of the neural network programming

already existing in the MATLAB software

suite. It also includes brain storming

sessions to generate algorithms for

shortest cost path and least distance path.

Testing & Evaluation

In the first part of the Research

Implementation raw data generated from

the shortest cost path is used. In the

second and final part of research, multi-

variable data is used. The accuracy of data

and its results is tested internally by the

software. However, the user has the ability

to determine the number of instances of

stimuli which he / she want to test in the

given neural network setting. The neural

network has three options namely: Train,

Validation and Test. In both validation

phase and test phase, inputs are given to

the neural network and test the

effectiveness of the output data.

So, se see that the project works

only with preliminary aspects of research

in this field. There are numerous scopes in

which the project can be improved. The

project work is an experimental piece of

work on testing effectiveness of neural

network in the field of management

analytics especially, the logistics network

and the supply chain network. In the

coming chapters we dive deep into

research implementation & sample

experimentation work.

11 | P a g e


5. RESEARCH IMPLEMENTATION

To start with, we start with a virtual

Logistics Network. The sample data is

randomly generated. Three matrices are

generated which consists of:

• Distance matrix

• Cost matrix

• Probability matrix

All the three matrices are 10 * 10 matrix

and the data is generated using MATLAB

random function which generates random

array of numbers. The distance matrix

represents the distance between two

nodal points and the cost matrix the cost

associated in transportation and storage

at nodal points. The probability matrix

takes care of the fluctuations and seasonal

variations which may appear in the cost

matrix. Each element of cost matrix is

added with each respective element of the

probability matrix to get the final cost

matrix. The function used to generate the

random data matrix is:

� = ��, �

Here, x and y represents the two

dimensions of the matrix. And, A is a x * y

matrix with all the individual values

between the range 0 and 1. To get the cost

and distance matrix, this function is

multiplied by a factor. For example:

� �� = � ��, � ∗ ��

Here, the Cost Matrix is multiplied by a

factor of 1000 and so all the values will lie

in the range greater than 0 but less than

1000. And, the round function converts the

value to the nearest one eliminating the

point places. Similar to cost matrix,

distance matrix is also generated.

�� = � ��, � ∗ ��

The values of cost matrix are given in INR

Lakhs while the values of distance matrix

are given in Kilometres. All the sample

matrices are given below. A : J and K : T

are the nodal points associated with the

Logistics Network.

A B C D E F G H I J

K 157 876 704 436 137 191 778 302 78 230

L 348 490 572 502 172 681 190 49 46 238

M 403 183 755 866 930 281 115 228 754 824

N 348 686 816 483 280 377 912 902 457 493

O 195 135 406 303 424 874 123 691 315 540

P 448 213 197 90 705 863 813 88 931 593

Q 49 585 545 623 970 426 130 608 536 827

R 449 735 499 349 306 382 123 309 394 465

S 394 929 484 347 166 373 117 217 946 122

T 920 50 578 226 856 921 281 642 449 114

Table 1. Cost Matrix of the Network ( in INR Lakhs )

A B C D E F G H I J

K 9902 8118 8714 4838 3715 7051 2264 4566 7323 7646

L 4050 3148 9050 8258 2579 2134 6258 7913 7862 2632

M 7632 5988 7207 6772 4614 8970 985 7752 6377 8345

N 3279 409 149 4312 1329 1167 1350 1649 1923 3983

O 6708 7000 5684 8071 447 8622 5591 3437 1225 4282

P 4008 9132 9210 978 2158 9716 59 987 4077 2300

Q 156 8180 8874 9058 7692 7317 1918 9327 2092 9023

R 5643 3793 9421 7838 3439 6654 8306 9531 7556 3602

S 6185 1514 8880 4199 6187 1339 7752 6814 9315 8787

T 1989 5090 883 7231 3419 8590 8974 9108 8724 501

Table 2. Distance Matrix of the Network ( in Kilometres )

12 | P a g e


A B C D E F G H I J

K 0.3577 0.4666 0.0629 0.0766 0.8989 0.9874 0.8403 0.2986 0.2891 0.8670

L 0.6813 0.9738 0.2661 0.3828 0.2271 0.8583 0.3547 0.1322 0.3943 0.5605

M 0.5432 0.0786 0.6342 0.7042 0.6898 0.2217 0.0438 0.0317 0.2321 0.6050

N 0.9957 0.6954 0.0035 0.8875 0.8866 0.8612 0.0489 0.4074 0.3844 0.8173

O 0.9386 0.1575 0.5219 0.9384 0.5309 0.1954 0.2673 0.9402 0.7235 0.2163

P 0.1314 0.1394 0.0279 0.4660 0.8255 0.0643 0.9378 0.6046 0.3836 0.6502

Q 0.9802 0.9147 0.5725 0.9511 0.2246 0.7436 0.7786 0.1367 0.3118 0.7136

R 0.6398 0.1863 0.5436 0.8118 0.2646 0.0592 0.4683 0.8497 0.2046 0.2442

S 0.4832 0.8941 0.4781 0.7825 0.7164 0.4231 0.1120 0.7354 0.2084 0.5437

T 0.7584 0.4073 0.6258 0.7982 0.8239 0.3393 0.5348 0.9655 0.7821 0.7647

Table 3. Probability / Noise Matrix

�� ( , �) = �� ( , �) + ��(�� ( , �) ∗ ��)

A B C D E F G H I J

K 193 923 710 444 227 290 862 332 107 317

L 416 587 599 540 195 767 225 61 85 294

M 457 191 818 936 999 303 119 231 777 885

N 448 756 816 572 369 463 917 943 495 575

O 289 151 458 397 477 894 150 785 387 562

P 461 227 200 137 788 869 907 148 969 658

Q 147 676 602 718 992 500 208 622 567 898

R 513 754 553 430 332 388 170 394 414 489

S 442 1018 532 425 238 415 128 291 967 176

T 996 91 641 306 938 955 334 739 527 190

Table 4. Actual Cost Matrix ( in INR Lakhs )

We take the above example and try to

implement the shortest path algorithm to

the cost matrix as well as the distance

matrix. By applying the algorithm to cost

matrix we get the least coast involved in

the transportation and the route through

which it needs to be travelled while in case

of distance matrix the total cost of

transportation along the route with least

distance is calculated.

