Abi 3 l First Advisory Panel Meeting Dr Liz Varga [email protected] 25 th Nov 2010

abi3lFirst Advisory Panel Meeting Dr Liz [email protected]

25th Nov 2010

Agenda

• 10:00 Welcome and intro to abi3l plus role of panel - LV and AB• 10.10 The challenges for freight strategy in a constrained financial environment – MG

and AB• 11:00 Strategic Modeling software and break-out session 1 – SV• 11.40 Coffee• 12:00 Feedback from breakout session 1• 12:30 Agent based technology – principles and cases – PG• 13:00 Complex Systems Research Centre past models – LV and PA• 13.15 Lunch• 14:00 Introduction to proposed model structure – PG• 14.45 Breakout session 2 • 15.15 Coffee• 15:30 Feedback from breakout session 2• 16:00 Close

AGENT BASED INTELLIGENT LOGISTICS abi3l

209083_presentation251110

Partners

MDS Transmodal - lead

Cranfield University

LCP Consulting

Barloworld Supply Chain Software

Part funded by the Technology Strategy Board

What is abi3l?

• - an attempt to employ agent based modelling to the freight industry

• What is Agent Based Modelling?

• forecasting on the basis of the behaviour of decision makers

• Why try?

• because existing methods are normally optimisation exercises for individual actors

• but the freight industry is far more complex

Defining the freight industry

• All those companies & authorities facilitating and initiating the movement of freight• Infrastructure owners & operators• Equipment owners & operators• End users

• In practice• Retailers & wholesalers• Ports• Shipping lines• Hauliers• Train operators• Forwarders• 3PLs• Road and rail network providers• Developers

• The decisions each make define the opportunities available to the rest• Freight almost entirely in the private sector

The challenge for freight investment policy

• Carbon reduction and freight’s contribution

The challenge for freight investment policy …

• Economic value from freight and logistics

• 5% of the total vehicle parc create 25% of emissions

• 5% of the vehicle parc creates up to 50% of the congestion on the national network

• Tonne-kms have decoupled but more will be needed


• Radical solutions are needed and will not be enough

• Will need to re-design networks to reduce T-kms


• Network investment will be increasingly private as public spending commitments are reduced

Department for Transport

£ billion

2010-11 2011-12 2012-13 2013-14 2014-15

Resource DEL1 5.1 5.3 5.0 5.0 4.4

Capital DEL 7.7 7.7 8.1 7.5 7.5

Total DEL 12.8 13.0 13.1 12.5 12.0

Policy elements

• …to drive change

• Regulation

• Fiscal measures

• Planning

• Private investment

• Public investment

• Complex interactions – difficult choices

Arguments for modelling & forecasting

• For the State• To get policies on taxation & investment• To regulate• To satisfy international agreements re climate change• To promote economic efficiency• Only achievable through understanding private sector

business decisions• For the private sector

• To make optimum investment decisions based on the behaviour of the other actors

• To accelerate pace of change by understanding how other actors behave, reducing risk

Illustrating interactions

• The more deep sea port development in the South East• The better the case for rail freight terminals in Northern England

• The more rail freight activity • The better the case for warehouse development on rail linked

sites• The more rail linked sites

• The more skeletal trailers required• The more rail network capacity required

• The faster imports grow from the Far East• The more deep sea port development• But where should be?

• A complex system!

Our process

• To establish interactions within the industry• A matrix of relationships• Populated by companies

• To populate those relationships with data• Synthetic cargo flows• Transport costs

• To model interactions• Modify parameters

• To produce a calibrated model

Data sources

• Through GB Freight Model/State

• Continuing Survey Road Goods Transport

• Network Rail

• Maritime Statistics

• Customs & Intrastat

• Privately sourced

• Generic data on production & consumption

Our offer to the Panel

• In exchange for advice on interactions in the industry

• To provide insight into the outcomes of our modelling and the applicability of the tool

• Our goal is to produced a suite of pilot models for different actors in the chain

Agenda



Strategy and software

• Strategic Software modeling

• Strategic, Tactical & Operational Models

• Software Approaches

• Strategy Formulation –

Optimization Vs Robustness

• Breakout Session – Format for response

Freight Software Document Inclusion

Existing modelling software

Software Approaches

• Evaluation/Simulation – Base position – Prescribing alternative Solution

• Optimization – Optimization Engine

*Based on internal data and evaluated after the event

Strategy Formulation

• Optimization Vs RobustnessOptimization may drive seemingly appropriate strategic decisions

in the form of capital investment yet does not mitigate risk

• 3663 Example 18.5 ton vehicles purchased to consolidate distribution (right

decision at time) Category growth in the Chilled product group

*In an uncertain world – awareness the whole picture matters!!

