optimization models for long-haul freight transportation teodor gabriel crainic dept. management and...
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![Page 1: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/1.jpg)
Optimization Modelsfor
Long-haul Freight Transportation
Teodor Gabriel Crainic
Dept. Management and TechnologyUniversité du Québec à Montréal
andCentre for Research on Transportation
Université de Montréal/H.E.C./Poly
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Transportation Systems
Physical (Conceptual) Infrastructure and Services
Production, Consumptionof Goods and Services
SUPPLY DEMAND
Movements of people, goods, vehicles = TrafficCosts/profits, delays, energy, emissions, …
Economic and legal environment
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Transportation Systems
Physical (Conceptual) Infrastructure and Services
Production, Consumptionof Products and Services
SUPPLY DEMAND
Movements of people, goods, vehicles = TrafficCosts/profits, delays, energy, emissions, …
Economic and legal environment
![Page 4: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/4.jpg)
Transportation Systems
Physical (Conceptual) Infrastructure and Services
Production, Consumptionof Products and Services
Modes and Services Stations and Terminals Vehicles and Convoys Routes and Frequencies Costs and Tariffs
Economic Criteria Service Quality Criteria Mode Choice
Multimodal Multicommodity FlowsPerformance measures
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Transportation Systems Passengers vs. Freight User/Shipper vs. Carrier Urban vs. Interurban/“Regional” Uni- vs. Multi/Inter-modal Integration ? Intelligent Transportation Systems - ITS
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Passenger Transportation Customized (door-to-door) services:
private cars, walking, other modes vs. Consolidation transportation: transit
Urban Multimodal Short planning horizons (hours)
dependent upon time-of-day, day-of-week, week-of-year, …
“authorities plan, users decide”
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Freight Transportation Producers who own or operate the
transportation fleet (and infrastructure) vs.
“For hire” carriers Long-haul (intercity) transportation vs.
“Local” vehicle routing and distribution Multimodal transportation system of a
region vs. Carrier network and services Consolidation transportation vs.
Customized (door-to-door) services
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1
2
3
4
5
6
7
89
A
B
C
a
b
c
d e
f
Main route
Feeder routePick up and delivery route
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Freight Transportation Many more actors/deciders/issues Variable planning horizons Products Terminals
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Planning Levels
StrategicLong-termDesigns the system structure
TacticMedium-termDesigns the service structure
OperationalTime-dependent Makes happen: dynamic management and
control of resources, routes, schedules, ...
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Strategic System Analysis and Planning
International, national, regional planning All (most) products All (most) transportation modes
(infrastructure networks and services) Scenario analysis (“what if ?”)
Infrastructure modificationsEvolution of demandTechnology changesVariations in policy and economic environment…
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Methodological Approaches
Spatial price equilibrium Route/mode choice/loading Network optimization
Sequential shipper-carrierSystem-wide representation
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System-wide Modelling
Zones: origins and destination of freight Modes: transportation means/services Nodes and modal links Intermodal transfers Products: commodity groups Demand: origin-destination matrices by
product (and mode choice) Output:product flows and costs on links
(modes), transfers, and paths
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Model
( )
( )
( )
Min ( ) ( )
s.t. , , , , ( )
0, , , , , ( )
and , ,
, t ,
m pod
p
p
p p p pa a t t
p P a A t T
m pl odl L
m pl od
pa al ll L
pt tl ll L
F s v v s v v
h g o d N p P m p M
h l L o d N p P m p M
v h a A p P
v h T p P
Nonlinear (convex) multimode multicommoditynetwork flow formulation
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Technological Transfer
Computer-based decision support systems Custom-made vs. “tool box” Example: STAN, Strategic Transportation
Analysis, software for multimodal, multiproduct transportation systems
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Consolidation Transportation
Long distance freight carriers One vehicle/convoy serves many customers
Railways Less-than-truckload motor carriers Intermodal container transportation Express package services Control agencies, ...
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Consolidation Transportation
Accounts for a huge proportion of the freight moved both in volume and value
Vital component of transportation and economic systems
Less studied (compare to VRP, location, pure design) Fewer, more “remote” players Messier problems and formulations
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1
2
3
4
5
6
7
89
A
B
C
a
b
c
d e
f
Main route
Feeder routePick up and delivery route
![Page 19: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/19.jpg)
Consolidation TransportationCharacteristics
Regular services Consolidation terminals Frequencies and Schedules Operation efficiency = profits Service quality = customer satisfaction
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Physical Network
terminal A
terminal C
terminal D
terminal E
terminal F
SERVICE: - origin terminal - destination terminal - mode - frequency
Mode 1
Mode 2
terminal B
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terminal Aterminal B
terminal C
terminal D
terminal E
terminal F
ITINERARY: Path of services used to move freight from its origin to its final destination
(B, F)
(B, F)
(A, E
)
(B, F)
Physical and Service Networks
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terminal A
terminal C
terminal D
terminal E
terminal F
Trade-offs: Operating costs minimisation vs.