Suppose that a material needs to

travel from a node point to another node

point ( or suppose from a supplier to

manufacturer / customer via various

storage location or transit points ). And,

there is a need to find the following

routes:

a. Through which cost is minimum

b. Through which distance travelled is

minimum

For that we need to feed the above matrix

into the program and we get the result as

output. Various operators and functions

used for preparing the coding of this

algorithm are as follows:

• rand( x , y ) : generates a random

matrix of x , y dimension

• randi([ x , y ]) : generates a random

integer between the range of x , y

• clear all : clears all the local

and temporary variables in memory

• clc : clears the command

window

• size( x ) : shows the size or

dimension of a matrix x

• for / while : loop statement

• if / else if : condition statement

• end : ending sequence for

loop statement or conditional

statement

13 | P a g e


The complete MATLAB code for finding the

shortest path is given below.

clear all; clc;

XM=10;YM=10; X=1;Y=1; a=round(rand(XM,YM)*10); results=[];

length=0;

for z=1:10000 x=X;y=Y; temp=a(x,y); r1=[1];r2=[1]; rtemp=[a(x,y)];

while (x<XM | y<YM)

xn=x;yn=y; while (xn==x & yn==y) if (x<XM & y<YM) xr=[x x+1];yr=[y y+1];

elseif (x==XM & y<YM) xr=[x];yr=[y y+1]; elseif (x<XM & y==YM) xr=[x x+1];yr=[y]; end

x1=size(xr); y1=size(yr);

xn=xr(1,randi([1,x1(1,2)])); yn=yr(1,randi([1,y1(1,2)])); end

x=xn;y=yn;

temp=temp+a(x,y); rtemp=[rtemp,a(x,y)]; r1=[r1,x];r2=[r2,y]; end

if (temp<length | length==0) results=rtemp; length=temp;

end

end a

length results

In the code, a is a randomly generated

matrix. But for practical purposes we can

feed the code by replacing it with actual

data. XM & YM are the dimensions of

matrix. So, also we need to change these

with the change in number of nodes. At

the bottom; a, length and results is used

to provide the output of the algorithm

where a represents the actual matrix,

length gives the total cost involved ( in

case of cost matrix ) or total distance ( in

case of distance matrix ) and results gives

the output path along the least cost /

distance.

Now let us feed the actual cost

matrix in the system and get the output.

a =

157 876 704 436 137 191 778 302 78 230

348 490 572 502 172 681 190 49 46 238

403 183 755 866 930 281 115 228 754 824

348 686 816 483 280 377 912 902 457 493

195 135 406 303 424 874 123 691 315 540

448 213 197 90 705 863 813 88 931 593

49 585 545 623 970 426 130 608 536 827

449 735 499 349 306 382 123 309 394 465

394 929 484 347 166 373 117 217 946 122

920 50 578 226 856 921 281 642 449 114

length = 3841

results = [ 157 348 183 816 303 705

426 123 217 449 114 ]

The final path would be:

157 876 704 436 137 191 778 302 78 230

348 490 572 502 172 681 190 49 46 238

403 183 755 866 930 281 115 228 754 824

348 686 816 483 280 377 912 902 457 493

195 135 406 303 424 874 123 691 315 540

448 213 197 90 705 863 813 88 931 593

49 585 545 623 970 426 130 608 536 827

449 735 499 349 306 382 123 309 394 465

394 929 484 347 166 373 117 217 946 122

920 50 578 226 856 921 281 642 449 114

So, it is evident from the shortest path

algorithm when applied to the cost matrix

is that the minimum project cost for

transportation would be INR 3841 lakhs.

The work is only half done. The next step

would be to analyze shortest path

algorithm on distance path and to see

whether both the paths coincide for cost

and distance matrix.

14 | P a g e


Distance matrix, a =

99

02

81

18

87

14

48

38

37

15

70

51

22

64

45

66

73

23

76

46

40

50

31

48

90

50

82

58

25

79

21

34

62

58

79

13

78

62

26

32

76

32

59

88

72

07

67

72

46

14

89

70

98

5

77

52

63

77

83

45

32

79

40

9

14

9

43

12

13

29

11

67

13

50

16

49

19

23

39

83

67

08

70

00

56

84

80

71

44

7

86

22

55

91

34

37

12

25

42

82

40

08

91

32

92

10

97

8

21

58

97

16

59 98

7

40

77

23

00

15

6

81

80

88

74

90

58

76

92

73

17

19

18

93

27

20

92

90

23

56

43

37

93

94

21

78

38

34

39

66

54

83

06

95

31

75

56

36

02

61

85

15

14

88

80

41

99

61

87

13

39

77

52

68

14

93

15

87

87

19

89

50

90

88

3

72

31

34

19

85

90

89

74

91

08

87

24

50

1

length = 47555

results = [ 9902 3148 5988 149

4312 1329 1167 5591 987

2092 3602 8787 501 ]

The final path would be:

99

02

81

18

87

14

48

38

37

15

70

51

22

64

45

66

73

23

76

46

40

50

31

48

90

50

82

58

25

79

21

34

62

58

79

13

78

62

26

32

76

32

59

88

72

07

67

72

46

14

89

70

98

5

77

52

63

77

83

45

32

79

40

9

14

9

43

12

13

29

11

67

13

50

16

49

19

23

39

83

67

08

70

00

56

84

80

71

44

7

86

22

55

91

34

37

12

25

42

82

40

08

91

32

92

10

97

8

21

58

97

16

59 98

7

40

77

23

00

15

6

81

80

88

74

90

58

76

92

73

17

19

18

93

27

20

92

90

23

56

43

37

93

94

21

78

38

34

39

66

54

83

06

95

31

75

56

36

02

61

85

15

14

88

80

41

99

61

87

13

39

77

52

68

14

93

15

87

87

19

89

50

90

88

3

72

31

34

19

85

90

89

74

91

08

87

24

50

1

The yellow colour shows the path

traversed by the network while green

colour shows the starting and the ending

node. The shortest distance between the

nodes is 47555 kms. Hence, from the two

marked matrices it can be noticed that the

shortest paths are different in both the

cases. So, network optimization is required

to make both the path same. From

Logistics point of view, it’s best for

business that both the paths are same. If

both the paths are same then materials

take minimum time travelling it while the

cost involved is also minimum. The above

example can be viewed as a Logistics

network of Rail / Road / Air transit points

where flow from one location to another is

associated with two parameters namely

cost and distance. And, we worked to find

the best route available for the

transportation of goods / information.

Now let us take some time to give a brief

overview of the MATLAB coding hereby

used.

The code calculates the shortest

path between the top left corner and the

bottom right corner. At first marker is set

at the top left corner. At that point it has

three places to go, i.e. place directly below

it, the place directly right to it or the place

which is at diagonal. The choice of this is

done randomly. Even while a marker has 8

places surrounding it, then also 3 places

are considered which is towards the

bottom right corner. As the marker

reaches the bottom right corner, one cycle

is completed. Since this code works on

random position on cursor hence the

entire procedure is repeated for a large

number of cycles. Whenever a cycle has a

lower value it is replaced by previously

held value. Hence, at the end of complete

running of the code, the shortest distance

is found with more than 99% accuracy.

This program works on matrix of any

dimension but a higher dimension matrix

would take more time and more

computing resources to complete

successfully.

The shortest path algorithm is like a

Neural Network where the data takes

random path from source to destination.

The best solution in this process is

recorded. These types of neural networks

15 | P a g e


are known as Stochastic Neural Networks.

