Corporate Technology Mathematical Optimization at …co-at-work.zib.de/berlin2009/downloads/2009-09-29/2009-09-29-1630... · 1980: EWSD (digital ... Corporate Technology Corporate

Copyright © Siemens AG 2008. Alle Rechte vorbehalten.

Corporate Technology

Mathematical Optimization at Siemens Michael Hofmeister

Siemens AG Munich, Corporate Technology

Seite 2 07.07.2009 © Siemens AG, Corporate TechnologyMichael Hofmeister

Outline

Siemens Company – a short introduction

Mathematics at Siemens Corporate Technology

Assembly of printed circuit boards

Water supply network planning

The internet of things

Outlook


Siemens CompanySiemens' innovations have changed the world

From the first electronic controls –to fully automated factories

From the invention of the dynamo –to the world's most efficient gasturbines

From the first interior view of the body –to full-body 3D scansHealthcareHealthcare

IndustryIndustry

EnergyEnergy


Siemens Company Milestones for Three Centuries

1881: Electricalstreetcar

1993:ElectronMicroscope

1924: Traffic light1881: Telephone switchboard

1879: Electricalrailway 1866: Dynamo

1847: Telegraph

1905: Tantalum lamp

1905: First R&D laboratory

1962: Thyristorfor energy

1965: Integratedcircuits

1935: Coax cable

1974: Computertomography scanner

1980: EWSD (digitalswitching technology

1988: Megabit chip

1958: Heartpacemaker

1931: „Hot air shower“(hair-dryer)

1906: Vacuum cleaner

1953: Very clean silicon

1985: High speedtrain ICE (300 km/h)

1959: Transistor baseduniversal computer

1984: Hicomtelephoneswitchboard

2003: Maglev in Shanghai

2004: Bio-lab on chip

2005: Electronic wedge brake

2000: Piezo Injectionfor diesel engines

2000: syngo GUI 2004: Full lengthMR tomography

2005: Home entertainment

2004: 64 lines computertomography scanner

1994: High temperaturefuel cell

1998: Terabit switch 1998: Most efficientPower plant

Sales in 2005:75,445

Billion Euro

Employees in 2005:461,000

Information and CommunicationEnergy Industry Automation MobilityHealthcare Lighting Technology Household Appliance

Innovation for: Lo

garit

hmic

scal

e

1847: Wernervon Siemensfounded the

company


Bracknell

LissabonZaragoza

Toulouse

Eynsham

Brüssel

Mailand

Roke Manor

Paris Zürich

Porto

Tel Aviv

BurlingtonAuburnHills

Mülheim

SalzburgGraz

Karlsruhe RegensburgErlangen-Nürnberg

BerlinDresden

Istanbul

Helsinki

Arlington

Orlando

Buenos Aires

Curitiba

Penang

Taipei

Seoul

Shanghai

Bombay

Issaquah

Bangalore

Peking Tokio

Lake MaryAustin Newport News

PrincetonPiscataway

Norcross

Melbourne Sydney

Linz

Changchun

Xi‘an

ChengduNanjing

Tianjin Ichon

TilburyPeterboroughDrummondville

Chatham

Knoxville

Johnson City Madrid

SophiaAntipolis

NetanyaNeu-Delhi Kakegaw

a

KawasakiYokohama

Hong Kong

Goa

BerkeleyConcord

Santa ClaraSan Diego

Mountain View

Sacramento

San José

Athen

St. Petersburg

Treviso

Oslo

Moskau

Pretoria

GöteborgPandrup

MünchenWien

Danvers

Budapest BratislavaPittsburghHoffman

nEstates

London

Sao Paulo

Zagreb

Bogota

BredaHengelo

Amsterdam

Timisoara

Prag

Guadalajara

Singapur

Siemens Company Almost 50.000 R&D Employees at 150 Locations


Corporate Technology Innovation Network of the Integrated Company

Chief Technology Officer (CTO)

Evaluation of innovation strategy Technology based support of synergiesassurance of innovative powerEvaluation of technologiesGuidance

Customers

Corporate Intellectual Property and Functions (CT I)

Intellectual Property Services & StrategyStandardization, Environmental AffairsGlobal Information Research

Corporate Research and Technologies (CT T)

Global Technology Fields with multiple impactPictures of the FutureAccelerators for new business opportunities

Corporate Technology (CT)

Reg

ions

Sectors / Divisions

Industry Energy Healthcare

Chief Technology Office (CT O)

