The Operations Research Software -...
Transcript of The Operations Research Software -...
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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
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• 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
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• 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
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Optimal Decision Technologies Ltd
• University spin-off company
•All Engineering Science graduates
• Implementation partner in Air New Zealand work
• Co-developers of Doris & Siren
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Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
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
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•DORIS–Airport staff scheduling
•PETRA–Call centre staffing
•PIVOT–Paper Mill Supply Chain
•FIDO–Fibre optic network design
•SIREN–Ambulance simulation
Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
SoftwareSoftware
TheUniversity
of Auckland
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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
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New Zealand CustomsArrival Passenger Processing
Secondary Search Area
Baggage Primary LineCarousels Fro
m P
lane
s
Photos NZ Customs & AIAL
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Arrivals
from aircraft
Carousels
Primary Line
Secondary
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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
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Flight Schedule Information
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Determining Work Requirements from Flight Schedule
Officers on duty from 2200 (10pm) to 2215
Flight being processed
Time
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Work Requirements Visualization
Queue Length
Cum
ulat
ive
Pas
seng
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ount
Time
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Off
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Smoothed Workload
Arrivals
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Work Requirements
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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 …
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 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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Full time
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Smoothed Workload
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Generated Shifts
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Part time
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Smoothed Workload
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Generated Shifts
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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
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Shifts and Work Requirements
Departures
Arrivals
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Customs Preferences
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Doris Scheduling Steps
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•DORIS–Airport staff scheduling
•PETRA–Call centre staffing
•PIVOT–Paper Mill Supply Chain
•FIDO–Fibre optic network design
•SIREN–Ambulance simulation
Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
SoftwareSoftware
TheUniversity
of Auckland
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• 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
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••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
Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
SoftwareSoftware
TheUniversity
of Auckland
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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
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BOYER MILL
Auckland MILL
KAWERAU MILL
Geographical Network
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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
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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
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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
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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;
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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#
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•DORIS–Airport staff scheduling
•PETRA–Call centre staffing
•PIVOT–Paper Mill Supply Chain
•FIDO–Fibre optic network design
•SIREN–Ambulance simulation
Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
SoftwareSoftware
TheUniversity
of Auckland
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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
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Customer
TelstraExchange
The Telstra Problem…
• A 2-fibre circuit connects customer to exchange
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Customer
TelstraExchange
The Telstra Problem…
• A ‘diverse’ connection – 2 paths to exchange
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Customer
TelstraExchange
Conventional Diverse Network Design
SONET Rings with add-drop multiplexers (‘muxes’)
Customer
Muxes
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Customer
TelstraExchange
Telstra Network Design
Run fibres back to exchange: fibre rich thinning ring
Customer
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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
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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
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B
TelstraExchange
Use network flow to find diverse paths
For each street, seek 2 minimal length diverse paths
A
C
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A C D EB
2: Connect Buildings on Street
Lot sizing locates multiplexors & assigns buildings
� � � �� �
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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
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TelstraExchange
Cabling…
Use heuristic to trade off welding vs cable costs
A C D EBB
A
C
A
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FID
O in
Act
ion
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Phy
sica
l Cab
les
to a
Str
eet
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A P
hysi
cal C
able
sho
win
g Jo
ins
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All
the
Cab
les
and
Join
s
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•DORIS–Airport staff scheduling
•PETRA–Call centre staffing
•PIVOT–Paper Mill Supply Chain
•FIDO–Fibre optic network design
•SIREN–Ambulance simulation
Technology Transfer: Technology Transfer: Successfully Delivering Successfully Delivering Operations Research Operations Research
SoftwareSoftware
TheUniversity
of Auckland
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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
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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
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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
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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
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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
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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
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Dispatch Logic Testing
1/4
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Dispatch Logic Testing
2/4
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Dispatch Logic Testing
3/4
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Dispatch Logic Testing
4/4
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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
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Sire
n S
imu
latio
ns
• At Scene Logic– Vehicle left at scene, & collected later
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Siren Simulations
H
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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
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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