Workshop TA2: Open Logistics Interconnection Model 

& Protocols

In telecommunications, interconnection is the physical linking of a carrier's network withequipment or facilities not belonging to thatnetwork. The term may refer to a connectionbetween a carrier's facilities and the equipment belonging to its customer, or to a connection between two (or more) carriers.

Open Logistics InterconnectionModel & Protocols

Sources: wikipedia

Open Logistics Interconnection Model & Protocols – OLI model

Weight,(Volume,(Temperature,(Hazard5Level(

Single(vs.(mul<ple(des<na<ons,(Single(vs.(mul<ple(sourcing(loca<ons(

Delivery(<me(specifica<ons(

(Number(and(types(of(π5containers((Assignment(of(products(to(π5containers((

Number(of(shipments(Assignment(of(π5containers(to(shipments(Logis<cs(service(class(for(each(shipment(

Target(route(for(each(shipment,(defined(as(sequences(of(segments/nodes,(with(<ming(specifica<ons(

s:(Assignment(of(π5containers(to(π5means(Assignment(of(π5means(to(π5links(

(Genera<on(of(loading,(moving,(sor<ng,(storing,(retrieving(and(unloading(orders(

Montreuil, B., E. Ballot, and F. Fontane. An Open LogisticsInterconnection Model for the Physical Internet. in INCOM 12 Conference. 2012. Bucharest, Romania: IFAC.

Ballot, E., B. Montreuil, and M. Thémans, OPENFRET: contribution à la conceptualisation et à la réalisation d'un hub rail‐route de l'Internet Physique, PREDIT, Editor. 2010, MEDDAT: Paris. p. 114.

Open Logistics InterconnectionModel & Protocols

Agenda: • Presentations

• Discussion

Evaluating five typologies on costs and requirements for hyperconnectedlogistics networksWout Hofman

Networking in the Real World: Unified Modeling of Information and Commodity Distribution NetworksAmitangshu Pal and Krishna Kant

Open Logistics InterconnectionModel & Protocols

Discussion topics• What is the purpose of a protocol suite?• What is interconnection of logistics services?• How to use these frameworks? • …

NETWORKING IN THE REAL WORLD: UNIFIED MODELING OF INFORMATION AND PERISHABLE COMMODITY DISTRIBUTION NETWORKS

Amitangshu PalandKrishnaKantComputerandInformationSciencesTempleUniversity

The Perishability Challenge PerishableInformationDistribution

Increasingamountsoftimesensitiveinformation,includingstreaming&otherreal‐timedata.

Notwellservedbytraditional“besteffort”Internet. Newarchitecturestohandlethese(e.g.,contentcentricnetworks)

PerishableCommodityDistribution Perishability Efficiencytradeoff VaryingperishabilityofproductsMixingproductsverydifficult

Localfoodmovementleadstouniquelocallogisticschallenges. Integrationbetweenlocal&nonlocallogisticsamust

Goal: StudysynergiesbetweenPerishablecommodityvs.Informationdistributiontoadvancebothfields

Information vs. Commodity DistributionManysimilarities Movementofpackagesorpacketsbetween“nodes”withvariouscapabilities

• Change of mode (or “media”), e.g., truck to railcar or barge• Store and forward w/ potential product remix.

Accentuatedbyemergingstandardization(e.g.,GS1)&capacitysharing(e.g.,3PL)ideas

MultipleflowswithvariedSLArequirements Privacyandsecurityrequirements(e.g.,privacypreservingdistribution)

Dynamicallychangingavailabilityanddemand

Information vs. Commodity Distribution

Butmanykeydifferencesaswell Recursivebundling(boxeswithinboxes) Physicalgoodscannotbe“cloned”(exceptatsource) Packet“carrier”(e.g.,truck)andauxiliaryresources(e.g.,driver,loadingequipment)

Complexrequirements(e.g.,longtermcontracts).

Canwecreateaunifiedmodelthatcomprehends InformationNetworks(IN) PerishabilityCommodityDistributionNetworks(PCDN)

A Unified View 5layermodelsimilartoTCP/IPprotocolstack Twotypesofpackets

Non‐clonable Transportationofperishablegoods ClonableComputerNetworks

PacketscanbeofdifferentQoS classes Basedonperishabilityorotherhandlingcharacteristics Eachclasshasaperishabilityfunctionwithin0and1

Physical Layer

Media Switching Layer

Routing & Distribution

Transport/Delivery

Virtualization Layer

Physical layer  Layer 1 Dealswithactualmovementofpacketsalongamediasegment/channel IN:Transports“frames”overawired/wirelesschannel

• Max frame size, smaller sizes less efficient PCDN:Physicaltransportoveramediasegment(road,rail,waterway,orair)

• Transport in units (e.g., PI containers) that fit the carrier (e.g., truck)• Carrying smaller PI containers less efficient

PCDNChannelisfarmorecomplex MultipleroadwaysegmentsfrompointAtoBwithdifferentcapacities.