They work by introducing random

variation in the network which makes

them perfect to be used for network

optimization. Hence, in the example

above; simulation of the network was

done to find the shortest cost path and the

shortest distance path.

After we get the individual result of

each matrix, now the next step is to collect

all the input output pair in a different

matrix. Now we take the cost matrix and

work further on it. We take the

determinant value of each cost matrix and

insert it into a different input vector; and

the corresponding length in another

output vector. We gather input output pair

for 1000 random cost matrices. This input

output pair is then fed into the MATLAB

neural network simulator. To generate the

input output pair, the following coding is

used:

clear all;

clc;

testInput=[];

testTarget=[];

XM=10;YM=10;

X=1;Y=1;

for p=1:1000

a=round(rand(XM,YM)*1000);

b=round(rand(XM,YM)*10000);

results=[];

length=0;lx=0;

for z=1:100

x=X;y=Y;

temp=a(x,y);l2=b(x,y);

r1=[1];r2=[1];

rtemp=[a(x,y)];

while (x<XM | y<YM)

xn=x;yn=y;

while (xn==x & yn==y)

if (x<XM & y<YM)

xr=[x x+1];yr=[y y+1];

elseif (x==XM & y<YM)

xr=[x];yr=[y y+1];

elseif (x<XM & y==YM)

xr=[x x+1];yr=[y];

end

x1=size(xr);

y1=size(yr);

xn=xr(1,randi([1,x1(1,2)]));

yn=yr(1,randi([1,y1(1,2)]));

end

x=xn;y=yn;

temp=temp+a(x,y);

rtemp=[rtemp,a(x,y)];

r1=[r1,x];r2=[r2,y];

end

if (temp<length | length==0)

results=rtemp;

length=temp;

lx=l2;

end

end

testInput=[testInput,lx];

testTarget=[testTarget,length];

end

testInput

testTarget

The following code works with a cost

matrix of XM , YM dimension and gives the

output matrices of testInput & testTarget.

This output is then fed into MATLAB Neural

Network Simulator. The simulator is started

by typing in the command nnstart in the

command window, which starts a

simulator with a GUI interface.

After starting the GUI interface we

follow these steps to perform further

optimization and Neural Network Setup.

a) Select Dynamic Time Series App

b) Select Nonlinear Autoregressive

with External Input (NARX)

c) Click Next

d) In Input dropdown select testInput

variable

e) In Targets dropdown select

testTarget variable

f) In Time step select Matrix Column

g) Click Next

16 | P a g e


h) Select Validation and Testing

percentage to 20 % each. The

Training will become 60 % by itself.

i) Click Next

j) Change Number of Hidden Neurons

to 30 and number of delays to 5.

k) Click Next

l) Now choose a training algorithm.

We chose Levenberg-Marquardt.

m) Click on Train

n) Generate the Performance Graph

o) Generate Error Plot, Correlation

Graph & other plots whichever is

necessary.

p) Click on Next

q) Under Optionally Perform

Additional Tests again select the

testInput matrix for Input field and

testTarget for Target field.

r) Click Test Network

s) Click on Next

t) Click on Neural Network Diagram

u) Finally click on Generate MATLAB

Function to generate the final code

for the Neural Network.

So, the testInput matrix and the

testTarget matrix which contains the input

and target pair has the dimension of 1 * n.

Both the matrices contain data in the

following format:

testInputtestInputtestInputtestInput = 1.0e+291.0e+291.0e+291.0e+29 **** [[[[----0.3312 0.3312 0.3312 0.3312 ----0.8003 0.8003 0.8003 0.8003

----0.1425 2.9128 0.1425 2.9128 0.1425 2.9128 0.1425 2.9128 ----0.1145 0.1145 0.1145 0.1145 ----0.8028 0.8028 0.8028 0.8028

----0.6527 0.0303 0.6527 0.0303 0.6527 0.0303 0.6527 0.0303 ----0.0609 0.1297 0.0609 0.1297 0.0609 0.1297 0.0609 0.1297

----0.1761 0.0371 0.1761 0.0371 0.1761 0.0371 0.1761 0.0371 ... ... ... ... ... ... ... ... ............ ]]]]

testTargetestTargetestTargetestTarget = [4375 5581 3289 = [4375 5581 3289 = [4375 5581 3289 = [4375 5581 3289

5817581758175817 4073 5860 5046 5936 4073 5860 5046 5936 4073 5860 5046 5936 4073 5860 5046 5936

5698 6386 5698 6386 5698 6386 5698 6386 ............ ... ... ... ... ...]...]...]...]

One of the main characteristics of a Neural

Network is adaptive learning. It learns

during the training period. So, if the

network is trained for multiple times, its

efficiency slowly increases. The code for

Neural Network hence generated can be

used as a function in some other

programs without the need of repeating

the tasks described again and again. The

code generated in given below:

function [Y,Xf,Af] =

myNeuralNetworkFunction(X,Xi,~)

%MYNEURALNETWORKFUNCTION neural network

simulation function.

% Input 1

x1_step1_xoffset = -4.98999253881676e+29;

x1_step1_gain = 2.43320745266833e-30;

x1_step1_ymin = -1;

% Input 2

x2_step1_xoffset = 2389;

x2_step1_gain = 0.000415627597672485;

x2_step1_ymin = -1;

% Layer 1

b1 = [1.8622064538453846;-

1.3174394252249615;1.9871803820807037;2.00

36257151714532;1.5905552659314099;-

1.7757453601101036;-1.133700114441897;-

1.1610975421466321;1.0547846028904724;-

1.209130730342453;0.21669830424588038;0.54

201616038863742;0.9063674742950677;-

0.087828013925516285;-

0.33129153301616143;-

0.3102409292987372;0.36928862994813999;0.4

3016239621204977;0.17090046832086914;0.728

28274161038487;0.49848283419706602;-

0.48909627613539775;0.98650826861617913;1.

