research overview

© C.Hicks, University of Newcastle

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Research Overview

Christian Hicks

http://www.staff.ncl.ac.uk/chris.hicks/presentations/presin.htm


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Research InterestsMain areas:• Simulation / modelling of manufacturing systems• Scheduling / planning and control• Supply chain management• Manufacturing Layout

Other areas include:• IT implementation • Company classification• Manufacturing strategy • Web-based teaching• Data / statistical analysis • Business Process Analysis• Benchmarking in the semiconductor industry


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Capital Goods Companies: Generic Issues• Products are highly customised and are produced on a

make, or engineer to order basis.• Production facilities include jobbing, batch, flow and

assembly systems as well as construction.• Lead time reduction increasingly important.• International competition: effective and efficient use of

resources is very important.• Complex and dynamic supply chains.• Product offering broadening to include service

elements.• Involves civil, mechanical and electrical engineering.


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Simulation of Manufacturing Systems


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Manufacturing Planing & Control System

Manufacturing Facility

Manufacturing System Simulation

Planned Schedule

Resource information

CAPM modules used

System parameters

Product information

Operational factors

System dynamics Logic

Measures of performance

Flow measurement Cluster Analysis Layout generation methods

Tools


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Key Features• Large scale model allows whole manufacturing facilities

to be represented.• Models facilities, products, processes, layout and

planning and control systems.• Many product families can be represented with shallow,

medium or deep product structure.• Hierarchical description of products and resources.• Allows variety of planning and control methods to meet

local requirements.• Integrated with scheduling and layout optimisation tools.• Comprehensive stochastic modelling.


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Schedule Optimisation


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check and reordercomponents

SolutionSpace

Chromosome

Chromosome

Chromosome

::

Parent 1

Parent 2+ ==>

Offspring 1

Offspring 2

Parent 3 ==> Offspring 3

Mutation Operation

Crossover Operation

Genetic OperationPopulation

Fitness Measure

Offspring 1

Offspring 2

Offspring 3

FitnessTesting

random

encode

decoding

selection

next generation

Repair Process

Start

Terminate?

Stop

noyes

Chromosome

RouletteWheel

chromosomeselection

check and reorderoperations

identify and avoiddeadlock

check capacityand adjust timing

randomly

combine

genes

Schedule optimisation using Genetic Algorithms


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Product

1st Operation

Assembly

Component

Initial Schedule


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New schedule from GA


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Stochastic Planning Methods

• Developed methods that either meet a service target or minimise the combination of earliness and tardiness costs.

• Investigated approaches for infinite capacity, finite capacity and dynamic scheduling cases.

• Planning methods investigated and validated through simulation modelling.


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Manufacturing Layout

• Clustering– Matrix-based methods– Similarity coefficient methods

• Optimisation– Genetic Algorithm– Simulated Annealing

Dendogram based upon Similarity Coefficients


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Genetic Algorithm Procedure

Start Encode GenesChromosome

Chromosome

Chromosome

Ran

dom

ly c

ombi

ne g

enes

Crossover Function

Parent 1

Parent 2

X

Offspring 1

Offspring 1

Parent 1 Offspring 1

Mutation Function

Genetic Operators

Ran

dom

ly s

elec

t chr

omos

omes

Check and eliiminateduplication

Produce layout usingplacemenrt algorithm with

constraint checking

Evaluate "fitness" in termsof total direct / rectilinear

distance travelled

RouletteWheel

Stop

Terminate ?

Display

Create population forgenerationYes

No


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Placement Algorithm


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Initial Layout


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Resultant Brown-field layout


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Total Rectilinear Distance vs Generation

0

100000

200000

300000

400000

500000

600000

700000

800000

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191

Generation

Tota

l Rec

tilin

ear

Dis

tan

ce (

m)

Minimum

Average

Population size 200Generations 200Crossover 90%Mutation 18%

Total rectilinear distance travelled vs. generation (brown field)


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Supply Chain Management


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Supply Chain Management• Modelled business processes using SSADM• Company structures range from vertically integrated to project

integrators that outsource all manufacturing.• Important factors include: available capital, risk, potential

utilisation of plant, capabilities, flexibility.• Three stages of interaction with customers: marketing,

tendering and contract execution

• “Normal” / “radical” design

• Functional vs. technical specifications• Procurement decisions made by: customers, designers,

procurement departments


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Summary of contributions• Planning, control and layout problems in capital

goods companies. Outcome: first large-scale simulation model of manufacturing in capital goods companies

• Scheduling complex products in deterministic and stochastic environments.