Direct supportof CTO

Siemens IT Solutions and Services (SIS)Siemens Financial Services (SFS)


Corporate Technology Modeling, Simulation, Optimization

Control SystemsLocalization Technologies

Digital Product

Mathematical Engineering

Smart Decision Support

Autonomous Learning, Prognosis and Diagnosis

Discrete Optimization

Learning Systems Virtual Design

Value Chain Optimization


Research and InnovationThe Mindset

Mathematics evolves its impact most effectively if it appears as a PARTNER together with other

sciences and technologies


Giants …

• GUI

… and Dragons

• Commissioning

• Data base

• Software Engineering

• Incorrect inputs

• Bug Fixing

• Interfaces

• 100k LOC

• Operating system

• Middleware

• External systems

Research and InnovationThe Mindset

Dwarfs and elves• Modeling

• Theorems and proofs

• Algorithms


Research and Innovation From Modeling to Application

Modeling

Realization

Integration


Distribute components to all stations in order to achieve a well-balanced line

Assembly of Printed Circuit BoardsOptimizing the automaton

Construct short paths on the board for each headfor fast placement

Arrange component feeders for fast picking sequences

Select and configure nozzles for each headsuch that the numberof placement cyclesis minimized

Comp. Types Nozzle Types


Assembly of Printed Circuit BoardsCoherence in Systems Hierarchy

FactoryLayoutDispatching and schedulingStaff assignmentMaterial supplyMaintenance planning

Production LinesMatched line configuration of machinesOptimum component feed along the lineTrade-off between set-up times and processing times

Machine LevelTiming and spatial constraints of single machinesNecessary resources behaviorAvailability

Coherence

of Models

Semantic

Coupling


Assembly of Printed Circuit BoardsNozzle Selection

Given:Bipartite Graph such that

Component type u may be placed through nozzle type v

For every node u ∈ U a component number

For every edge ( u,v ) ∈ E an integer variable

For every node v ∈ V an integer variable

k = Number of nozzle positions at the placement head

Variable z = Number of placement cycles

),( EVUG ∪=

⇔∈ ),( Evu

ub

uvy

vx

Nonlinear model:

integers nonegative variablesAll

:

: such thatmin

),(:

),(:

kx

zxyVv

byUu

z

Vvv

vEvuu

uv

uEvuv

uv

≤

≤∈∀

=∈∀

∑

∑

∑

∈

∈

∈

Minimize no. of placement cycles

All components have to be placed

Set up a sufficient number of nozzles at placement head

Upper limit for nozzle segments

Runtime requirement: At most 5 ms!


Water Supply Network PlanningA Simple Network


Required: Schedule of pump switches for the planning horizon (24h)Required: Schedule for valves

Input: Contract with energy supplier

Objectives: 1. Safe operation2. Full supply of consumers3. Minimal costs for water and energy4. As few as possible pump switches

Required: Fill levels forthe reservoirs

Input: Contract with other water suppliers

Water Supply Network PlanningInputs and Goals

Input: Demand forecast for consumers (districts)


Required: Schedule of pump switches for the planning horizon (24h)

Required: Schedule for valves

Objectives: 1. Safe operation2. Full supply of consumers3. Minimal costs for water and energy4. As few as possible pump switches

Required: Fill levels forthe reservoirs

Input: Contract with other water suppliers

Water Supply Network PlanningInputs and Goals

Input: Demand forecast for consumers (districts)


Water Supply Network PlanningGeneral Setting for Optimization

• Demand forecast on a hourly basis;Planning period: 24 h

• No consideration of pressure progression


Water Supply Network PlanningSubnets

Subnet 1

Subnet 2

Subnet 3


Water Supply Network PlanningGraph Model

Subnet 1

Subnet 2

Subnet 3

Feeder 1

Supplier

Consumer 1

Consumer 2

SpringWell


Min cost flow solutions

vB

fP

t

t

Schedules / fill levels

vB

fP

t

t

Water Supply Network PlanningCreating pump switches

Solutions of the model have to betransformed into switching points of time for the pumps and fill level curvesfor the reservoirs


Water Supply Network PlanningThe Real Planning Cycle

Compute schedule:Pump switches, fill level curves

Evaluate schedule:Pressure progressionModify specifications

Activate schedule

Initialize planning:Set current constraints

Optimization

Simulation


Water Supply Network Planning The Toolset

Siwa-Plan is a toolset for the management of water supply networks, including planning and control functions.