0342303883301023;0.91787242299088112;-

1.5080617045328351;-1.076145305423639;-

2.3354188909909031;2.4260369891928395;1.63

56262790583684];

IW1_1 = [-0.8990662879531256

0.63461441145057462 0.62069385836740265

0.73868345515978784

0.18065175069981662;0.83514571633870671

0.49432518088486799 0.16614340202975794

0.64806118134276736 0.12905628337033456;-

0.026496114337601035 0.13954316354780882 -

1.1845489186968488 0.58886316300453012

0.46775714406894559;0.24337221641342624

0.27993231322857781 -0.61434779168579279 -

1.2497150186046722 0.63585373554834046;-

0.013105345224936202 0.38984235186004551 -

0.15806467311727126 -1.1028179320429057

0.21237483678855992;0.23814126292331

0.5119158762223095 0.078313953593802083 -

0.96839287378850569

0.261817165927826;0.38435356615316235

0.15795276738597055 -0.99790964039322505

0.65885836132524589 0.61652792897625142;

0.39757621067164434 -0.486336538715748 -

0.19126431183574655 -0.91834363610115655 -

0.75615096639269364;-1.3748683911122817

0.10833156118945539 -0.37291015867472049

0.084893388233545947 -

0.027105282183725349;1.1455673347416533

0.53003726632197057 -0.47084124912411263

0.54119986676313814 -0.52968706330652982;

0.95276031958611007 0.29468288623147559 -

0.0063621284686704999 -2.6488401124019521

17 | P a g e


0.81553830857503695;-0.21869342802252509

0.23359834929142709 0.55908184351159917

0.20247696276611285 -0.89166792247530846;-

0.19219735592111095 -0.6929442795969627

0.44413234208284058 0.028171323570738566

0.52867998445926723;-0.80544929429151213 -

0.70960043978053555 -0.25668434724595557 -

0.74040111346637461

0.65513673328972544;0.72257785097141947

0.59687143961080269 0.67487290163600855 -

1.0503050331803363

0.58705665912220306;0.34309769040657734

0.57908827555917342 -0.92254795366028486 -

0.81037216649739419 -

0.19635804242575949;0.9384730365578271

0.079036443022610689 -0.033884509095074913

0.0086077331698611037 -

0.54936897550401143;0.33914637239829887 -

0.38940273136201964 0.23933785195616014

0.75203180951076187 -

0.26275431507056585;0.5033458562545432

0.20986094044036369 2.6370533627181731 -

0.93046359659903666

0.85836339352292179;0.70745880175925957 -

0.14445441321995967 -0.44112025728102122 -

0.98091735881663544 -1.2632184679735374;-

0.28762577820823498 -1.0142405287027487 -

0.46376470886144222 -0.31176220871814775

1.3246741992758304;0.52418104732678672

0.2762184661076818 1.4821886659486661 -

1.0466682714588009 -

0.41634183406386549;0.97039527504630074

0.4704524071147253 0.74280607436066359

0.69702490138182704 0.23983591620954248;-

0.55912436050875702 0.59830234669057969

0.65300134116136177 0.44186425739382568 -

1.0795960947346597;0.47835467047887176 -

0.88369549718136475 -0.2078912740824943 -

0.74222955412495439 -0.56068541374555247;-

0.64203716571715497 0.32288247956151234 -

0.30317220206179352 -0.98654247000060336

0.80927029855826194;-0.36624915607711345 -

0.77702853510993908 0.72215093887452553

0.71303583113890234 0.66120508707766668;-

0.53024896849301084 -0.17212434682316929 -

0.00044656499703289373 0.54849998163047564

0.70620579740084444;0.47978752559878707

0.63456884801241331 0.14239686178803643

0.81578454678473844

0.91246619454709377;0.84229378752886486 -

0.54899708465557717 0.99474871880497606

0.79640279449997364 -

0.037553398680422921];

IW1_2 = [-0.34611344785693254 -

0.40830675831848545 -0.86085433308025094

0.96480333925503114

0.37266981117479286;0.73536093575949479 -

0.77041359053094483 -1.0621340035067459

0.091383347730121106

0.8020243215829721;0.51949901018363076 -

0.08583137819447699 0.39445447714986326

0.94575351747801806 -0.0917014538018429;-

0.56843871876638952 -0.42478948686014029

0.17908729598308123 -0.1813913782930516

0.52028997376900543;0.24173913466964367 -

0.019117405919199054 0.79082190259439999 -

0.53989605123072049 0.68566975800058361;-

0.82493960622365103 0.77488597809783832 -

0.09972668064817361 -0.22626322996665793

1.2235094452239581;1.3325546981674625

0.04084751033621372 0.81272081662096896 -

0.68882196442835197 0.42526157859027569;-

1.1624456301620405 0.074752836632442365

0.28698793803347228 -0.18836171731554191

1.6994328309264659;0.082641165505694864 -

0.72647638294345307 0.37222916618193874 -

0.59557182059838309 -

0.54264118200755551;0.6170121320454558

0.51443721915202689 -0.13301985987703244

0.63812925800480447 -

0.017084794628528632;0.0017130577130541197

-0.72800368520101699 0.10703330995541277 -

0.17316408790626062 -

0.3755092278573019;0.35223039924279559 -

0.13764491172306542 -1.13361136467351

1.5277529899169502

1.7189480228176701;1.1672419360255182 -

0.10334655171986172 0.53427977367682888

1.5139044180624517 -1.550737677175634;-

1.0569191856627287 -0.26358498521492124 -

1.0464774105351735 0.59866436090761566

0.16687822195650651;-1.2789849892807565 -

0.12786975087607283 -0.02558122556063841

0.80215017670022715

0.55039043808640442;0.61421688780853689 -

1.0028508140191952 0.17348480707386513

0.87214976193028992 1.0579861500912122;-

1.5529209822890586 -0.43808715501915063 -

0.59074272848153941 0.88027658657167618

0.86266064323177183;-0.05043044769732264

0.47044464482943216 -0.12667436822643174

0.37517910056700515

1.043301418892971;0.47022588543937671 -

0.65691194612863257 -0.97086448280318516

0.094226358566928708 -

1.1293816066309033;1.0421132922519136

0.065357225366302646 -0.94145327173758075

0.86012002196354642 0.28503649126835584;-

0.48094833405868009 -0.82269501151454727

1.1468223251793694 0.60030284193955996

0.17448178916311574;0.013868739336124189

0.98211319217429494 -1.4017692964368589 -

0.36548314935748533 0.39642465685040162;-

0.53132671320011282 -1.0596004121598877 -

0.095853903277147712 -0.30715393072113406

0.38775546556609308;0.29364552417358758 -

0.57991963812864633 0.1391714880152958

0.30755397521125971

0.1327141859513469;0.4226223163090283 -

0.56216412131062587 0.9658677372049681 -

0.90499507143959779 0.2589082153187397;-

0.53149722899416429 -0.69210825201258275 -

1.4810029456546885 -0.81809206139490032

1.2776826328475663;-0.12991100698731972

0.77426373433953333 -0.44820799882802415 -

0.92006271632766257 -

0.37847869211162261;0.80518593508973324 -

0.48792315659912644 0.99758979395753478

18 | P a g e


1.4785751355499472 -0.035433985779074202;-

0.46555554288242873 -0.057754331726513439

0.11789196263562859 -0.5617207144591243

0.040644474042050689;-0.59831406066985027

-1.1672947553207704 -0.3915596324049469

0.84081490926390678 0.2556588847771385];

% Layer 2

b2 = 0.08711061819165343;

