Intermodal Transportation
Teodor Gabriel Crainic
ESG UQAM
&
CIRRELT - CRT
CIRRELT
Plan
What are we talking about?
à Container-based intermodal transportation
à System design (location)
à Fleet Management (empties)
à Perspectives
à
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© Teodor Gabriel Crainic 2006
What are we talking about?
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© Teodor Gabriel Crainic 2006
Intermodal Transportation
Simple and straightforward definition:
à Movement of a person or a load by a sequence of at least
two transportation modes, the transfer from one mode to
the next being performed at a (intermodal) terminal
à E.g., Door-to-door transportation of containers
Ó Over long distances
Ó Origin → “land” transport → port → container ship
→ port → “land” transport → destination
à
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© Teodor Gabriel Crainic 2006
A “Strict” Definition
Movement of goods
à One and the same loading unit or vehicle
à A chain of
à Several transportation modes (services)
Ó Coordination
Ó Interactions
à Intermodal terminals
Ó No handling of the goods themselves
à Door-to-door service
à
European Conference of Ministers of Transport (1993)
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© Teodor Gabriel Crainic 2006
A More General Definition
Movement of goods
à A chain (network)
à Several transportation modes (services)
Ó Coordination (more or less)
Ó Interactions
à Intermodal terminals
à “Door-to-door” service
à
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© Teodor Gabriel Crainic 2006
Many Things to Many People
Major instrument for E.U. policy aimed at switching
freight from trucks & highways to more environmentfriendly modes (rail, water)
à Dedicated rail services (subdivisions) to move large
volumes of containers/trailers over long distances: the
trans-continental “land bridges”
à Container transportation
à Consolidation carriers: local & long-haul operations,
several long-haul types of services, with/without use of
external services
à
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© Teodor Gabriel Crainic 2006
Many Things to Many People (2)
Uncontainerized cargo
à Courier (express) services
à National planning
à City Logistics
à Terminal (ports, airports, …) planning and operations
à
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© Teodor Gabriel Crainic 2006
Scope of Presentation
Container-based intermodal transportation
Ó Illustrative planning/operations management issues
Ó Operations research models and methods
à A very young field
à No definite answers
à An open invitation to join in !! ☺
à
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© Teodor Gabriel Crainic 2006
Plan
What are we talking about?
à Container-based intermodal transportation
à System design (location)
à Fleet Management (empties)
à Perspectives
à
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© Teodor Gabriel Crainic 2006
Intermodal Transportation – Containers
Advantages
Ó Reduced cargo handling
Ó Increased security regarding damage and loss
Ó Increased standardization of transportation and
transfer equipment
Ó Increased automation of terminal operations
⇒ Cost reduction, more efficient door-to-door
transportation
à Sustained growth
à
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© Teodor Gabriel Crainic 2006
Evolution of Container Traffic (Koh and Kim 2001)
Year
1993
1995
1997
1999
2000
2001
2002
2003
Container traffic (M)
113.2
137.2
153.5
203.2
225.3
231.6
240.6
254.6
Growth rate (%)
12.5
9.8
4.2
10.0
10.9
2.8
3.9
5.8
2004
280.0
10.6
2005
304.0
8.6
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© Teodor Gabriel Crainic 2006
Intermodal Transportation – Containers (2)
Lifeline of world-wide trade and economy
à Increasingly larger container ships for inter-continental
transportation (liners)
Ó These cannot berth at all ports
Ó It is not economical to stop at many ports
à Container mega ports
à New coastal navigation feeder services (“regular” ships):
mega ports and huge liners ↔ regular ports
⇒ A new link in the multi-modal chain
à “New” long-distance rail services (double-stack)
à
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© Teodor Gabriel Crainic 2006
Intermodal Transportation – Containers (3)
Asia (Hong Kong, Singapore, …) to America or Europe:
à Origin → truck → port → large container ship (liner)
→ mega port → “small” container vessel → port →
truck/rail/river → destination
Þ
à
Container port terminal transformations for increased
efficiency in loading/unloading operations and
exchanges with land carriers
Ó New terminals / Enhancement of existing ones
Ó Automation
Ó Intelligent Transportation Systems
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© Teodor Gabriel Crainic 2006
Notes
Container intermodal transportation
(& express courier / post services)
Ó Customer: Customized service
Ó Operator(s): Hub-and-spoke network with
consolidation
à All long-haul transportation must address the issue of
empty vehicle repositioning
Ó Trade is unbalanced
⇒ Vehicle flows as well !!
Ó Tight fleet size? Long distances?
à
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© Teodor Gabriel Crainic 2006
Plan
What are we talking about?
