the operations research software -...

TheUniversity

of Auckland

Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research

SoftwareSoftware

Dr Andrew J Mason Dr Andrew J Mason BE, PhD BE, PhD (Cambridge)(Cambridge)

Dept of Engineering ScienceDept of Engineering ScienceUniversity of AucklandUniversity of Auckland

[email protected]@aucklandauckland.ac..ac.nz nz www.esc.www.esc.aucklandauckland.ac..ac.nznz/Mason/Mason

Optimal Decision TechnologiesOptimal Decision Technologieswww.optimalwww.optimal--decision.comdecision.com

• Staff and Airline Scheduling• Multi-criteria Optimisation• Forestry Planning• Hydro-electricity Scheduling• Electricity Markets• Emergency Services Simulation• Storage Network Design• Telecommunications

Department of Engineering ScienceUniversity of Auckland,

New ZealandTheUniversity

of Auckland

• Many years experience with Air New Zealand • Scheduling software saving US$8 million per annum• Informs Franz-Edelman finalist, 2000

• Father of ‘Constraint Branching’• A champion of ‘Real OR’

• Technology transfer and impact in the real world

Professor David RyanChair in Operations Research

Department of Engineering ScienceUniversity of Auckland

Optimal Decision Technologies Ltd

• University spin-off company

•All Engineering Science graduates

• Implementation partner in Air New Zealand work

• Co-developers of Doris & Siren


SoftwareSoftware•DORIS

–Airport staff scheduling •PETRA

–Call centre staffing

•PIVOT–Paper Mill Supply Chain

•FIDO–Fibre optic network design

•SIREN–Ambulance simulation

TheUniversity

of Auckland

•DORIS–Airport staff scheduling

•PETRA–Call centre staffing





SoftwareSoftware

TheUniversity

of Auckland

New Zealand CustomsNew Zealand Customs

•• Process passengers arriving/departing at Process passengers arriving/departing at Auckland International Airport, NZAuckland International Airport, NZ–– Must meet maximum passenger waiting timesMust meet maximum passenger waiting times

•• Independent review recommends partIndependent review recommends part--timers onlytimers only•• Staff & Management respond by hiring University Staff & Management respond by hiring University

–– David Ryan, Andrew Mason, David David Ryan, Andrew Mason, David PantonPanton

•• Parties agree on new optimizationParties agree on new optimization--based approachbased approachOperations Research, Vol 46, Number 2, p161-175

New Zealand CustomsArrival Passenger Processing

Secondary Search Area

Baggage Primary LineCarousels Fro

m P

lane

s

Photos NZ Customs & AIAL

Arrivals

from aircraft

Carousels

Primary Line

Secondary

Project Scope

• Original Staffing Plan– Paper-based 6-on, 3-off shift schedule

– Staggered team starts during day• Some attempt to match staffing to flight times

– No variation in staffing over week or seasons

• New Approach– More efficient optimised cyclic schedule for full-timers– Introduce flexible part-time staff– Mix of optimised and manual scheduling

Flight Schedule Information

Determining Work Requirements from Flight Schedule

Officers on duty from 2200 (10pm) to 2215

Flight being processed

Time

Work Requirements Visualization

Queue Length

Cum

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b=

b1b2b3b4b5b6b7b8b9b10b11b12b13b14b15

:

bm-6bm-5bm-4bm-3bm-2bm-1bm

A=

1 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 11 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 … 1 1 1 …

1 1 … 1 1 1 … 1 1 1 … 1 1 1 …1 … 1 1 1 … 1 1 1 … 1 1 1 …

1 1 1 … 1 1 1 … 1 1 1 …1 1 1 … 1 1 1 … 1 1 1 …

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1 1 1 …1 1 1 …1 1 1 …1 1 1 …1 1 1 …

1 1 …1 …

...

...

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1 : : 1 : : 1 : : 1 : :1 1 1 1 1 1 1 1

1 1 1 13 hour shifts 4 hour shifts 5 hour shifts 8 hour shifts

min cTxs. t. Ax ≥ b, xi ∈{0,1,2,…}

Generating Shifts to cover Work Requirements

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Implementation Experiences• First System

– Mix of spreadsheet tools and pre-computed results– Implementation

• Year old passenger estimates• Record passenger levels at airport• Optimistic passenger processing times• Disgruntled staff + long queues = Disaster!