Strategy

• Matching Capabilities – Against Environment

Understanding the environment

(today & tomorrow)

is fundamental to success

Break Out SessionStrategic Decision Making Process

• External Factors

• Identification

• Incorporation

• Of ‘knowledge’

• Simulation of impact

• Consultancy

• applicable to your business?

• how, where, for what?

Briefing Documentfor breakout session – Strategic Modelling Software

• Identification – External factors to Strategic Planning, including innovations/technology, market data, competitor activity, research, government legislation, infrastructure limitations, etc

• Incorporation – How do I use the above information? What data is considered critical? How do I attribute relevance/importance to the above factors? (what factor is most important to me?)

• Simulation – Do I or can I model the potential impact of the above drivers to understand impact? What do I use to do so?

• Consultancy - What resources do I utilise, external/internal . Why do I value these resources? Where are they best used?

Agenda



Challenges & opportunities of complex environments

• How do we recognize complexity and why do we need to approach it differently?

• Why bother?

• What’s been done so far?

• What does good look like?

Why botherVery hard to model using formulas – because its all about autonomous behaviour

Possible model if we accept simple rules for each agent

• I want to survive (objective/goal)• Crashing is bad• Crashing with big things is very bad

And allow them to adapt by:• Choosing when to brake• Choosing when to accelerate

If agents learn accelerating into something big hurts then they brake when they are approaching a bus

Freight networks are similar

• The network is “self-organising”

• Warehouses are built without co-ordination

• Rail, road, and port investments are not co-ordinated

• Freight movements at a national level are not co-ordinated

• Policy is modally focused

Outcomes are therefore not predicted but emergent

Interdependencies

Transport Policy

Modal Investments

Modal Capacity

Modal Choices

Ship RailRoad

CO-EVOLUTIO

N

Some examples of ABM & Freight

Collaboration in Freight

Cost 1

Cost 2

Cost 3Adapted from: Krajewska & Kopfer (2006)

Collaboration in Freight

Cost 1

Cost 2/2 < Cost 1

Cost 3

Trivial until : • Large no of manufacturers• Large no of freight consolidators • Capacity constraints• Accumulation of benefits• Different contract lengths• Negotiation• Different pricing strategies

Optimal number of freight forwarders ?• Too many and scale effects are minimal• To few and power influences pricing strategy

Adapted from: Krajewska & Kopfer (2006)

Investment decisions

Demand

Fleet mix

New PriceRecoverabl

e price

• What rules work best and when do they work best?

• Long term investment• Short term investment

• Which vessel types should be invested in and when?

• Handysize• Panamix• Capesize

• First mover benefits• Differentiated rules by vessel type

Adapted from: Engelen et al (2010)

Behaviour and policy interactions

Agents allowed to plan routes under different policies – motorway charging and no charging

Agents that were allowed to make mistakes learned faster and outperformed those that didn’t make mistakes

Policy frameworks that incorporate learning are

more likely to achieve their objectives

Adapted from: Liedtke (2009)

What makes a good freight model

• Incorporates behaviour (choice & learning/adaptation

• Multi-modal

• Incorporates feedback

• Integrates freight and passenger travel

• General and not too specific

Source: Hedges (1971)

Literature review

Purpose of Model Source

Freight and passenger interactions

Zhang et al (2005); Peeta et al (2005)

Evaluate policy impact Liedtke (2009)

Minimise system cost Krajewska & Kopfer (2006)

Optimise location of infrastructure

Van Dam et al (2007); Wang & Jiang (2007);

Frameworks van Dam et al (2003); Roorda et al (2010); Hendher & Puckett (2005); Dong & Li (2003);

Asset Investment Engelen et al (2007)

