Infraday 2009

Consideration of logistics for policy analysis with freight transport modelstransport models

Hanno Friedrich (hanno.friedrich@iww.uni-karlsruhe.de)( @ )Gernot Liedtke (gernot.liedtke@iww.uni-karlsruhe.de)

15 10 200915.10.2009

AGENDA

• Motivation

• Representation of logistics in freight transportation modelsRepresentation of logistics in freight transportation models

• Lessons learned

1

NEED OF LOGISTICS IN TRANSPORTATION MODELS

• Rising attention for logistics in politics– Masterplan Logistics EU– Masterplan Logistics EU– Masterplan Logistics Germany

• Higher relevance of freight transport in overall transport

• Logistics as natural interface between economic activity system and transportation system

2

DIFFICULTIES IN MODELING FREIGHT/LOGISTICS

• Heterogeneity of actors and data availability

• Freight transport emerges from Definition Logistic Mesostructure:

g p ginteraction between logistic systems using synergies (concave cost functions)

• The routing of freight flows therefore

A logistic mesostructure is an emergent operational structure, that handlesseveral commodity flows. It can be described by how, where and when• The routing of freight flows therefore

is dependent on:– Existence other flows– Networks/tour structures

ythe goods of the commodity flows are transported, reloaded and stored. Amesostructure is the result of an optimization of one or several actors

– Distribution structures – Locations of warehouses and

warehouse structures

punderspecific circumstances. These circumstances include the state of the actorsactorsand the state of their environment.

Need to consider combinations of flows and necessity to model logistic

mesostructures

3

CHOICES – FROM ECONOMIC ACTIVITY TO VEHICLE FLOWS

Company Aspiration Choices

Logistic choices

Company Aspiration Choices (Profits, Growth)

Activity Pattern Choices(P d t Mi M k t V l )(Product Mix, Markets, Volumes)

Business Location Choices (Factory, Distribution Regions)

Sourcing Choices (Suppliers)

Supply Path Choices

Logistic Location Choices(Warehouses, Reloading Points, Network design)

Supply Path Choices

Lot Size / Frequency Choices

Mode Choices

Dispatching Choices (Tours, Actual operational lot size)

Lot Size / Frequency Choices

4

THE MICRO MACRO GAP

Macro Level: Traffic load

Aggre-gated

indicators

Meso Level: Transport meso-t t (T)

Aggregation possible

structures (T)Lorry survey

n no

t pos

sibl

e:

omm

odity

flow

not p

ossi

ble:

m

odity

flow

sf(n*T)=n*f(T)

Logistic meso-structures (L)

ECHOsurvey

Dis

aggr

egat

ion

Sin

gle

trip ≠

co

Agg

rega

tion

Trip

s ≠

com

m

Optimization and/or

Micro Level: Commodity flows (F)

D and/orsimulation

f(n*F) ≠n*f(F)

Micro Level: Commodity flows (F)

Shipper survey

5

AGENDA

• Motivation

• Representation of logistics in freight transportation modelsRepresentation of logistics in freight transportation models

• Lessons learned

6

Reviewed models

OVERVIEW SELECTED MODELS

ry m

odel

DTR

IP

E/S

LAM

Appr

oach

mod

el

ET2.

0

R P ARLO

G

RA

DE

Cal

gar

GO

OD

SM

ILE

ADA

A

Toky

o

EIU

NE

WIV

ER

BVW

P

ASTR

A

Logistic locations

INTE

R

SYN

T

Logistic locations

Transport paths

evel

s

Mode

c ch

oice

le

Lot size

Logi

stic

Tours

Modeled on disaggregate level (actors, flows between actors or vehicles)

7

Modeled on aggregate (flow) level

INTERLOG – MODEL OVERVIEW

Company generationGeneration Company generationGenerationForwarder generation

Set of attributed companies in space

modulesGeneration Forwarder generation

Set of attributed companies in space

modulesGenerationGeneration

Sourcing module: choice of suppliers

Commodity flows between companies

Distribution Sourcing module: choice of suppliers

Commodity flows between companies

DistributionDistribution

Marketinteraction

module

Shippers models

Forwarders models

Market choiceLorry model

Lot-size choiceMarket

interactionmodule

Shippers models

Forwarders models

Market choiceLorry model

Lot-size choiceMarket choiceLorry model

Lot-size choice

Attributed lorry trips on the road network

Aggregation verification

yNetwork model Attributed lorry trips on the road network

Aggregation verification

yNetwork model

yNetwork model

Aggregation, verificationAggregation, verification

8

INTERLOG – ARTIFICIAL INDUSTRY LANDSCAPE

9

INTERLOG – RESULT: VEHICLE FLOWS

10

SYNTRADE – MODEL OVERVIEW

Model Core W h

Input data and generation of commodity flows

Simulation of h

Output

Simulation of l i ti i

Simulation model

General data• Distances • Regions

Model core

• Warehouse structures

• Pallet km (food retailing sector)

• Shipment structure

warehouse structures in the food retailing sector(model core)

logistics in consumer goods distribution (model periphery)

Model core• Food retailing

companies– Outlet structure– Article structure

• S i

• Optimization heuristic for warehouse structure

p(for inbound transport)

Model periphery • Pallet km (consumer

( )

• Bundling of commodity flows

• Lot size optimization on • Sourcing

Model periphery• Artificial industrial

landscape

including – Number– Levels– Locations

Allocation

• Pallet km (consumer goods distribution)

• Shipment structures for interregional transport flows

ptransport links

• Supply path optimization for commodity flowsp

• Imports• LSPs and

Wholesalers • Consumption in

regions

– Allocation• Forward looking

elements: changes in lot sizes and regions

• Generation of good flows to regions

transport paths

11

E i ti h l ti Si l t d h l ti

SYNTRADE – RESULTS: WAREHOUSE LOCATIONS

Existing warehouse locations of food retailing companies in Germany

Simulated warehouse locations of food retailing companies in Germany

12

SYNTRADE – RESULTS: FUEL PRICE SCENARIO

Change in pallet km(Mio. pallet km) Change in warehouse structures

B i

44.335

Base scenario

36.935

7.400

Fuel price scenario (Assumed change of +25% in transport costs )

37.374

44.143

6.769

Via retailer DirectTotal

13

Via retailer warehouses

DirectTotal

AGENDA

• Motivation

• Representation of logistics in freight transportation modelsRepresentation of logistics in freight transportation models

• Lessons learned

14

LESSONS LEARNED FOR FREIGHT TRANSPORT MODELING

Challenges Solution approaches

• Data availability • Artificial generation of disaggregated datay(modeling heterogeneity)

g gg g• New data sources through more detailed

modeling of sectors

• Complexity– Combinatorial problems

• Simplified but realistic heuristics • Modeling markets and market interaction

– Involvement of many actors in decisions

g• Possible simplifications through market

modeling: simplified representation of supply or demand or the overall market outcome

• Reaching “realistic” overall system states through simulation

• Choosing realistic decision scopes and heuristicsthrough simulation and heuristics

• Forward looking elements in simulation

15

CONCLUSIONS FOR POLICY ANALYSIS

• State of the art freight transport models only include basic logistic aspects

• Recent developments try to include more complex logistic structures but are still limited in scopestructures, but are still limited in scope

• Policy analysis on effects of logistics on transportation by freight transportation models is therefore limited

16