service quality maximization
(B, F)
(B, F)
(A, E
)
Freight consolidation
BEST SERVICE AT MINIMUM COST
(A, E)+
(A, E)+
(A, E)+
terminal B
(B, F)
and
Itineraries
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Cost vs. Service Trade-offs
Number of markets satisfying the service targets (%)
0 10 20 30 40 50 60 70 80 90290
440
490
590
690
390
340
740
640
540
Firm42%624000$
345000$
73%440000$
87%
M(in
100
0$)
transportation and handling costs
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Carrier Tactical Planning
Goal: optimal allocation and utilisation of resources to achieve the economic and customer service objectives of the company
Means: tactical plan(load, transportation, … plan)
Evaluation tool of strategic alternatives
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Carrier Tactical Planning
Interrelated decisionsService selection:
routes, frequencies, schedulesTraffic distribution:
itineraries, flow distributionTerminal policiesEmpty balancing
Interactions and trade-offsAmong operationsBetween cost and service quality (time) measures
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Tactical planning issues for freight carriers generally addressed through
Service Network Design formulations and methods
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Service Network Design
It’s planning => Network view Planning horizon
Strategic/Tactical Tactical/Operational
Generally several interacting resources Usually several interacting objectives Certainly many “decisions” Static or dynamic (deterministic) formulations
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Service Network Design:Model Classes
Location
Frequency
Schedules/Dispatching (Dynamic)
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Location Design
Strategic “long term” design of infrastructure considering impact on services and trafficLocation of terminalsLocation-routing
Not many models specific for long-haul consolidation freight transportation
Deterministic service network design models used to simulate scenarios
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Location Design
A few discrete location modelsProduction-distributionHub-network designMulticommodity location-allocation with
balancing requirements
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A Container Land Distribution and Transportation System
Loaded container
Empty container
Empty vehicle
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Location with Balancing
Locate depots to optimize the distribution and transportation of empty containers
MovementsCustomer to depot: return movementDepot to customer: allocation following requestBetween depots: to counter supply-demand
imbalances and reposition for future periods
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Network Structure
(flows of empty containers)
customers
customers
depots
demand
supply
k
Di'
Dip
Oip
Oi
s jkp
cijp
i
j
i'
![Page 34: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/34.jpg)
Network Structure
(flows of empty containers)
customers
customers
depots
demand
supply
k
Di'
xijp w jkp
Oi
i
j
i'
y j
xki p'
![Page 35: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/35.jpg)
Location With Balancing Formulation
Minimise
Subject to
[Demand / Flow conservation]
Z f y
c x c x s w
x O i C p P
x D i C p P
jj D
j
ijp ijp jip jip jkp jkpk Dj Dj Di Cp P
ijp ipj D
jip ipj D
{ ( ) }
,
,
![Page 36: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/36.jpg)
Location with Balancing Formulation
,
,
[Linking / Feasibility]
[Balancing]
,
0 ,
x O y i C j D p P
x D y i C j D p P
x w x w j D p P
x x i C j D p P
w j D k D p P
y
ijp ip j
jip ip j
ijpi C
kjpk D
jipi C
jkpk D
ijp jip
jkp
j
,
,
,
, ,
,
0
0
{ , }0 1 j D
![Page 37: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/37.jpg)
Frequency Service Network Design
Objectives Strategic planning and scenario analysis Study of interactions and trade-offs among
subsystems, decisions, objectives Typical issues
What type of service? How often over the planning horizon? Terminal workloads Traffic itineraries (includes empties)
![Page 38: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/38.jpg)
Two Major Approaches
Service levels as Decisions Service levels as Output
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Service Frequencies: Decisions
Integer frequencies Continuous flows Nonlinear Mixed Integer formulations:
frequency-related measures (costs, delays-congestion, etc.)
