Last-Mile Logistics - Welcome to the University of WarwickS.−S T3.) ∑V ∈0expS V−S T3 V...

Last-MileLogistics

DrArneStrauss

AssociateProfessorofOperationalResearchUniversityofWarwick

InternationalConferenceonOperationsResearch,6-8Sept2017,Berlin,Germany

45%

23%

19%

Morespecificdeliverytimeslots(eg30mins)

Sundaydeliveries

Same-daydelivery

TOP3CONSUMERWANTS

MintelE-CommerceUK2014

Last-MileCostChallenge

2

MethodologicalInnovations

4

Orderbooking Orderprocessing Orderdelivery

DemandManagement

Same-DayRouting

DemandManagement– ProblemOutline

5

Time

DeliveryCost

DeliveryDay

OptimalControl&VehicleRoutingProblem

𝑉" 𝒙" = max𝒅∈𝑫

+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<

§ Stage𝑡:smalltimeperiod§ State𝒙":acceptedordersuntiltimeperiod𝑡§ Decision𝒅:deliverycharges,discountsand/orotherincentives,orslotavailability§ 𝐽:Setofdeliverytimeslots§ 𝜆: customerarrivalrate§ 𝑃.(𝒅):Probabilityofcustomerselectingdeliverytimeslot𝑗§ 𝑟:orderprofitbeforedeliverycost

6



+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<


7

Opportunitycost



+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<


8

Deliverycost:Vehicleroutingproblemwithtimewindows



+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<


9

Curseofdimensionality



+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<


10

Non-linear(choice-based)priceoptimization

TypicalSolutionApproach


+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<

11

𝑉C(𝐱) ≈ 𝑉F"(𝒙)

OnlineOffline Opportunitycostestimates

Controlpolicy

TypicalSolutionApproach


+𝜆𝑃. 𝒅�

.∈0

𝑟 + 𝑑. − 𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. + 𝑉"56 𝒙" ∀𝒙", 𝑡

𝑉<56 𝒙< = −𝐶 𝒙< ∀𝒙<

12

𝑉C(𝐱) ≈ 𝑉F"(𝒙)

OnlineOffline Opportunitycostestimates

Controlpolicy

OnlineProblem:Choice-BasedPricingPolicy

Notation:ο."(𝒙"):opportunitycost ofacceptingorderintimeslotj givenpreviouslyaccepted

orders𝒙" uptotimetD: feasiblecontrols

13

Difficultydependsonchoicemodelandfeasibleregion D

max+𝑃. 𝒅 𝑑. + 𝑟 − 𝑜." 𝒙"

�

.∈0s.t.𝑑 ∈ 𝐷

Example1:PricingPolicyunderMNLonContinuousSupport

14

Problem:

where

Result:

o 1-1mappingbetweenpricespaceandsalesprobabilityspace

o Objectiveintermsofprobabilitiesisconcave

o ObtainoptimalpricesbystandardNewtonrootsearch

Source:DongL,Kouvelis PandTianZ.DynamicPricing&InventoryControlofSubstituteProducts.Manufacturing&ServiceOperationsManagement(2009)

max+𝑃. 𝒅 𝑑.

�

.∈0s.t. 𝒅 ∈ 𝑹 ∪∞ |0|

𝑃. 𝑑 = exp(𝛽. − 𝛽T𝑑.)

∑ exp 𝛽V − 𝛽T𝑑V + exp(𝛽W)�V∈0

Example2:PricingunderMNLonFinitePriceSet

15

Problem:

where𝐴 = 𝑎Z. Z,.unimodular,

'

( )

' :

max

. 1

j jS

ij j S

S

ij J

j

P S

s

d

S S J a b it

Î

ÎÎ

ì üÎ Ì £ "í ýî þ

å

å

0

)( .jj

kk S

P Sv vv

Î

=+å

Result:EquivalentLP

Source:DavisJ,GallegoGandTopaloglu H.AssortmentPlanningundertheMultinomialLogitModelwithTotallyUnimodularConstraintStructures.CornellUniversity,WorkingPaper(April2013)

0

0

0

0

m

. .