Most of the literature describes frameworks

Modelling frameworks (single Layer)

Business Unit Agent

Attributes Assets Processes

Business Unit Agent

Attributes Assets Processes

1 Level of inter-agent relationship

Each Agent has a set of:• Assets

• Attributes • Processes

Modelling frameworks Multiple Layers

Each business unit agent has relationships with its own functional agents, and either separately or collectively the business unit/function agents form relationships with other

business unit/function agents

Business Unit Agent

Attributes Assets

Function Agent

Processes

Function Agent

Processes

Business Unit Agent

Attributes Assets

Function Agent

Processes

Function Agent

Processes

Summary

• Freight movement plays out on a rich landscape incorporating many agent types

• The freight landscape incorporates many dependencies

• Freight agents make autonomous decisions with limited knowledge of the big picture

• Limited visibility requires constant adaptation to an ever changing environment

Agenda



Logistics, Complexity and Modelling

• Cranfield’s Complex Systems Research Centre

• In reality - National is sum of Regional – which is sum of local etc. Structure is

multi-scaled!

• Structure is driven by decisions and policies concerning factors such as: Economic activities, salaries, rents and taxes. Industry, commerce, manufacturing, retail, services, finance….

• Logistics reflect and affect the distribution of people, ages, employment, education, crime, travel, patterns of demand, family size, health, lifestyle, unemployment etc.

Overall Development depends on Multiple Scales

Can we build an “emergent” distribution system?

• Can we build a system that will itself design and adapt its structure over time?

• It will need to represent the way that the many actors in the system operate, and how their actions affect each other.

Cost of production

Costs of transportTo Showroom

Costs of Showrooms

Costs of gettingTo Customer

Simple Case Study:Photocopiers across the UK

1

7

13

19

25

S1 S5 S9

S13

-10-50510152025303540455055

Av.No.Machines/day

Distribution of Demand

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

10-15

5-10

0-5

-5-0

-10--5

We can model the emergence of distribution centresThese might be warehouses or depots or might be centres of

repairs, spare parts etc.

Actual Photocopier Distribution Centres

1

6

11

16

21S

1 S5 S9

-100102030405060708090100110120

Machines/day

Actual Locations ofDistribution Centres

110-120

100-110

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

-10-0

The Case Study modelled had actual distribution centres as aboveThe questions asked are: How many Centres should there be?

Are the ones we have in the right places?

What goes into the model?

• Factory Gate prices

• Costs of distribution to showrooms

• Costs of showrooms (Fixed and variable) – with unit costs falling with volume

• Road Network distance to customers

A Self-OrganizingModel

•Demand is considered to be proportional to the population

•Distances are calculated using road networks that can provide both distance and time of travel

•Costs of warehousing depends on density of land-use

•Cost/unit of goods transfer depends on volume

Mathematics……..

k

ljk

lji

A

AjilAttraction

,

,),(

),(*)((Pr*exp, jiDostsTransportCiiceRAl ji

Attractionl(i,j) = attraction of Centre i as viewed by customer of type l located at j.R = Rationality (linked to homogeneity of customers l,

information…)Price(i) = Factory Gate Price(k) + Transport Costs to Centre i from k + costs at i.Factory Gate Price (k) = Capital costs, land, labour at kCosts at Centre i = Capital Costs, land, labour at i

Customer lat j

Centre iProductionat k

Can use a Model to create “emergent structure”:

RandomBehaviours

Attractors,Routines

Running Modelforward

Knowledge GenerationUnder different levels of disturbance

-5 -5 -5 -5

-5

0

5

10

15

20

Self-Organising LogisticsThe Initial Condition

15-20

10-15

5-10

0-5

-5-0

Each Cell is a small distribution centre

1

5

9

13

17

21S

1 S3 S5 S7 S9

S11

-100102030405060708090100110120

Machines/day

Location of CentresSuggested by Model

110-120

100-110

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

-10-0

CustomersChoice

NodeVolume

Price+

Can be a multi-level model:

• We can use the same type of program to build a multi-level set of centres

• This could test the advantage of having major points of distribution, or even having several levels

• It can tell us how many levels are necessary

• This will only apply if there are economies of scale in the transportation at different levels

Improved Transport Infrastructure for West Bengal

• Work with Asian Development Bank for West Bengal

• Survey by Halcrow Consultants of the current flows of goods on the roads

• Transport Infrastructure projects: effects on transport costs.

