Intelligent Tools for Techno-Economic

Modellingand

Network Design

Tim GloverChris VoudourisAnthony Conway

Edward TsangAli Rais Shaghaghi

Michael Kampouridis

Network DeploymentGiven a new country/city

Where should phone/Internet cover be provided?

Deployment Plan Example

Year 1

Year 2

Year 3

No deployment

Cost vs RevenueCost

Hard optimization problem

Very technicalProfitability

depend on it!

Revenue Based on

business model Commercial

confidential

0 1 2 3 4 5 6 7 8 9 10

-300,000

-200,000

-100,000

0

100,000

200,000

300,000

400,000

Cash Flow

In-vest-mentRevenue

Net

Year

Cost: Fibre Trenching(Graph Problem)

The network may include fibres between exchanges, roads in a town, or conduit in a building.

The task is to minimise: Trenching; and costs for fibre optic

network deployment.

Confidential MaterialTim Glover(BT)

Ali Rais Shaghaghi(Essex)Michael Kampouridis (Essex)

Edward Tsang (Essex)

Intelligent Tools for Fibre Access Network

Design Tim Glover(BT)

Ali Rais Shaghaghi(Essex)Edward Tsang(Essex)

BT NetDesign is a software platform for assisting with physical network design. It is written as a Rich Client Platform Eclipse application. The main components are  an extensible data model describing networks as

nodes and linksa graphical editor for viewing and editing networksa problem solving package for representing and

solving network design problems

Fibre Access Network Design using the BT NetDesign platform

For example, the network may consist of fibres between exchanges, roads in a town, or conduit in a building. The problem is to minimise trenching and costs for fibre optic network deployment.

1.Fibre Trenching(Graph Problem)

1

1 2

3

Guided Local Search is a Metaheuristic  search method.

Using solution features to improve the local search algorithm

Considerable improvement in solver algorithm in regards to execution time and optimised solution when compared to the existing BT NetDesign algorithms (Simulated Annealing)

Integration to the current BT NetDesign platform

Guided local Search for Graph Problem

In general, an access fibre network consists of a set of Customers, a set of Distribution Points (DPs) and a set of Pick–Up Points (exchanges, or PUPs). These points are located in a network of roads, and possibly open spaces. The problem is to construct a tree of fibres that connects each customer to a PUP, either directly, or via one or more DPs, that minimises the total cost.

2.Access Fibre Network Design

Different DPs are available of different capacities (eg 44, 88).

Different customer types may require different numbers of connections

Different cables are available that bundle together different numbers of fibres

Different roads may have different costs associated with digging trenches

Digging a trench across a road costs more than digging along a pavement.

There is a maximum reach between customers and DPs, and between DPs and PUPs

Some considerations affecting cost

Tightly constrained problem In some cases finding a single feasible solution

could take months

Conventional search methods were unable to solve the problem

Use of advanced CS methods to have fast and optimised solutions In Cases of extremely tightly constrained problems

the CS solver would ensure that at least one solution is found

Intelligence for solving constraint satisfaction network design problem

Benefit of joint work to date

This work has mainly focused on introducing novel intelligent problem solving algorithms to the BT NetDesign platform.

They have contributed mainly into two areasGraph ProblemAccess Fibre Network Design

Concluding Remarks

Intelligent Tools for Techno-Economic

Modellingand

Network DesignTim Glover (BT)

Michael Kampouridis (Essex)Ali Rais Shaghaghi (Essex)

Edward Tsang (Essex)

Techno-economic modelling for FTTx

Produce a model whichAnalyses the technological requirements of the

deployment of an FTTx investmente.g. number of workers, trenching length, cable

lengthAnalyses the economical requirements of the

above deploymente.g annual cost, annual revenue, cash flow

Purpose of model: to advice on the viability and profitability of the investment

Model inputsArea population

Social category

Competition

Budget

Rental tariffs and number of customers

PAYG tariffs and number of customers

Study period

Model outputsAnnual revenue

Annual cost

Cash flow

Net Present Value

Internal Rate of Return

Need for intelligenceWhile a techno-economic model can evaluate

different deployment plans, the number of such plans can be very largee.g. if we plan to roll-out to 50 cities within the

next 5 years, the number of different deployment plans is 550

Computationally expensive to evaluate all available deployment plans

Question: “What is the deployment plan that offers the highest profit”?

Adding intelligenceUse different heuristics to locate the optimal

deployment planSimple Hill ClimbingSteepest Ascent Hill ClimbingGenetic Algorithms

Heat Map-Deployment Plan for London

Improvement of up to 18% in the NPV-equivalent to millions of pounds savings

Graphs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-40000000

-20000000

0

20000000

40000000

60000000

80000000

Cash Flow

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

10000000

20000000

30000000

40000000

50000000

60000000

70000000

Annual Revenue

ConclusionUse of intelligent methods for finding optimal

deployment plans for FTTx deployment

Results show that thanks to the methods used, there has been an increase in the profitability of the investment

Presented a techno-economic tool for evaluation of such investment