LW2_1 = [0.45852110202363688

0.12637545383546009 -0.30321962693291749 -

0.46561838228245878 -0.60165853483385245 -

0.6141655036760979 -0.33844568110575796 -

0.89559170822650036 0.51470320408859283

0.12788968205527926 -0.43402497270887641

0.17947011010088426 -0.55226708854813755 -

0.12813696032788227 0.80011444752262162

0.20648492637305527 -0.77525379331602473 -

0.41751528644098324 -0.36547039909167822

0.18857855743724139 0.45470559546583444

0.48652515383598738 0.22978250493786384 -

0.70688834235757514 0.36382965901896747 -

0.45786941916913054 -0.06523296931512948 -

0.5769283845107902 -1.0834250534825229

0.42348378732602371];

% Output 1

y1_step1_ymin = -1;

y1_step1_gain = 0.000415627597672485;

y1_step1_xoffset = 2389;

% ===== SIMULATION ========

% Format Input Arguments

isCellX = iscell(X);

if ~isCellX, X = {X}; end;

if (nargin < 2), error('Initial input

states Xi argument needed.'); end

% Dimensions

TS = size(X,2); % timesteps

if ~isempty(X)

Q = size(X{1},2); % samples/series

elseif ~isempty(Xi)

Q = size(Xi{1},2);

else

Q = 0;

end

% Input 1 Delay States

Xd1 = cell(1,6);

for ts=1:5

Xd1{ts} =

mapminmax_apply(Xi{1,ts},x1_step1_gain,x1_

step1_xoffset,x1_step1_ymin);

end

% Input 2 Delay States

Xd2 = cell(1,6);

for ts=1:5

Xd2{ts} =

mapminmax_apply(Xi{2,ts},x2_step1_gain,x2_

step1_xoffset,x2_step1_ymin);

end

% Allocate Outputs

Y = cell(1,TS);

% Time loop

for ts=1:TS

% Rotating delay state position

xdts = mod(ts+4,6)+1;

% Input 1

Xd1{xdts} =

mapminmax_apply(X{1,ts},x1_step1_gain,

x1_step1_xoffset,x1_step1_ymin);

% Input 2

Xd2{xdts} =

mapminmax_apply(X{2,ts},x2_step1_gain,x2_s

tep1_xoffset,x2_step1_ymin);

% Layer 1

tapdelay1 = cat(1,Xd1{mod(xdts-[1 2 3

4 5]-1,6)+1});

tapdelay2 = cat(1,Xd2{mod(xdts-[1 2 3

4 5]-1,6)+1});

a1 = tansig_apply(repmat(b1,1,Q) +

IW1_1*tapdelay1 + IW1_2*tapdelay2);

% Layer 2

a2 = repmat(b2,1,Q) + LW2_1*a1;

% Output 1

Y{1,ts} =

mapminmax_reverse(a2,y1_step1_gain,y1_step

1_xoffset,y1_step1_ymin);

end

% Final Delay States

finalxts = TS+(1: 5);

xits = finalxts(finalxts<=5);

xts = finalxts(finalxts>5)-5;

Xf = [Xi(:,xits) X(:,xts)];

Af = cell(2,0);

% Format Output Arguments

if ~isCellX, Y = cell2mat(Y); end

end

% Map Minimum and Maximum Input Processing

Function

function y =

mapminmax_apply(x,settings_gain,settings_x

offset,settings_ymin)

y = bsxfun(@minus,x,settings_xoffset);

y = bsxfun(@times,y,settings_gain);

y = bsxfun(@plus,y,settings_ymin);

end

% Sigmoid Symmetric Transfer Function

function a = tansig_apply(n)

a = 2 ./ (1 + exp(-2*n)) - 1;

end

% Map Minimum and Maximum Output Reverse-

Processing Function

function x =

mapminmax_reverse(y,settings_gain,settings

_xoffset,settings_ymin)

x = bsxfun(@minus,y,settings_ymin);

x = bsxfun(@rdivide,x,settings_gain);

x = bsxfun(@plus,x,settings_xoffset);

end

19 | P a g e


The efficiency of a Neural Network can be

observed from the MSE ( Mean Squared

Error ) value of the network. The lesser the

MSE value, the better is the performance.

To analyze the flow of MSE value as the

system is simulated, we need to look at

the MSE graph which is given below.

Fig 6. MSE graph for 1

st Training period

From the graph, it is noticed that Best

Validation Performance is at epoch 7

which means that the network reached its

peak performance on the 7th run cycle.

But, further cycles were done to test and

validate the data. If the network is trained

again for the 2nd time, we get the following

graph.

Fig 7. MSE graph for 2

nd Training period

From the graph it is noticed that in the 2nd

training period, peak performance is

obtained in the epoch 4 which is 3 cycles

better than the last training. Hence, the

common observation would be that the

performance of the neural network

increase with each training cycle. Also

notice that the peak MSE value is also

reduced. All these prove that the network

adapted to the incoming stimuli.

Fig 8. Training State of ANN

Fig 9. NARX Neural Network Diagram

The neural network as compiled in the

steps given above is shown in Fig 9. It

works on single input system x(t) and gives

the output y(t) which is again fed into the

network. The number of hidden layers is

30 and the amount of delay is 5. The

output layer is the activation layer which

decides if there will be output based on

activation function, input vector and

weights. The total sample is first divided

into three parts: training data, test data

and validation data ( here 60% of the

sample are used for training, 20% for

testing and remaining 20% for validation ).

Training is first done to educate the

network to incoming stimuli. Then testing

is done to test the performance of the

network. Finally, validation is used to make

sure that the network does not show any

abnormal behaviour and that it is working.

20 | P a g e


Let us now analyze the MSE graph. The

validation curve closely matches to the test

curve which means there is no over fitting.

Contrary, if the test curve had been ahead

of the validation curve, then some over

fitting might have occurred.

Fig 10. Regression Plot for the Network

The graph shows the relationship between

the shortest cost path in the cost matrix

and the respective distance from the

distance matrix. Hence, it shows the

variability of cost associated with

transporting something between two

nodes of certain distance. Since, the data

was randomly generated without any

actual relevance to the world; hence

association between the two vectors is

minimum, almost equal to zero. R

represents the relationship between the

inputs and the targets. The dashed line

represents the perfect target which is

results – targets. The solid coloured line

represents the best fit. In this example, R =

0.19444 ( for combination of Training, Test &

Validation data ).

�� = �. ��

Hence, R2 = 3.78% .

So, there is least

amount of connection

between the two

variables. The points

which are scattered

across the graph

shows the data points

used as inputs for

training, validation

and test purposes. If

for a particular run

cycle, the network

does not yield good

results, then it is

recommended to run

the whole network

again as each cycle of

initialization involves

different network

parameters and so it

might produce

different results. We

can also change the

amount of hidden networks involved in

the network as each extra hidden network

gives the whole network to optimize itself.

We can also change the algorithm to see if

the yields are successful.

In the above text we worked briefly

with a simple example as to how the

neural network is setup and analyzed.