• Economic gains lead to increased consumption and production. Spatial multipliers on jobs created allows calculation of the “impact on poverty” – where and how much extra employment and wealth created

Application to West Bengal

market and costs

migration attraction

comparativeadvantage

comparativeadvantage

resourceuse

resourcesuse

potantialdegradation

investmentpopulationgrowth

spatial pattern of

natural resources

spatial pattern of

population economic activity

spatial pattern of

householdincome

demand

migrationattractivity

supplysupply

price

profit

investment

costscostsproductivity

vacancy

jobs

wages

population

water supply

adult

availableland

population

+

+

+-

+

- +

+

+

+

+

+

+

-

++

+

+

+ +

-

-

-

The System of Spatial Economic Multipliers:

Demand

Supply

Price

Cost

Profit Jobs

IncomeOf Zone

Population

TransportCost

+

-

- +

Transport InfrastructureProjects – Cost Reductions

-

-

-

-

++

+

++

++

This system was used to examine the spatial impactsof Transport Infrastructure Improvements in West Bengal

2025 – Jobs Created – Poverty Reduction….

Jobs in Agriculture, Industry and Services

Savings made by Poor, Medium and Rich

Effects of Investments:

Adding 50Million$ - 2 Scenarios

Scenario 1- equiv 26%, Scenario 2 - equiv 28%

0

50,000,000,000

100,000,000,000

150,000,000,000

200,000,000,000

250,000,000,000

300,000,000,000

350,000,000,000

400,000,000,000

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Ad

de

d G

DP

Np

rps

Scenario 2-1

Interest 1

Scenario 3-1

Interest 2

0.00

100.00

200.00

300.00

400.00

500.00

600.00

1 2 3 4 5

Gross Regional Products (BnNprps) 3 Scenarios 2017

GRP1

GRP2

GRP3

),(

),(),((Pr*

)(

),(*),(Pr*exp(

ilCost

ilCostilice

lSum

ilJobilyrityAttractiviInvestment

Is it worth investing in Nepal?

Spatial Implications by Sector:

0

200

400

600

800

1 2 3 4 5

New Agricultural Jobs 1997-2017

Scenario 1

Scenario 2

Scenario 3

0

50000

100000

150000

200000

250000

300000

1 2 3 4 5

New Industrial Jobs 1997-2017

Scenario 1

Scenario 2

Scenario 3

0

10000

20000

30000

40000

50000

60000

70000

1 2 3 4 5

New Service jobs 1997-2017

Scenario 1

Scenario2

Scenario 3

-300

-250

-200

-150

-100

-50

0

DjobAg DjobInd DjobSer

Region 1

Region 1

-400

-300

-200

-100

0

1 2 3

Region 2

Region 2

-600

-500

-400

-300

-200

-100

0

1 2 3

Region 3

Region 3

-20000

0

20000

40000

60000

80000

100000

120000

1 2 3

Region 4

Region 4

-1200

-1000

-800

-600

-400

-200

0

1 2 3

Region 5

Region 5

-300

-250

-200

-150

-100

-50

0

1 2 3

Region 1

Region 1

-400

-300

-200

-100

0

1 2 3

Region 2

Region 2

-200-150-100

-500

50100150

1 2 3

Region 3

Region 3

-20000

0

20000

40000

60000

80000

100000

120000

1 2 3

Region 4

Region 4

-200

0

200

400

600

800

1 2 3

Region 5

Region 5

Jobs -Scenario 2

Jobs – Scenario 3

Conclusions:

• This preliminary and simple model showed how infrastructure decisions and the changed patterns of freight distribution can re-structure the whole regional economy

• The models are based on the decisions of agents within the system and can explore the potential impacts of changed investments.

Agenda



abi3l approach

• A new framework – why and what is it

• DNA approach

• Model purpose

• Data and relationships

Why not a single layer approach?

What processes to include in each agent type?

Can a retailer also be a service provider?

End up with a lot of programming – which is

difficult to control, validate and verify !