0

0

ax

1

j jJ

jJ

P j

j

j

jij i

J j

j

j

Pd

s t P P

Pi

P

Pav

j

bv

Pv

Î

Î

Î

£ "

"

+

£

-

£

=

å

å

å

Example2:PricingunderMNLonFinitePriceSet

16

Problem:

where𝐴 = 𝑎Z. Z,.unimodular,

'

( )

' :

max

. 1

j jS

ij j S

S

ij J

j

P S

s

d

S S J a b it

Î

ÎÎ

ì üÎ Ì £ "í ýî þ

å

å

0

)( .jj

kk S

P Sv vv

Î

=+å

Result:EquivalentLP

Source:DavisJ,GallegoGandTopaloglu H.AssortmentPlanningundertheMultinomialLogitModelwithTotallyUnimodularConstraintStructures.CornellUniversity,WorkingPaper(April2013)

0

0

0

0

m

. .

0

0

ax

1

j jJ

jJ

P j

j

j

jij i

J j

j

j

Pd

s t P P

Pi

P

Pav

j

bv

Pv

Î

Î

Î

£ "

"

+

£

-

£

=

å

å

å

Furtherinfo:Strauss,KleinandSteinhardt.AReviewofChoice-BasedRevenueManagement:TheoryandModelsWorkingpaper,UniversityofWarwick,August2017

OfflineProblem:OpportunityCostEstimation

Example:ApproximateDynamicProgrammingApproach§ Decomposedeliveryareausingcontinuousclustering-first,routing-secondapproach

§ Optimizeparameters𝛾 and𝜃 ofvaluefunctionapproximationforeachareaindependently:

𝑉C 𝐱 ≈ 𝑉F" 𝒙 = 𝛾W −+𝛾.𝑥. + 𝑇 + 1 − 𝑡 𝜃�

.

Then,opportunitycostestimatesaregivenby𝑉"56 𝒙" − 𝑉"56 𝒙" + 𝟏. ≈ 𝛾.

17

Source: Yang,X.,Strauss,A.K.(2017).Anapproximatedynamicprogrammingapproachtoattendedhomedeliverymanagement.EuropeanJournalofOperationalResearch 263(2017)935–945

CurrentState-of-the-Art:OrderinAdvance

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FocusondemandmanagementLow High

Low

High

Focusonvehiclerouting

Asdemir etal.(2009)

Campbell&Savelsbergh (2005)

Campbell&Savelsbergh(2006)

Yangetal.(2016)

Yang&Strauss(2017)

Agatz etal(2011)

Ehmke&Campbell(2014)

Cleophas&Ehmke(2014) Kleinetal.(2016a)Kleinetal.(2016b)

SlotavailabilitycontrolSlotpricecontrolAccept/denyrequests

CurrentState-of-the-Art:Same-Day

19

FocusondemandmanagementLow High

Low

High

Focusonvehiclerouting

Opportunity?

Arslanetal.(2017),Voccia etal.(2017),Ulmeretal.(2016),Klapp etal.(2016a,b),Reyesetal.(2016),Cattaruzza etal.(2016)Archetti etal.(2015)Azi etal.(2012)

Ulmer(2017)

TechnologicalInnovations

21

Source: USPatent9,718564B1.Aug1,2017.

Trucks&Drones

Basictrade-off:§ Speed:drone>>truck§ Capacity&range:drone<<truck

Problemdesigns:§ Single/multipledronespertruck§ Dronesdepartingfromtruckand/ordepots§ Variousrestrictionsonlaunch,landingand

recoverlocations§ Same-daydelivery§ DiscreteTSP-based/continuousapproximation

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Figuresource:Murray&Chu,(2015).TheFlyingSidekickTravelingSalesmanProblem:OptimizationofDrone-assistedParcelDelivery.TransportationResearchPartC

BusinessModelInnovations&ResearchOpportunities

24

Flexible(crowdsourced)drivers SharingEconomy(ShippingAlliance)

SelectedLiterature– DemandManagementfortheLastMile

§ Agatz,N.A.H.,Campbell,A.M.,Fleischmann,M.,&Savelsbergh,M.W.P.(2011).Timeslotmanagementinattendedhomedelivery.TransportationScience,45(3),435–449.