Hence the following can be inferred from

the data graphs given above:

• Data was based on a declining /

faulty / dead company

• Algorithm used was incorrect

• Data was bogus

• Data was manipulated for some

reason

21 | P a g e


The above example of Neural Network

was done using nnstart tool which has

scope for generation of the network

and other related analytics. It is also

used to generate the function which

can be later used to make predictions.

Now we would work with another

Neural Network tool nntool which can

be used effectively to make predictions

also. In the previous example data was

randomly generated hence there was

little amount of correct / appropriate

relationship in it. We again try the

whole neural network with relevant

data.

For the second example we take a

multi parameter matrix for the logistics

network.

Sl. Parameter Value Range

1 Noise 0.0063 – 0.99

2 Seasonal 0 / some value

3 Excise Rate Less than 10

4 Normal / Tatkal 0 / 1

5 Failure Rate 0.2 – 0.6

6 Inflation Rate Less than 10

7 % transported <=100

8 Distance (10000

miles)

<=5

9 No. Of nodes <10

10 Quantity <500

11 Approx Cost

(INR Lakhs)

<50

12 Time (days) <500

13 Overhead Cost

(INR Thousands)

<30

14 Output Cost ---------------------

The test matrix contains a single value pair

based on which prediction will be made.

Finally the predicted value is multiplied by

the multiplying factor for the cost matrix.

Only the data pair in the form of a matrix

is fed into the network. The neural

network automatically analyzes the data

column and trains the network which is

then reinforced automatically for the

situation by the AI. The predicted value will

be compared with company financial data

to get the final conclusion if the logistics

network is a case of success or fail. The

data pair used here contains more than

500 instances. Each instance contains 13

input parameter and 1 output parameter.

The input matrix is stored in testInput

(13*506 matrix) and the output matrix in

testTarget (1*506 matrix). The steps

followed for this example are given below:

a) We start the Neural Network / Data

Manager by typing in nntool in the

command window.

b) Click on Import.

c) Select testInput variable, select

destination as Input Data and click

on Import. Finally Click Ok.

d) Select testTarget variable, select

destination as Target Data and click


e) Select testData variable, select

destination as Input Data and click


f) Click Ok.

g) Click on Close.

h) In the Neural Network / Data

Manager Click on New

i) Select Network type. We select it as

Feed-forward backprop as the

network algorithm.

j) Select testInput as Input Data.

k) Select testTarget as Target Data.

l) Click on Create.

m) Click on Ok and then on Close.

n) Select network1 in the Neural

Network Manager and click on Open.

o) Select Train Tab. Under it select

testInput as Inputs & testTarget as

Targets. Finally Click on Train

Network.

p) Generate the Regression and

Performance Plots.

22 | P a g e


q) Click on Close and select the

Simulate tab.

r) Select testData as Inputs and click

on Simulate Network. Finally click

on Ok.

s) Under Neural Network Manager

select network1_outputs and click

on Open.

t) The predicted values are displayed.

Fig 11. Performance Graph for 2

nd example

Fig 12. Gradient, mu and validation fail plot

for 2nd example

From the above two graphs it can be

observed that MSE value at best

performance is 15.0921 which is quite

small. Comparing the MSE value for the 1st

example which was 621031 for 2nd training

cycle, we can clearly see the difference. We

can therefore conclude that the data has

all the merits. And, the best performance

was observed at epoch 10. From the

gradient curve it is observed that the error

rate was slowly reduced as the network

training was simulated. And, validation

checks were performed from epoch 10

onwards as the best performance was

observed in epoch 10. Now let us look at

the regression curve showing the data

points and fit curve.

Some of the sample values used to train

the network is given below:

1111 0.111 0.173 0.279 0.179 0.113 0.435

2222 0 0 0 0 30.00 0

3333 27.740 9.690 9.690 9.690 4.930 10.59

4444 0 0 0 0 0 1.00

5555 0.6090 0.585 0.585 0.585 0.428 0.489

6666 5.983 5.707 5.926 5.670 6.897 5.344

7777 83.50 54.00 42.60 28.80 54.30 100

8888 2.109 2.381 2.381 2.798 6.336 3.87

9999 4.000 6.000 6.000 6.000 6.000 4.00

10101010 711.0 391.0 391.0 391.0 300 277.0

11111111 20.10 19.20 19.20 19.20 16.60 18.6

12121212 396.9 396.9 396.9 393.2 391.2 396.

13131313 13.350 13.590 13.590 17.60 11.38 23.0

14141414 23.100 19.700 18.300 21.200 22.00 20.00

And the testData, the matrix which is used

to predict the scenario is also given

Sl. No. Values 1 0.2300

2 10.0000

3 10.5000

4 1.0000

5 0.5000

6 5.7000

7 68.0000

8 2.3500

9 6.0000

10 650.0000

11 20.0000

12 15.0000

13 22.0000

On running the simulation of the neural

network representing the logistics

network, the above set of data gave an

output of 1*1 matrix representing the cost

associated with the Logistics network.

�� = ��. ��

23 | P a g e


From the parameter table, the value of

output can be taken as INR 25.5999 Lakh.

We now look at the regression plot and

analyze it with respect to the regression

plot obtained in the first example.

Fig 13. Regression plot for 2nd example

From the above regression plot, the R-

value can easily be noted to be 0.92248

which is clearly better than the R value of

the previous example. Hence, the data

points are better fit. Even then some of the

data points are scattered around the fit

curve. Since, the R value is greater than 0.9

hence the network can be said to be

similar to that of the Logistics network and

that the data points are accurate.

The output value that we received

as a result of the prediction is the total

cost involved with the network for the

transportation under the parameters

given as the input. So, to decide if the

network is a case of success or fail, we

must compare the output cost ( INR

25.5999 lakhs ) to the amount of money

the company is capable and willing to

spend. If the output is less than that sum

of money, then the

network is a successful

one or else it is a case of

unsuccessful network.

The MATLAB code for

the neural network

which was generated in

this example is given

below.

function [Y,Xf,Af] =

neural_function(X,~,~) %NEURAL_FUNCTION

% ===== NEURAL NETWORK

CONSTANTS =====

% Input 1 x1_step1_xoffset =

[0.00632;0;0.46;0;0.385;

3.561;2.9;1.1296;1;187;

12.6;0.32;1.73];

x1_step1_gain =

[0.022479517787368;0.02;

0.0733137829912024;

2;4.11522633744856;

0.383215175320943;

0.0205973223480947;

0.181869435931944;

0.0869565217391304;0.00381679389312977;0.2

12765957446809;0.00504311866458218;0.05518

76379690949]; x1_step1_ymin = -1;