– Many lessons learned & changes made

• Current Doris System– Integrated software system

• optimisation engines, database, user interface

– Handles full and part-time staff, payroll, leave etc

Shifts and Work Requirements

Departures

Arrivals

Customs Preferences

Doris Scheduling Steps







SoftwareSoftware

TheUniversity

of Auckland

• Developed for Tabcorp Holdings Limited• Australia's largest gambling/entertainment group

• Schedules 700 call-centre staff each week• Customized Integer Program software

• Developed by PhD student David Nielsen

• now with Dash Associates (Express MP)

• Commercial partner Mantrack Ltd

PETRA

••DORISDORIS––Airport staff scheduling Airport staff scheduling

••PETRAPETRA––Call centre staffingCall centre staffing

••PIVOTPIVOT––Paper Mill Supply ChainPaper Mill Supply Chain

••FIDOFIDO––FibreFibre optic network designoptic network design

••SIRENSIREN––Ambulance simulationAmbulance simulation


SoftwareSoftware

TheUniversity

of Auckland

PIVOT -Paper Industry Value Optimisation Tool

• Decision support tool for pulp & paper supply chain–Model designed by Prof Andy Philpott, University of Auckland–Developed by Graeme Everett, Norske Skog, using AMPL–See Philpott & Everett, Annals of OR, 108, 225-237, 2001

• Provides decision support capability to test the validity and cost of current constraints:–Wood supplier/Freight contracts, Major customer agreements

• Quickly assess impact of changes to parameters:–Demand, Freight Rates, Manufacturing Costs, Exchange rates

TheUniversity

of Auckland

BOYER MILL

Auckland MILL

KAWERAU MILL

Geographical Network

Paper Mill Schematic

Wood Preparation

Pulp Production

Paper Production

Typical Mill Process converting fibre to pulp, then to paper

Pulp markets

Finishing

Suppliers

Pulp process

Finished product markets

Distribution Facility

Paper process

Finishing

Supplier Yield

Debarker/chipper Slasher

Supplier Yield

Original Spreadsheet InterfaceAlburySupplier Currency Pchips Plogs OMG ONP Pchips Plogs OMG ONP Pchips Plogs OMG ONPABGATE - TREEOWNERSAUD 0 0 0 0 0 0 0 0 0 0 0 0ANM AUD 0 0 0 0 0 0 0 0 0 0 0 0DUNN AUD 0 0 0 0 0 0 0 0 0 0 0 0JV AUD 0 0.00 0 0 0 0 0 0 0 0 0 0SFNSW AUD 0 0 0 0 0 0 0 0 0 0 0 0TROWN AUD 0 0 0 0 0 0 0 0 0 0 0 0VPC - HANCOCKAUD 0 0 0 0 0 0 0 0 0 0 0 0ADELAIDE AUD 0 0 0 0 0 0 0 0 0 0 0 0BRISBANE AUD 0 0 0 0 0 0 0 0 0 0 0 0CANBERRA AUD 0 0 0 0 0 0 0 0 0 0 0 0MELBOURNE AUD 0 0 0 0 0 0 0 0 0 0 0 0PERTH 0 0 0 0 0 0 0 0 0 0 0 0SYDNEY AUD 0 0 0 0 0 0 0 0 0 0 0 0

BoyerSupplier Currency Pchips Plogs Echips Elogs Pchips Plogs Echips Elogs Pchips Plogs Echips ElogsANM_FE AUD 0 0 0 0 0 0 0 0 0 0 0 0ANM_FP AUD 0 0 0 0 0 0 0 0 0 0 0 0ANM_MG AUD 0 0 0 0 0 0 0 0 0 0 0 0AUSPIN AUD 0 0 0 0 0 0 0 0 0 0 0 0FRENCH AUD 0 0 0 0 0 0 0 0 0 0 0 0FTAS_E AUD 0 0 0 0 0 0 0 0 0 0 0 0FTAS_P AUD 0 0 0 0 0 0 0 0 0 0 0 0NFPROD AUD 0 0.00 0 0 0 0 0 0 0 0 0 0SMILLS AUD 0 0.00 0 0 0 0 0 0 0 0 0 0