Physical network: given infrastructure Service network: decision structure Traffic itineraries: on service network
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F4
A1 F1A2 F2A3 F3
A3
CX1
A4
CX2
A4
SA4
CX3
A2
CA4
CX4
A2
TITINERARIES FOR A TRAFFIC-CLASS (O-D-C)
PHYSICAL NETWORK (NODES, LINKS)
A4SERVICE LEG
SERVICE NETWORK (ROUTES, STOPS, MODES, FREQUENIES)
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Model Elements
Physical network Nodes: rail yards and stations, LTL breakbulk
and end-of-line terminals, ports, …Links: tracks, roads, …Capacities and operational rules
ServiceRoute, type, costs, ...Frequency
y s Ss ,
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Model Elements
Demand Market = origin, destination, commodityEmpty vehicles = product(s)VolumeCosts, service and operational rulesSet of feasible itinerariesItinerary flows
x hlp
lp, Lp
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Model
Minimize “Fixed” cost of offering serviceCosts of moving the freight
through the service networkPenalties on unsatisfied service objectives or
operational rules and characteristics (e.g., capacities)
Subject toDemand satisfactionService and operation constraints
![Page 44: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/44.jpg)
Service Frequencies as Decision Variables
Minimize
Subject to
and integer
Specific service and operation constraints
ss S
lp
l Lp P
lp
l L
p
s
lp
y y h y h
h w p P
y s S
h l L p P
p
p
( ) ( , ) ( , )
,
0
0
![Page 45: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/45.jpg)
Service Cost
Determined by system characteristics and (potentially) all other services
Cost of operations in terminals and en-route Cost of time (average delay) spent in
terminals and en-route
s sO
sD
s sy C C E y( ) ( [ ])
![Page 46: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/46.jpg)
Itinerary Cost
Determined by system characteristics and (potentially) all services and itinerary flows for all markets
Cost of operations in terminals and en-route Cost of time (delay) spent in terminals and
en-route
lp
lpO
lpD
lp lpy h C C E h( , ) ( [ ])
![Page 47: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/47.jpg)
Itinerary Cost
Capacity considerations on service segments
C u y xpS
sk s sk(min{ , })0 2
lp
lpO
lpD p
lp lp lpy h C C H E h( , ) ( min{ , [ ] ( )) 0
Compliance with service targets
![Page 48: Optimization Models for Long-haul Freight Transportation Teodor Gabriel Crainic Dept. Management and Technology Université du Québec à Montréal and Centre](https://reader036.vdocuments.mx/reader036/viewer/2022062307/5518cab0550346a61f8b59ee/html5/thumbnails/48.jpg)
Delays - A Few Examples
Rail yard operations: car classification and blocking, train formation, …
Consolidation of freight in vehicles Waiting at terminal “gates” before admission Train delays due to meetings and overtakes on
the lines of the network. Departure/connection delays in terminals:
the waiting time for the designated service to be available
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Delays
Representation: Congestion functions Models: Engineering procedures + queuing
models
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Dynamic Service Network Design
Objectives Planning of “schedules” If or when services depart Traffic itineraries
Space-time graphs
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Space-time Diagram
Holding arcEmpty repositioningLoaded movementEnd of horizon
Ter
min
als
Time
Current Period Future Periods
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Dynamic Service Network Design
“To operate or not” a given service at a given moment (0,1) variables
Continuous flows (usually) Capacity constraints/considerations Special operational constraints (often) MIP formulations: the previous formulations
in a time-dependent framework Deterministic (for now)
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Most service network design and related issues yield
Fixed Cost, Capacitated, Multicommodity Network
Design Formulations
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Minimise
Subject to
z h y f y k h
h w p P
h u y ij A
h u y ij A p P
h p P l L
ijij A
ij lp
l Lp Plp
lp
l L
p
lp
ijlp
l Lp Pij ij
lp
ijlp
l Lij ij
lp p
p
p
p
p
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0
LINEAR PATH-BASED FORMULATION
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SERVICE NET.DES. SOLUTION METHODS
Network design formulations are difficult (even in simple cases)
Problem instances are very large(time dependencies)
Mainly heuristics and metaheuristics A few MIP (+ heuristics) methods Some models integrated in decision support
systems
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SOLUTION METHODS
Work in progress on network designMetaheuristicsModel analysis and polyhedral characterizationBranch-and-bound (and cut, and price, …)HybridsParallel optimization
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CONCLUSIONS
Transportation offers many challenges and opportunities: planning, operations management, control (dynamic, real-time)
Operations Research and Mathematical Programming models and methods offer good analysis framework and solution approaches
Need to develop efficient implementations and user-friendly decision support systems
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Many challenges yet
Models (more realistic, more real-time) Math. analysis of formulations Computing efficiency Integration with
TelecommunicationsElectronic commerce
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Operational Planning and Management
Crew scheduling Terminal and Line-haul operations Empty vehicle distribution and
repositioning Dynamic allocation and dispatching of
resources
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Issues
Time-dependent elements (e.g., demand) and decisions
Stochastic variations in demands, supplies, travel times, …
Network interactions still strong Impact of real-time information and ITS Decision support systems