% Layer 1 b1 =

[36.357184522980909;14.119396930672583;-

0.27278614037853477;3.4046596216055507;-

11.269335472679851;-13.169119619915456;-

0.88236889557629072;23.938045111726254;0.3

2849481229537225;12.090640960323316]; IW1_1 = [-6.4156964643184944

12.916636442378659 36.160509774436825 -

14.47529888885345 20.203037621955211 -

17.681922329162976 21.070309153982532

27.994332023180903 23.1837743628844

1.7416235328037739 17.293024630348778 -

20.695869400746105 -

12.413148760083043;19.887374545364036 -

40.817613391351415 -3.6829731767938467 -

17.602203115150555 -117.7853249654877 -

94.008539298530934 27.923670320088508

44.50977196623667 57.395440325422641 -

40.172046845310021 18.314427951551593 -

74.156876361418739 -

22.038089268098158;22.845485688330754 -

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7.0115781538036401 -0.0091110059063216758

-22.108140682162873 -1.3984949157611064

13.877773903176005 -4.8939641488803893

14.65222686408784 -8.0655091321759276

2.7385798751957333 -1.1196236353942755

9.6334073350981946

20.41669141826533;1.072620238762759 -

0.30693381329168934 0.46051583907811083

0.28340723772764953 0.51158114965318713

0.20688380402814513 -0.23617158155962514

1.9523863679811178 0.079913107980995587 -

0.29538069766762654 0.39394328174388155 -

0.097795381675424536 0.97680092536560514;-

47.718828012254058 -78.384819237707475 -

20.922777811170167 57.366510664461572

4.1092560946004246 -27.391325703481208 -

21.441766084618628 83.472867999734902

0.33581033109609215 61.900439949174398 -

16.46006980952281 -5.2883855524545655

17.528738567171022;-25.541725833509716

1.619237173206638 9.0973408365424877 -

2.0791070396877793 -1.6571075525831935

7.7738962113921941 -3.018631665568718

4.8360742638482144 -3.2671295018250563

3.4230819328560171 1.2375789286726433 -

2.8379672241416167

9.7650569037510451;1.7427259672558875

0.56555642593638211 1.5019459829113471

0.42296242211352753 -1.3165095534157836

1.4586643689664693 0.27956313228990187

0.33774436316394524 0.38383314782350603

1.3228474840496229 -0.50016771666758519

0.61687652611345956 -3.1972156084233441;-

5.3939244427350133 14.277127528008331 -

15.247185045540963 -4.5023635424427679

18.632301070911673 30.278089939425694

6.8571526378241501 21.791410254600525

40.270442822900407 -61.772106045546735 -

13.178610708585811 7.6935633634734257

33.340303562139688;3.3124594126878431 -

0.09920842851718209 -1.73801863970958

0.70809817398694219 1.1211057707233578 -

1.2558163550695634 0.9070426172621413 -

0.66001347360774243 -2.6681716122608279

2.4343030426294967 -0.6967079798890965

1.4062890184437782 -0.38884529018376163;-

1.2380753697247968 -0.30939007693102072

0.37112729591861893 13.898220360966006

1.9815539353641571 2.4354207199194611 -

0.61340664986142834 1.6309868230079145 -

0.31370159924020102 -0.74581371322647372

0.035648037806396519 1.13008051224665

0.5491079757426125];

% Layer 2 b2 = 0.38351634976819882; LW2_1 = [-0.022979527292961349

0.026871202455676233 0.044731178755804089

-0.71357672644922121 0.008576025304220249

0.02201117689749682 0.2259544252312109

0.0020545652658588518 -0.22490850690782052

0.39085707864842389];

% Output 1 y1_step1_ymin = -1; y1_step1_gain = 0.0444444444444444; y1_step1_xoffset = 5;

% Format Input Arguments isCellX = iscell(X); if ~isCellX, X = {X}; end;

% Dimensions TS = size(X,2); % timesteps if ~isempty(X) Q = size(X{1},2); % samples/series else Q = 0; end

% Allocate Outputs Y = cell(1,TS);

% Time loop for ts=1:TS

% Input 1 Xp1 =

mapminmax_apply(X{1,ts},x1_step1_gain,x1_s

tep1_xoffset,x1_step1_ymin);

% Layer 1 a1 = tansig_apply(repmat(b1,1,Q) +

IW1_1*Xp1);

% Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1;

% Output 1 Y{1,ts} =

mapminmax_reverse(a2,y1_step1_gain,y1_step

1_xoffset,y1_step1_ymin); end

% Final Delay States Xf = cell(1,0); Af = cell(2,0);

% Format Output Arguments if ~isCellX, Y = cell2mat(Y); end end

% ===== MODULE FUNCTIONS ========

% Map Minimum and Maximum Input Processing

Function function y =

mapminmax_apply(x,settings_gain,settings_x

offset,settings_ymin) y = bsxfun(@minus,x,settings_xoffset); y = bsxfun(@times,y,settings_gain); y = bsxfun(@plus,y,settings_ymin); end

% Sigmoid Symmetric Transfer Function function a = tansig_apply(n) a = 2 ./ (1 + exp(-2*n)) - 1; end

% Map Minimum and Maximum Output Reverse-

Processing Function function x =

mapminmax_reverse(y,settings_gain,settings

_xoffset,settings_ymin) x = bsxfun(@minus,y,settings_ymin); x = bsxfun(@rdivide,x,settings_gain); x = bsxfun(@plus,x,settings_xoffset); end

25 | P a g e


5. CONCLUSION

In this brief study of Neural Networks and

its use, we came across how to use the

neural networks for predictions. We also

came across shortest path algorithm

which is used to calculate or traverse the

shortest cost / distance path between two

nodes. We also looked upon various

MATLAB functions and applications which

were used to simulate the neural network,

plot graphs & diagrams, perform data

generation, etc. In this highly competitive

business world, extra cost can be the

difference between a successful and an

unsuccessful business. And, logistics play a

very important role in the fate of a

company as logistics operations are

responsible for movement of resources,

personnel, raw materials, finished goods

and wastages. If the logistics network is

not optimized, then there can be cases of

late arrival, more wastage, more costly

transport, etc.

Sometimes networks can be quite

complex and it becomes tedious to

calculate all possible scenarios and

probabilities. It is where neural networks

are more powerful. Neural networks can

be effectively used to calculate time series

problems, match data clusters, recognize

patterns & solve linear and non-linear

equations fast as well as accurately. The

success of calculations depends hugely on

the correct data so that the network could

easily play around with the analytics. In

case of a neural network, the hidden

layers allow the network more

optimization options.

Fig 14. Neural Network Diagram for the 2nd

example

Now let us discuss a little about the

network as given in the 2nd example. We

can see clearly that there are 13 inputs

given to the network at a particular

instance of time. The hidden layer adapts

itself with each incoming stimuli while

matching it to the targets. In this way,

network training is done. After training is

done, then validation, testing and

simulation of the network takes place. In

validation and testing phase, the neural

network thus achieved from training

phase is tested with different inputs to see

if their output lies within the specified

range. There are different types of neural

network algorithms such as Levenberg-

Marquardt, Resilient Back-propagation,

Scaled Conjugate Gradient, Variable

Learning Rate Back-propagation & many

others.