Developed first optimisation techniques.

• Layout analysis and optimisation. Developed integrated tool.

• Supply chain management in capital goods companies.New models proposed and linked to strategic issues.


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Classification


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Simulation


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Representation of ResourcesCompany

Factory(code 1000)

Department 1Code 1100



Cell1110

Machinecode 1111

1112

1120 1210 1220 1230

1211 1212

Figure 16 Representation of Resources


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234

621 679

452

213 321

2 99 17

Part codes

Representation of Products


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Stochastic Simulation

• Several random number generators: Knuth, Wichman & Hill, SunOs.

• Normal [polar form of Box-Muller (Marsaglia and Bray 1964); Beta (Press, et al. 1989, p188), Gamma (Press, et al. 1989, p228), Poisson (Press, et al. 1989, p230) as well as Log normal, Multi-modal, Exponential, and empirical (based on historical data).

• Full / fractional factorial designs• ANOVA / Regression analysis


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Scheduling


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Chromosome representation

P 110 1

P 11 0 3

P 120 1

P 120 2

P 110 2

Sub-chromosome 1

Sub-chromosome 2

Machine 1

Machine 2

P 120 2

P 110 2

P 110 1

P 11 0 3

P 120 1

Chromosome

P ...0 ...

P i0 j

Machine n

P i = Part or component number i

O j = Operation number j

Resource no. 1

Resource no. 2

P ...0 ...

Resource no. nSub-chromosome n

P i0 j


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Crossover OperationsInitial Description Reference BCGA

CX Cycling crossover Oliver et al., 1987 ER Edge recombination Whitley et al., 1989EERX Enhanced edge recombination crossover Starkweather et al., 1991 AEX Alternating edges crossover Greffensette et al., 1985MPX Maximal preservation crossover Mühlenbein et al., 1992 1PX One point crossover Murata and Ishibuchi, 1994 OX Order crossover Davis, 1985 PBX Position based crossover Syswerda, 1991 IPX Independent position crossover Murata and Ishibuchi, 1994PMX Partial matching crossover Goldberg and Lingle, 1985 LOX Linear order crossover Falkenauer and Bouffoix, 1991 SCX Sub-tour chunk crossover Greffensette et al., 19852PEX Two points end crossover Murata and Ishibuchi, 1994 2PCX Two points centre crossover Murata and Ishibuchi, 1994 2PECX Two points end / centre crossover Murata and Ishibuchi, 1994DX Diagonal (three parents) crossover Eiben et al., 1989


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Mutation Operations

Initial Description Reference BCGA2OAS Two operations adjacent swap Murata and Ishibuchi, 1994 3OAS Three operations adjacent swap Murata and Ishibuchi, 1994 2ORS Two operations random swap Murata and Ishibuchi, 1994 3ORS Three operations random swap Murata and Ishibuchi, 1994 IM Inverse mutation Goldberg, 1989 SOM Shift operation mutation Murata and Ishibuchi, 1994 CIM Centre Inverse mutation Tralle, 2000 E2ORS Enhanced two operations random swap Tralle, 2000


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Fitness function

Minimise : Pe(Ec+Ep) + Pt(Tp)

Where Ec = max (0, Dc - Fc)

Ep = max (0, Dp - Fp)

Tp = max (0, Fp - Dp)


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An Example of Production Plan

Operation 1

Operation 1

Operation 1Dc (part 2)

Dc (part 1)

Ec 2 Ec 3

Part 1(Component 1)

Operation 2

Operation 2

Part 2(Component 2)

Part 3(Assembly)

Ec 1

Inte

rnal

due

dat

e (pa

rt 3)

Exte

rnal

due

dat

e (pa

rt 3)

Ep 1

KeyDc = Component due dateEc = Earliness of componentDp = Product due dateEp = Earliness of product


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Layout


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Rank Order Clustering (King 1980)


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Case Study

• 52 Machine tools• 3408 complex components• 734 part types• Complex product structures• Total distance travelled

– Direct distance 232Km

– Rectilinear distance 642Km


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0

100000

200000

300000

400000

500000

600000

700000

800000

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191

Generation

To

tal r

ecti

linea

r d

ista

nce

(m

)

Average

Minimum

Total rectilinear distance travelled vs. generation (green field)


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Resultant green field layout

Note that brown field constraints, such as wallshave been ignored.