§ Asdemir,K.,Jacob,V.S.,&Krishnan,R.(2009).Dynamicpricingofmultiplehomedeliveryoptions.EuropeanJournalofOperationalResearch,196(1),246–257.

§ Campbell,A.M.,&Savelsbergh,M.W.P.(2005).Decisionsupportforconsumerdirectgroceryinitiatives.TransportationScience,39(3),313–327.

§ Campbell,A.M.,&Savelsbergh,M.W.P.(2006).Incentiveschemesforattendedhomedeliveryservices.TransportationScience,40(3),327–341.

§ Cleophas,C.,&Ehmke,J.F.(2014).Whenaredeliveriesprofitable?Consideringor- dervalueandtransportcapacityindemandfulfillment forlast-miledeliveriesinmetropolitanareas.BusinessandInformationSystemsEngineering,6(3),153–163.

§ Ehmke,J.F.,&Campbell,A.M.(2014).Customeracceptancemechanismsforhomedeliveriesinmetropolitanareas.EuropeanJournalofOperationalResearch,233(1),193–207.

§ Klein,R.,Mackert,J.,Neugebauer,M.,&Steinhardt,C.(2016a).Ontheapproximationofopportunitycostfordynamicpricinginattendedhomedelivery.UniversityofAugsburg.Workingpaper.

§ Klein,R.,Neugebauer,M.,Ratkovitch,D.,&Steinhardt,C.(2016b).Differentiatedtimeslotpricingunderroutingconsiderationsinattendedhomedelivery.ForthcominginTransportationScience.

§ Yang,X.,Strauss,A.K.(2017).Anapproximatedynamicprogrammingapproachtoattendedhomedeliverymanagement.EuropeanJournalofOperationalResearch 263(2017)935–945

§ Yang,X.,Strauss,A.K.,Currie,C.S.M.,&Eglese,R.(2016).Choice-baseddemandmanagementandvehicleroutingine-fulfillment.TransportationScience,50(2),473–488.

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SelectedLiterature– Same-DayDeliveries

§ Archetti,C,D.Feillet,&M.G.Speranza.(2015) Complexityofroutingproblemswithreleasedates.EuropeanJournalofOperationalResearch,247(3):797–803,.

§ Arslan,A,Agatz,N&Zuidwijk,R.(2017)SameDayDelivery:VehicleRoutingProblemwithSelf-SchedulingDrivers.LastMileDeliveryWorkshop,Mannheim,Germany,June22-232017

§ Azi N,Gendreau M,PotvinJY. (2012) Adynamicvehicleroutingproblemwithmultipledeliveryroutes.Ann.Oper.Res.199(1):103–112.

§ Cattaruzza,D,Absi,N&Feillet,D.(2016)TheMulti-TripVehicleRoutingProblemwithTimeWindowsandReleaseDates.TransportationScience50(2),676- 693

§ Erera,A,Reyes,D.&Savelsbergh,M(2016).ComplexityofRoutingProblemswithReleaseDatesandDeadlines.Workingpaper,GeorgiaTech

§ Klapp M,Erera A,Toriello A(2016a)Theone-dimensionaldynamicdispatchwavesproblem.ForthcominginTransportationSci.§ Klapp,M.Erera,&Toriello A.(2016b).TheDynamicDispatchWavesProblemforSame-DayDelivery.Workingpaper,Georgia

Tech§ Ulmer,MW(2017).DynamicPricingforSame-DayDeliveryRouting,TUBraunschweig,Germany§ UlmerMW,ThomasBW,Mattfeld DC(2016)Preemptive depotreturnsforadynamicsame-daydeliveryproblem.Working

paper,TUBraunschweig,Germany§ Voccia,S.A.,Campbell,A.M.&Thomas,B.(2017).TheSame-DayDeliveryProblemforOnlinePurchases.Forthcomingin

TransportationScience

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SelectedLiterature– Truck&Drone

§ Agatz,N.,P.Bouman,andM.Schmidt(2016).Optimizationapproachesforthetravelingsalesmanproblemwithdrone.Workingpaper,ErasmusUniversityRotterdam.