TasmanSupplier Currency Pchips Plogs Echips Elogs Pchips Plogs Echips Elogs Pchips Plogs Echips ElogsFCF USD 0.00 0.00 0 0.00 - - 0 0 0 - 0 0FCF3RD NZD 0 0.00 0 0.00 - - 0 0 0 0 0 0K_IND NZD 0 0.00 0 0.00 - 0 0 0 0 0 0 0

Cost ($/wet tonne) Supplier Maximum (wet tonnes) Supplier Minimum (wet tonnes)

Cost ($/tonne, pine=wet, rcf=dry) Supplier Max (tonnes, pine=wet, rcf=dry) Supplier Min (tonnes, pine=wet, rcf=dry))

Cost ($/wet tonne) Supplier Maximum (wet tonnes) Supplier Minimum (wet tonnes)

• Data Preparation - cutting and pasting error-prone

• Changes to Business data made in several places

• Model interface appearance was “amateur”.

• Only “super-users” able to run the model.

• The AMPL model itself has proven remarkably robust with few changes needed in 3 years of use

New PIVOT Database System• System has:

– 48 tables describing business data

– 20 tables to hold solution

– 26 main screens

– 32 reports

– approximately 150 queries required to support screens, model and reports

• Developed by ODT

Interface with AMPL OptimisationModelling Language

• AMPL table commands use ODBC drivers to read and write external files

• Access queries construct data in a format convenient for AMPL to read

Table MachineProductFibreRecipe IN "ODBC" (dbName) "SQL=SELECT sMachine, sProduct, sFibre, sgAdjustedFibrePercentage from qyAmplMachineProductFibreRecipe“ : [sMachine, sProduct, sFibre], Recipe1 ~ sgAdjustedFibrePercentage;

Read table MachineProductFibreRecipe;

User-defined Constraints# Constraint 12# -----------------# CPS must be supplied the bulk from either Auckland be # supplied from no more than two mills# Constraint is active#param Cpsonesitemin:= 1234;

var CpsAuckland{T} binary;

subject to CpsSupplyMelb {t in T}:sum {m in MILLMACH["Melb"]}DeliveredTonnes[m,"45_60",t,"Au_NSW_CPS"] >= Cpsonesitemin * (1-CpsAuckland[t]);

subject to TwoMillsSupplyCps {t in T}:CpsAuckland[t] + CpsMelb[t] +CpsWelg[t] <= 2;## Constraint 13# -----------------# Generic Constraint# # Constraint is not active#







SoftwareSoftware

TheUniversity

of Auckland

Development of FIDO - A

Network Design Tool for Fibre

Cable Layout

• Prof Andy Philpott & Dr Andrew Mason– See OR/MS Today, Vol 30#2, April 2003

• Used by TelstraClear in 2001/2 for fibre optic network design in New Zealand– US$700 million project

– Auckland (pop 1 million)

– Wellington (pop 200,000)

– Christchurch (pop 250,000)

TheUniversity

of Auckland

Customer

TelstraExchange

The Telstra Problem…

• A 2-fibre circuit connects customer to exchange

Customer

TelstraExchange

The Telstra Problem…

• A ‘diverse’ connection – 2 paths to exchange

Customer

TelstraExchange

Conventional Diverse Network Design

SONET Rings with add-drop multiplexers (‘muxes’)

Customer

Muxes

Customer

TelstraExchange

Telstra Network Design

Run fibres back to exchange: fibre rich thinning ring

Customer

FIDO (FibreDiversity Optimiser)

• Customised software for Telstra

• Want to know:– What diverse paths to run fibres along to serve each

customer

– How to aggregate fibres into commercial cable sizes (12, 24, 48, 96, 144)

– Welding plan for cables in each vault (manhole)