Alternateroutesevenatlayer1– HWYvs.normalroad PCDNtrafficofinterestjustasmallpartofalltrafficthat’sflowing

• Much higher uncertainties & little controllability

Whyisabstractionuseful? Statisticaldescriptionofthepath(e.g.,netcapacity) Canbecomparedtowirelesslinkw/fading&interference

Media Switching layer  Layer 2 Mediaaccesscontrol

Assignsthe“channel”(e.g.,pathtonexttransferpoint)• Transfer point: A switch in IN, and a physical transfer point for PCDN

Maydoreframing• IN: Break up into smaller frames, make use of jumbo frames• PCDN: Truck full of PI containers  Rail car full of PI containers• Damaged (rotten) frames  Discard & request replacement

ComplexitiesinPCDN PCDNneedstoassigncarriers (e.g.,trucks,railcars)tothepackets PCDNneedsadditionalresources(e.g.,drivers) PCDNinvariablyneedstoreturndamagedgoods

• Reverse logistics added complication Layer2routing

IN:Transportbetweensuccessiverouters(layer3endpoints)• Intermediate transfer point (switches & protocol bridges) with change in 

media (wired, wireless, optical, …) PCDN:TransferofPIcontainersfromadistributioncentertonext

• Intermediate transfer points may involve media changes (e.g., truck to railcar)

Routing & Distribution layer : Layer 3 End‐to‐endtransferofpackets/packages

IN:Transferfromsrc todest viamultiplerouters• May fragment a TCP stream into multiple IP datagrams• Different types of packets may be mixed before framing

PCDN:Transferfromsrc todest viamultipledistributioncenters• May fragment stream of goods into PI containers• Bundling of different types of contents together quite common

ComplexitiesinPCDN Mayinvolverecursivebundling(boxeswithinboxes) Involvesallocation&trackingL3resourcelikecontainers Bufferspacemanagementlotmorecomplex(contracts?)

RoutingneedsuniqueIDs ForIN,thismaybeamessageordatagramsequencenumber ForPCDNitmaybetheGTIN,GSIN,SSCCnumber

Whyistheabstractionuseful? NumerousroutingalgorithmsexploredinIN Emergingnotionsofcontentcentricnetworking

Transport/Delivery Layer : Layer 4 Endtoend(src todest)deliveryofpackets

IN:Userendpointtouserendpoint PCDN:Farmtoretailer/business(donotconsiderretailpurchases)

Similarities Flowcontrol,packetization,resourceallocation,retransmissionofdamagedgoods.

PCDNcomplexities Batching(accumulatingenoughgoodsfortransport)anessentialcomponent

• Needs to deal with tradeoffs between perishability, cost, efficiency Contractbaseddeliveryscheduling(lessflexibleflowcontrol) Qualitydegradationwithtime&productmixingmuchmorechallenging

• Lateral distribution to handle perishability Reverselogisticsforreturnsandreplacements

Whyistheabstractionuseful? Contentcentric&just‐in‐timemediadelivery

Virtualization Layer : Layer 5 VirtualizationgoalsinIN&PCDN

Sharenetworkcapacityefficientlyamongdifferentapplications Providestablecapacityallocationandisolation

ExamplesforPCDN Definea“HPTransport”asaVSfortransportinghighlyperishable(HP)betweenasrc &dest

• Similar VSs for moderate and low perishable items SeparateVSsfordifferenttypesofcustomers VSforpremiumcustomersorotherlow‐endcustomers

KeyChallenges Themappingofvirtualresourcesonphysicalresources Lackofvisibilityintotheentirenetworkandthedifficultyoftrackingtheentirenetworkstate

Whyistheabstractionuseful? Increasingcomplexityofbothcloudcomputingandlogistics

Resource Management in UNMTheunifiednetworkinvolvesacquisitionofcertainresourcesateachlayerofthenetwork Needtoobtaincarriers&containerstocarryproducts

Returnofcarriers&containers(fullorempty) Availability&properdistributionofcarriersandcontainersimpactstimeliness&freshnessofperishableproductdelivered.

Resourcemanagementcrucialformodelingperformance. Detailsofrepresentationandusageinthepaper

Asimplemodelingillustration

Analytical Model for Commodity Distribution in UNM

Assumedabulkqueuingtheoreticmodel: Trucksareservingthedistributioncenters(DCs)thatareplaceduniformly truckservicetimeisdeterministic

PacketsarearrivingatthedistributioncenterasaPoissonprocess

Distributioncentershavefinitebuffers( packages) PacketsarewastedwhentheDCbufferisfull DCqueueisserveduponarrivalofatruck Thetruckloadsalmost packagesataDC

• If <=  packages  entire queue is loaded onto the truck  truck leaves without waiting for other packages

Thetruckcapacityisassumedtobe . eachDCreservesaspaceof unitsinthetruck

Thisqueuingdisciplinefallsunderthecategoryof / /1/queue

Analytical Model for Commodity Distribution in UNM

100nodesareplacedinanareaof100x100sq.m. Packagefreshnessdegradeslinearlywithtimeatarateof0.25%(atdeliverycenters)and0.35%(ontruck)

Packagedelayincrease deliveryqualitydecrease Withlesserbatchsize highertruckaccesstime WithhigherpackagearrivalrateatDC higherwaitingtime

Package delay against packet arrival rate Package delivery quality against packet arrival rate

Analytical Model for Commodity Distribution in UNM

Tradeoffbetweentransportationefficiencyanddeliveryquality: Increaseinnumberoftrucks improvesthe deliveryqualityaswaiting

timeofthepackagesreduces Howeverthetransportationefficiencyreducesduetolesseravailable

packagesateachDC

IncreasingBloadsmorenumberofpackagesatanyparticularDCimprovesthedeliveryqualityespeciallyincaseofsmallernumberoftrucks

Conclusions Weconsideredthesynergiesbetweeninformationandcommoditydistributiondisciplines Devisedaunifiedmodeltocaptureboth Discussedananalyticalframeworktogetaninsightregardingthekeyperformanceparameters

Thereisalwaysatradeoffbetweenthetransportationefficiencyanddeliveryquality

Ongoingworks: Wedesignedadatacenteropticalnetworkinspiredbyintegrationoflocal&nonlocallogistics

Usetheperishablelogisticsconceptsincontentcentricnetworks

Makingtheperishablelogisticsmoreresilienttospoilageandcontaminationbysensingandlocaldelivery