Using Siwa-Plan, the first step is the engineering of the water net under consideration.

In addition to the implementation of optimization and simulation routines, the challenge is to generate an appropriate optimizer and simulator.

OPTIMSchedule

Administration,Prognosis

SEWERSewerControl

LEAKLeak Monito-

ring

SIMTRAIN

Simulation

BEPPump

Optimization

VIMEngineering

Tools


Water Supply Network PlanningIntegration Into Control Equipment

Bürowelt / Office

@aGlance / OPC-Server

Internet/Intranet

O&M

Terminal Bus Ethernet

OS Clients / Multi-Clients

OS-Server

Service

EngineeringStation ES

Industrial Ethernet / System Bus

OS

OPET 200M

PR

OFI

BU

S-D

P

PROFIBUS-PAPROFIBUS-DP

ET 200iS

PROFIBUS-PA

ET 200MEx-I/O

SIWACIS PLAN


The Internet of ThingsRouting Goods Like Data

Baggage handling atBeijing airport

Mail distribution centers

The Internet of Things –A public funded project *)

*) German Federal Ministry ofEducation and Research


The Internet of Things Hierarchical Structure of Commissioning Systems

HardwareIT-Structure


The Internet of ThingsBasic Concepts

New controls architecturesMigration of “Intelligence" to field level (e.g., "intelligent drives")Distributed controlling functionsVernetzung of devices at field level ("industrial ethernet")Web based services for the level of controls ("one homepage for every motor")

New systems architecturesDistributed software systemsDecentralized controls (e.g., agent technology)"Aware objects" (RFID, RF-based systems)Pervasive computing

New material flow strategies"Routing goods like data" (e.g., like e-mail)Methods of network management (e.g., IP routing) in physikal-logistic systems


The Internet of Things Negotiations of Local Agents

…

OK, I have to be X-rayed next …

Ouuh, I am skeptic. I have to work off the main baggage stream

for Flight No. XYZ.I decline.

Well, I can do the job. I expect you in five

minutes.

Auction


The Internet of Things Localization of Agents

Where to localize the agents?

At the field modulesModule agents have to manage their own workload in order to avoid congestion

At the material to be conveyedBaggage agents are responsible for their route through the net


The Internet of Things State Space of Agent Systems

Available informationMuch: global, up-to-dateLittle: local, out-of-date

Algorithmic complexityHigh: Complex computationsLow: Simple conditional rules

ScenariosTopology, dispatching, error handling, etc.

Algorithmic complexity

Available information

Scenarios

Much

Little

HighLow

Own space → difficult to formalizeE.g., density of topology network

??


The Internet of Things A Simple Scenario

Sorter

Sorter

Transfer

Che

ck in


The Internet of ThingsThree Scenarios

B: What is your length?C: x metersB: What is your expected workload at time t1?C: y parcelsB: Expect me at time t2!C: Well, I will update my forecast.

Knowledge about itself:LengthExpected work load progression

Algorithm: DijkstraRequired edge information:

Lengths of edgesExpected workload at time t (-> Dynamic penality cost)

Anticipatory

B: What is your length?C: x metersB: What is your current workload?C: y parcels

Knowledge about itself:LengthCurrent workload


Length of edgesCurrent workload (-> Penality cost)

Up-to-date

B: What is your length?C: x meters

Knowledge about itself:Length


Lengths of edges

Stupid

CommunicationConveyor agentBaggage agentScenario name

For all scenarios: Baggage agents plan their route exactly once, at Check In.Congested edges will be ignored.


The Internet of Things Stupid Routing – Situation Short After Dispatching

chec

k in

transfer sorter

sorter


The Internet of Things Anticipatory Routing – Situation Short After Dispatching

chec

k in

transfer sorter

sorter


Conclusion

The rapid increasing complexity of industrial challenges requires usage of mathematical methods of modeling, simulation and optimization to a great extend. Mathematicians and computer scientists are invited,

To improve available tools and methods continuously, and

To assist usage of such methods within projects along the complete life cycle of products, plants and systems.

The tight, project related collaboration with engineers and users is essential for the successful application of such methods.


Thank you for yourattention!

Contact:Michael HofmeisterSiemens AG, CT PP 7Otto-Hahn-Ring 6D-81730 [email protected]

Documents

Corporate Technology Mathematical Optimization at …co-at-work.zib.de/berlin2009/downloads/2009-09-29/2009-09-29-1630... · 1980: EWSD (digital ... Corporate Technology Corporate