A neural network is a powerful tool

for the analysis of complex data which can

be sometimes difficult to achieve using

linear programming methods. Finally, we

saw how the neural network was used to

predict an outcome. The output is

achieved in response to an incoming

vector. The prediction value is matched

with the financial values of the company to

arrive at an outcome. One aspect of neural

network which was left out in the previous

sections is that as the number of training

variables increase, so does the quality of

neural network. A more trained neural

network is much more agile and can

predict larger array of variables. There are

similarities between a neural network and

a logistics network, which makes it easy to

analyze the logistics matrix and its

transformation.

Hence the conclusion; that the

neural computing can take analytics and

prediction making to another level. And, if

the neural networks are combined with

some good analytical functions & logic can

make simply outstanding results.

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6. RECOMMENDATIONS

In the last section of this project work, we

would look into what the further work on

this topic could be & the ways to further

improve the network optimization. Since

the neural network which we worked with

depends on historical data so it is

recommended that the data be as original

as possible. The quality of prediction is

directly proportional to the quality of data.

The practical use of Neural

Networks is endless in business situations.

From the viewpoint of a Logistics network,

further experiments can be done with the

neural networks to show the inter-relation

between the different parameters and the

output. In today’s competitive world, it is

also essential to take into account the

quality of service. Using neural networks

we can optimize a path for certain

category of products so that for essential

products the time taken for transportation

is minimum and for non-essential

commodities, the cost is minimum. There

has to be a trade off between the distance

and the cost which could be easily found

by neural computing. Not only so, the

seasonal variations as well as the

randomness of a transportation network

can easily be simulated.

Every company has a Supply Chain

Network for transportation of goods. So,

software designed specifically for logistics

network can be created taking into

account the principles of neural networks.

Not only so, the principles of neural

computing can be applied to other

departments and / or technologies to

pinpoint the growth. Suppose a company

decides to introduce neural networks to

make intelligent predictions about the

manufacturing process, and then what

could be the outcome? Then it would even

be possible that the neural network

automatically read market data ( customer

data, retail data, popularity data, etc ) to

make necessary adjustments to the

product design by itself.

Suppose a neural network

algorithm finds out that customers prefer

books with dimension between 15 cm to

20 cm. Then, the neural network would

automatically instruct the machines to

produce books with the preferred

dimension and the contents inside the

book auto calibrated to fit images and

texts for best possible representation.

Then the neural network would wait to see

the reaction of customers to the newly

designed product and based on the

response it would again modify itself. In

this way, the company would definitely be

ahead of its competitors. Such is the

power and scope of neural networks.

Hence, we saw that the neural

networks have endless usage constrained

by human thoughts and their applications.

Also, newer and more efficient training

algorithms can be designed to enable

further growth in this sector. Coming back

to Logistics network which is a tip of the

iceberg, neural computing can take

business to another level of automation.

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7. REFERENCES

[1] "What is Logistics and Supply Chain Management?", Supplychainopz.com, 2016. [Online].

Available: www.supplychainopz.com/2012/04/what-is-logistics-and-supply-chain-management.html.

[Accessed: 01-Feb- 2016].

[2] "Artificial neural network", Wikipedia, 2016. [Online]. Available:

https://en.wikipedia.org/wiki/Artificial_neural_network. [Accessed: 13-Feb- 2016].

[3] "Stochastic neural network", Wikipedia, 2016. [Online]. Available:

https://en.wikipedia.org/wiki/Stochastic_neural_network. [Accessed: 18- Feb- 2016].

[4] K.A. Smith and J.N. Gupta, Neural networks in business: techniques and applications for the operations

researcher, 1st ed. PERGAMON, 2000.

[5] M. Kazemi, A. Niknafs, V. Ranjbar and A. Forouharfar, APPLICATION OF NEURAL NETWORKS IN

FORECASTING BUSINESS AND MANAGERIAL PROCESSES IN COMPARISON WITH NONLINEAR MODELS

(CASE STUDY: IRAN’S WOOD INDUSTRY), 1st ed. International Journal of Social and Economic Research,

2011, pp. 220-225.

[6] S. Rajamanickam, A. Maithili and R. Kumari, NEURAL NETWORK TOWARDS BUSINESS FORECASTING, 2nd

ed. IOSR Journal of Engineering, 2012, pp. 831-836.

[7] Y. Eldon, Artificial neural networks and their business applications, 1st ed. Taiwan: ELSEVIER, 1994, pp.

303-313.

[8] A. Moutinho and J. Costa Sousa, "NETWORK OPTIMIZATION MODELS", Presentation, 2016.

[9] S. Sivanandam, S. Sumathi and S. Deepa, Introduction to neural networks using MATLAB 6.0. New Delhi:

McGraw Hill Education (India) Private Limited, 2006.

[10] E. Davalo and P. Naïm, Neural networks. London: Macmillan, 1991.

[11 ] G. Swamy and G. Kumar, Neural Networks. Hyderabad, India: SCITECH, 2016.

[12] V. Sharma, Artificial Neural Network Applicability in Business Forecasting, 1st ed. Jammu: International

Journal of Emerging Research in Management &Technology, 2012.

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8. APPENDIX

( List of Tables & Figures used in the project work )

List of Figures

1. Artificial Neural Network Model ( Pg- 2 )

2. Probability Density Function ( Pg – 3 )

3. Schematic of a Neuron ( Pg – 4 )

4. Abbreviated Logistics / Supply Chain Network ( Pg – 7 )

5. Nodal points of a Logistics / Supply Chain Network ( Pg – 7 )

6. MSE graph for 1st Training period ( Pg – 18 )

7. MSE graph for 2nd Training period ( Pg – 18 )

8. Training State of ANN ( Pg – 18 )

9. NARX Neural Network Diagram ( Pg – 18 )

10. Regression Plot for the Network ( Pg – 19 )

11. Performance Graph for 2nd example ( Pg – 21 )

12. Gradient, mu and validation fail plot for 2nd example ( Pg – 21 )

13. Regression plot for 2nd example ( Pg – 22 )

14. Neural Network Diagram for the 2nd example ( Pg – 24 )

List of Tables

1. Cost Matrix of the Network ( in INR Lakhs ) ( Pg – 10 )

2. Distance Matrix of the Network ( in Kilometres ) ( Pg – 10 )

3. Probability / Noise Matrix ( Pg – 11 )

4. Actual Cost Matrix ( in INR Lakhs ) ( Pg – 11 )

List of Neural Network Coding

1. Code 1 ( Pg – 15 )

• Network based on a logistics network which takes in a network matrix as input and

the shortest path distance between the nodes as output.

2. Code 2 ( Pg – 22 )

• Network based on giving 13 parameters as input and getting the total cost as

output. The network is also used to predict the outcomes by providing a 13*1

matrix as test input which represents the 13 parameters.

complete_project

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