Why not a multi-layered approach?

Business Unit Agent

Attributes Assets

Function Agent

Processes

Function Agent

Processes

Business Unit Agent

Attributes Assets

Function Agent

Processes

Function Agent

Processes

If each BU agent has 6 functional agents, and there are 10,000 BU agents we have a population of 60,000 agents and that doesn’t include the assets – Model is too big!!!!

DNA approach

• Each BU has a DNA which determines:• What it does e.g. Retailer and road transport provider

[agent type chromosome]• What commodities it deal with [commodities

chromosome ]• What types of Assets it owns [Asset chromosome]• Infrastructure ownership [infrastructure ownership]• A description of its strategy mix [strategy

chromosome]• What processes it can access [process chromosome]

Genetic structure

Chromosome No of genes

Genetic influence

Agent type 12 Defines process template required to function

Commodity type

33 Determines what sort of agents it can form relationships with

Assets 10 Used in conjunction with agent type to define what assets are required to support the process template

Infrastructure 8 Determines who the agent forms relationships with and what processes are accessed

Processes 29 What process the agent carries out

Strategy 5 How the agent measures performance and its attitude to the environment

In theory this approach would allow in excess of 16,000 agent types and characters in excess of 7000 for each agent type

An example

The Agent chromosome

Retailer

Freight service provider by Rd

Warehousing service provider

•What sort of retailer?

•What sort of product?

•What sort of warehouses?

•What sort of vehicles?

An example (continued)

• Commodity chromosome indicates

• Grocery

• Clothing

• Asset Chromosome indicates

• Ambient HGVs & warehousing

• Chilled HGV’s & warehousing

• Frozen HGVs & warehousing

An example a bit more

• Strategy Chromosome

• Cost

• Collaboration

• Fast learning

• Innovative

• Sources domestically & imports

Prioritise partners who are low cost and have propensity to collaborate

Frequently measure performance and adapt to changes in performance

An example – and finally

• Process chromosome

• Capability of managing movements from NDC and RDC

Seeks partner to manage and move imported product from port to NDC

Summary

1

0

1

0

0

0

1

0

0

0

What I am

What infrastructure I have

What I move

What I assets I have

What process capability I have

What strategies I use

What don’t I do

Process Template

Man

age

impo

rts

Exe

cute

por

t m

ovem

ents

Sto

re c

onta

iner

s

Man

age

mov

emen

ts

Exe

cute

mov

emen

ts

At

port

At

hub

At

ND

C

At

RD

C

At

Dep

ot

Who

lesa

le

Mov

e fr

om D

epot

Mov

e fr

om R

DC

Mov

e fr

om N

DC

Mov

e fr

om h

ub

Mov

e fr

om W

hale

sale

Man

age

Mov

emen

ts

Gen

erat

e O

rder

s

For

mul

ate

Pol

icy

Freight Owner op op 0 op 0 0 0 0 0 0 0 0 0 0 0 0 op 1

Freight Mover op 1 0 op 1 0 0 0 0 0 0 1 1 1 1 1 1 1

Infrastucture Owner 0 0 1 op 0 1 1 1 1 1 1 0 0 0 0 0 0 1

Policy Maker 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Compare with the processes I do and based on what I am identify what sort of relationship(s) I need to form

Relationship choices are conditioned by need [capacity], commodity and strategy

Data Structure

Freight Movements

Cost Data

Origin - destination

Infra - structure

Operating costs

Ship

Road

Rail

Ship

Road

Rail

Ship

Road

Rail

Assets

Data Challenges

• European Movements – not captured and will need to be populated based on assumptions

• Origin-Destination data does not make clear the degree (primary, secondary or tertiary)

• Data Maintenance

Data Use

• Data inputs will set the initial conditions for the model

• “Old” data can be used to seed model and “current data” to calibrate/validate the model

Purpose

• To inform the design of policy (what impact)

• To validate policy (intended vs actual outcomes)

• To inform organizational strategy (robust vs optimised strategy)

• Risk management and mitigation

• System carbon and economic optimisation

Finally back to where we started

Agenda



Documents

Abi 3 l First Advisory Panel Meeting Dr Liz Varga [email protected] 25 th Nov 2010