• Seek minimal cost solution– Welding costs, cable costs, equipment costs

TelstraExchange

1: Connect Streets to Exchange

Street connected by 2 diverse paths to exchange

A C D EB

Street

Fee

der

Pat

h 1 F

eeder Path 2

B

TelstraExchange

Use network flow to find diverse paths

For each street, seek 2 minimal length diverse paths

A

C

A C D EB

2: Connect Buildings on Street

Lot sizing locates multiplexors & assigns buildings

� � � ��

3: Cables and Welding Plan• Lay cables, not individual fibres, from streets

back to exchange

• Cables in sizes of 12, 24, 48, 96 or 144 fibres

• Can join cablesby welding

1224Vault

TelstraExchange

Cabling…

Use heuristic to trade off welding vs cable costs

A C D EBB

A

C

A

FID

O in

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ion

Phy

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to a

Str

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A P

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able

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Join

s







SoftwareSoftware

TheUniversity

of Auckland

SIREN: Simulation for Improving Response of Emergency Networks

• Ambulance Simulation– Shane Henderson (Cornell),

Andrew Mason– Auckland NZ (1999), &

Melbourne Australia (2001)– What-if analysis

NZ Herald, October 1998

Operations Research and Health Care: Operations Research and Health Care: A Handbook of Methods A Handbook of Methods & & Applications, Applications, KluwerKluwer, Vol 70, pp77, Vol 70, pp77--102, 2004102, 2004

Siren SystemSiren System

•• Custom built C++ codeCustom built C++ code–– Microsoft Windows basedMicrosoft Windows based

•• Simulation of ambulance operationsSimulation of ambulance operations–– PlayPlay--back of historic emergency callsback of historic emergency calls

–– Detailed road networkDetailed road network•• Time of day congestion effectsTime of day congestion effects

•• Lights & SirensLights & Sirensvsvs Standard travel speedsStandard travel speeds

–– UserUser--configurable control logicconfigurable control logic•• Python based dispatch rulesPython based dispatch rules

•• Graphical Analysis ToolsGraphical Analysis Tools

SIREN: Simulation for Improving Response of Emergency Networks

• Ambulance Simulation– Shane Henderson, A. Mason– C++ system– Auckland, New Zealand and

Melbourne, Australia– What-if analysis

NZ Herald, October 1998

“Why is it that major corporations, airline industry … possess and use technology and resources to optimize and streamline processes … while EMS remain in the technological dark ages?”

Paramedic from Portland Oregon, Letter to “USA Today”

Auckland, NZ

The MelbourneOperation

• Many Vehicle Types (vs one in Auckland)

MC 2 MICA officers PR Paramedic Response Unit

1 MICA officer, 1 Paramedic officer

AP 2 Ambulance Paramedic officers

MR MICA Responder 1 Paramedic officer, no transport

OC On call Single officer, at home; operate as OCA+OCB pairs

The Melbourne Operation

• Detailed Case Classification System– Approx 270 ProQA case types

(vs 3 in Auckland)

• Complex Dispatch Rules– Depend on ProQA case type

– Multiple vehicle dispatch

• Up to 4 vehicles sent to a scene

Pro

QA

H

Dispatch Rules Example

ProQA Type: “4DQ1=ANIMAL BITE:

PERIPHERAL - MINOR BLEEDING”

Find the closest vehicle of type (MR or MC)

If this is an MR thenDispatch the MR at Priority 1 (Lights & Sirens)

Dispatch the closest AP vehicle at Priority 2

Else (it must be an MC)Dispatch the MC at Priority 1

Endif

HNB: Illustrative example only

Dispatch Logic Testing

1/4


2/4


3/4


4/4

The Melbourne Operation• Complex at Scene Rules

– Behavior depends on patient criticality • Estimated by analyzing historic vehicle movements

• In simulation, discovered on arrival at scene

– Multiple vehicles at scene• Must determine who does patient transport etc

• 2 single-officer vehicles → one 2-officer vehicle – Needed for patient transport

• 3 officers in vehicle for transporting serious cases

• Vehicles are left at scene– Must be collected after transporting patient

H

Sire

n S

imu

latio

ns

• At Scene Logic– Vehicle left at scene, & collected later

Siren Simulations

H

Siren Developments• New implementations planned for

– Perth, Australia– Toronto, Canada– West Yorkshire, United Kingdom

• New research challenges– Using GPS vehicle data– Providing real-time decision support– On-going research

• University of Alberta: S Budge, A Ingolfsson, E Erkut• Montreal group: G Laporte, M Gendreau, et al• University of Arizona: J Goldberg• MIT - Prof. Richard Larson

Final Thoughts• Integrated software systems becoming

increasingly important for delivery of OR solutions

• If possible, build in visualization from day one• Don’t under estimate data cleaning work• Be flexible and pragmatic

– Use a mix of approaches, eg heuristics

• Start small and develop incrementally– Use spreadsheets to test/demo approaches

• Work closely with client

TheUniversity

of Auckland

the operations research software -...

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