§ Ulmer,MWandThomas,B.(2017)Same-DayDeliverywithaHeterogeneousFleetofDronesandVehicles.Workingpaper,TUBraunschweig

§ Campbell,JF,SweeneyD.andZhang,J.(2017)StrategicDesignforDeliverywithTrucksandDrones.Workingpaper,UniversityofMissouri

§ Ha,Q.M.,Deville,Y.,Pham,Q.D.andHà,M.H.(2015)HeuristicmethodsfortheTravelingSalesmanProblemwithDrone.TechnicalReport,ICTEAM/INGI/EPL

§ Ha,Q.M.,Deville,Y.,Pham,Q.D.andHà,M.H.(2016)OntheMin-costTravelingSalesmanProblemwithDrone.”TechnicalReport,ICTEAM/INGI/EPL

§ Ferrandez,S.M.,Harbison,T.,Weber,T.,Sturges,R.andRich,R.(2016)Optimizationofatruck-droneintandemdeliverynetworkusingk-Meansandgeneticalgorithm.JournalofIndustrialEngineeringandManagement9(2),374-388

§ Murray,C.andChu,A.(2015)TheFlyingSidekickTravelingSalesmanProblem:OptimizationofDrone-assistedParcelDelivery.TransportationResearchPartC54:86-109

§ Ponza,A.(2016)OptimizationofDrone-AssistedParcelDelivery.UniversitaDegliStudiDiPodova,Italy.§ Wang,X.,S.Poikonen,andB.Golden (2016) Thevehicleroutingproblemwithdrones:Severalworst-caseresults.Optimization

Letters

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SelectedLiterature– Crowdshipping/ShippingAlliances

§ Arslan,A,Agatz,N.,Kroon,LandZuidwijk,R(2016).CrowdsourcedDelivery:ADynamicPickupandDeliveryProblemwithAd-hocdrivers.Workingpaper,ErasmusUniversityRotterdam.

§ Arslan,A,Agatz,N&Zuidwijk,R.(2017)SameDayDelivery:VehicleRoutingProblemwithSelf-SchedulingDrivers.LastMileDeliveryWorkshop,Mannheim,June22-232017

§ Devari etal.(2017)Crowdsourcingthelastmiledeliveryofonlineordersbyexploitingthesocialnetworksofretailstorecustomers,TransportationResearchPartE105(2017)105-122

§ Kafle,N.Zou,B.&Lin,J.(2017)DesignandModeling ofACrowdsource-EnabledSystemforUrbanParcelRelayandDelivery.TransportationResearchPartB99(2017)62-82

§ McKinnon,A.(2016)Crowdshipping:Acommunalapproachtoreducingurbantrafficlevels?Workingpaper,KühneLogisticsUniversity

§ Setzke etal.(2017).MatchingDriversandTransportationRequestsinCrowdsourcedDeliverySystems.Twenty-thirdAmericasConferenceonInformationSystems,Boston

§ Wang,Y.etal.(2016)Towardsenhancingthelast-miledelivery:Aneffectivecrowd-taskingmodelwithscalablesolutions.TransportationResearchPartE93(2016)279–293

§ Allen etal.(2016)Enablingthefreighttrafficcontrollerforcollaborativemulti-dropurbanlogistics:practicalandtheoreticalchallenges.WorkingpaperURL:http://www.ftc2050.com/

§ Pradenas,L.etal.(2013)Mitigationofgreenhousegasemissionsinvehicleroutingproblemswithbackhauling.ExpertSystemswithApplications40(2013)2985-2991

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THANKYOU

Email:[email protected]: go.warwick.ac.uk/astrauss/

Last-Mile Logistics - Welcome to the University of WarwickS.−S T3.) ∑V ∈0expS V−S T3 V...

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