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Collaborating Companies

• NEI Parsons• AMEC Offshore• NEI International Combustion• Clarke Chapman• Wellman Booth• Control Systems• Reyrolle (VA Tech)


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• Identified the characteristics of the companies in terms of products, processes, markets, level of outsourcing etc.

• Investigated buyer/supplier relationships in terms of supplier base, strategic alliances, partnership and single sourcing agreements etc.

• 3 stages: marketing, tendering, contract execution• Physical / non-physical processes,• Differing levels of vertical integration• Procurement often reactive rather than strategic


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SCM (continued)• Majority of controllable cost committed at the design

stage.• Normal / Radical design• Established / ad-hoc business processes• Product offering broadening – shift from just

hardware to retrofit, service and operations.• There are high levels of uncertainty and sparse

knowledge, particularly at the tendering stage.• Tendering is often subject to severe time pressure

and resource constraints.


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Company X - Context Diagram

Company X

ITT

Tender

ContractAwarded

aCustomer

aCustomer

ProgressReport

bSupplier

bSupplier

QuoteITT Order


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D ata F low D iagram - H igh Leve l

Tendering1

P repareTender

ITT

C ustom er

Tender

D1 ITT & Tender

ITT (copy)

Engineering2

D es ign fo rTende r

ITT (copy)

D2 Supplier Deta ils

Suppliers& Costing

Q uote

Q uality3

P repareC Q A R

ITT (copy)

CQ AR

Designs, TPS, PPRecom m end Suppliers

Projects5

Plan & CoordinateProject

ContractF ile

Engineering6

Conceptual &Detailed Design

ContractF ile (copy)

7 Purchasing

Supplier Selection,O rdering &Expediting



ProgressReport

ProjectPlan

ProgressReport

ProgressReport

ProgressReport

Drawings,M anuals

DrawingsDrawings

M3 Contract F ile

D/M4 Client Corresp

Approv eP.O .

8 Q uality

ITP & SupplierApproval

ITP

Update

S upplie r

S upp lie r

Q uote

SupplierApprov al

PurchaseO rder Expedite

Inspectionreport

SupplierApprov al

M5 Pref. Suppliers

Supplier

M6 Historic Designs

Designs

M5 Pref. Suppliers

M6 Historic Designs

Supplier

Designs

M/D7 Suppliers

Supplier

D8 Prev ious Suppliers

ContractAwarded

G en. M anager4

A pprova l O fTende r Tender

G.M

B R R

9ProjectReport

Action

Q uote

New DesignsSupplier

PurchaseO rder(copy)


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IT Implementation in Small Companies

• Selection and implementation of Accounting, planning and control systems and EDI in surgical shoe manufacturing company.

• Selection and implementation of a cost estimating system in a precision machining company.

• Selection and implementation of accounting and order processing systems in an electrical cable manufacturing company.


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Benchmarking in the semiconductor industry

• Benchmarking survey of four Siemens plants: WhiteOak (USA), ProMos (Taiwan), Siemec (Germany) and NTS (England).

• The design, construction, and operation of the plants are different leading to different consequences in terms of capital and operating costs, quality, etc.

• The research aims to establish the relationship between capital cost, construction lead-time and operating costs and performance.


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Postgraduate Students

• Dong Ping Song “Stochastic Models in Planning Complex Engineer-to-Order Products”

• Pupong Pongcharoen “Genetic Algorithms for Scheduling in the Capital Goods Industry”

• Tony Wells “Benchmarking in the Semiconductor Industry”

• Fouzi Hossen “Risk Management in Capital Goods Companies”

• Thanawat Muangman “Supply Chain Management in the Capital Goods Industry”


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Research Grants• “An investigation into the design change management process

and the relationship to resultant cost of change for capital equipment at BNFL”, £160,000, 2003, (CH/PMB/WO)

• “Harris & Sheldon TCS”, 2000-2, £81,408, discontinued, (RID/CH/PMB)

• “Haani Cables TCS”, 98-2000, £141,760, (CH/TMcG/MG/PMB)• “SCM at Wellman Booth”, 1997, £2,000, (CH/TMcG)• “Mectonics Instruments TCS”, 95-7, £65,000 (CH,CFE,WH)• “Innovation in Design”, 1996-9, £10,000 (JNF/CH)• JC Peacocks & Sons TCS, 91-5 £122,552 (GRJ/CH)

Total income: £490,000 Principle investigator: £368,000

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