à Container-based intermodal transportation
à System design (location)
à Fleet Management (empties)
à Perspectives
à
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© Teodor Gabriel Crainic 2006
System and Service Design
Strategic decisions – System Design
à Locate (intermodal) terminals
à Direct/indirect customer (zone) service
à Port/terminal dimensioning
Ó Number of berths
Ó Size of storage space
Ó Type & number of various equipment types
à Facility & service abandon, …
à
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© Teodor Gabriel Crainic 2006
System and Service Design (2)
Tactical decisions – Service Design
Ó Routes served (routes, stops, mode, equipment, …)
Ó Service frequency & schedule
Ó Cargo routing
Ó Terminal workloads
à Container port terminal equipment assignment
Ó To sea-side and land-side operations
à Not many contributions for container transportation
à
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© Teodor Gabriel Crainic 2006
System Design
Not many contributions
Ó Tactical or operational models to evaluate strategic
strategies
Ó Ports: queuing, simulation
à Discrete location models
Ó Consolidation / hub terminals
à Network design + location
Ó Select direct services/links
à Aim to capture economies of scale associated to
consolidation of freight
à
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© Teodor Gabriel Crainic 2006
System Design (2)
à
Location of facilities (terminals)
Ó Production-distribution
Ó Hub location
Ó Location with balancing requirements
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© Teodor Gabriel Crainic 2006
Location with Balancing Requirements
Land part of an intermodal container transportation
system (may be generalized)
à Use in-land container depots for more efficient
operations and reduced empty travel
à
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© Teodor Gabriel Crainic 2006
“Traditional” Operations
Loaded
Empty
Importing
customer
Exporting
customer
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© Teodor Gabriel Crainic 2006
Operations with In-Land Depots
Loaded
Empty
Empty
balancing
Importing
customer
Exporting
customer
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© Teodor Gabriel Crainic 2006
Location with Balancing Requirements (2)
Loaded movements are “profitable”
à Empty movements are not
Ó Customer to depot: Return movement
à
ISupply of empties
Ó Depot
to customer: Demand satisfaction
IDemand for empties
Ó Depot
to depot: Repositioning of empty containers
due to unbalances in supply/demand
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© Teodor Gabriel Crainic 2006
Location with Balancing Requirements Network
Oi
supply Oip
i
customers
cijp
s jkp
j
k
depots
i'
customers
demand
D i'
Dip
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© Teodor Gabriel Crainic 2006
Oi
supply
i
customers
xijp
yj
wjkp
j
k
depots
xki ' p
i'
customers
demand
D i'
(flows of empty containers)
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© Teodor Gabriel Crainic 2006
Location with Balancing Formulation
Minimise Z = ∑ f j y j
j ∈D
+ ∑ {∑ ∑ (cijp xijp + c jip x jip ) + ∑ ∑ s jkp w jkp}
p∈P i ∈C j ∈D
Subject to
j ∈D k ∈D
xijp = Oip
∑
j ∈D
i ∈C, p ∈ P
x jip = Dip
∑
j ∈D
i ∈C, p ∈ P
[Demand / Flow conservation]
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© Teodor Gabriel Crainic 2006
Location with Balancing Formulation (2)
xijp ≤ Oip y j
i ∈ C, j ∈ D, p ∈ P
x jip ≤ Dip y j
i ∈ C, j ∈ D, p ∈ P
[Linking / Feasibility]
∑x
i ∈C
ijp
+ ∑ wkjp − ∑ x jip − ∑ w jkp = 0
k ∈D
i ∈C
j ∈ D, p ∈ P
k ∈D
xijp , x jip ≥ 0
[Balancing]
i ∈ C, j ∈ D, p ∈ P
w jkp ≥ 0
j ∈ D, k ∈ D, p ∈ P
y j ∈{0,1}
j ∈D
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© Teodor Gabriel Crainic 2006
Plan
What are we talking about?
à Brief overview of freight transportation
à Container-based intermodal transportation
à System design (location)
à Fleet Management (empties)
à Perspectives
à
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© Teodor Gabriel Crainic 2006
Operational Planning
Resource management
Ó Crews
Ó Vehicles
Ó Power (engines, etc.), and so on
à Allocation – dispatching, schedules
Ó Make sure the required resources are where they
need to be when they need to be there
Ó Be efficient !
Ó (Satisfy demand, achieve economic and service
objectives, implement plan, obey laws, policies, …)
à
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© Teodor Gabriel Crainic 2006
Issues
Trade is unbalanced
à Moving goods results in unbalanced distribution of
resources: crews, vehicles, etc.
à One needs to reposition resources for use in the
following periods
Ó Regular operations (if possible)
Ó Balancing operations (vehicles, power units, …)
Ó Crews travelling as passengers
à The demand in following periods is a forecast
à
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© Teodor Gabriel Crainic 2006
Other Operational Issues
Real-time dispatch
à Pacing
à Real-time routing adjustment
à…
à
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© Teodor Gabriel Crainic 2006
Consolidation Transportation
Transportation plan “guides” operations
à It includes guidelines for repositioning (it should …)
à “Indicative” schedules: Ad-hoc (real-time) procedures
à “Regular” demand planning: Short-term and real-time
adjustment of plans
à Scheduled operations: Repositioning must follow and
“feed” schedules + real-time adjustment
à Well-defined crew (personnel) schedules
à
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© Teodor Gabriel Crainic 2006
Customized Transportation
No plan !!
à Dynamic management and control of resources: routes,
schedules, fleets, personnel, etc.
à Uncertainty plays important role
Ó Demand
Ó Travel times
Ó Service times at customers and terminals
Ó Weather, …
à
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© Teodor Gabriel Crainic 2006
The Empty Vehicle Repositioning Problem
Surpluses and deficits of empty vehicles
Ó Observed at terminals “at the end” of the day
Ó Computed with respect to the next period demand
à Need to reposition for the next period
Ó How many vehicles (of what type) to move from a
surplus location (origin) to a deficit location
(destination)?
à Much more decision freedom than in loaded
transportation
à A cost activity with “no” profit
à
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© Teodor Gabriel Crainic 2006
History
à
Transportation model – static and deterministic
Ó Known surpluses and deficits – No uncertainties
Ó No (not important) travel time impact – Static
Ó Arrival times known (certain prediction)
IAll travel, loaded and empty, occurs during the same
period
Ó Single
or fully substituable resources (vehicles)
Ó For certain LTL types, tactical planning, …
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© Teodor Gabriel Crainic 2006
History (2)
à
Deterministic, multi-period transshipment model
Ó Different movements require different travel times
Ó Vehicles become empty at different moments
(customer release times)
Ó Demand varies in time …
Ó The dynamic characteristic of the system represented
through (dynamic, time-dependent) space-time
networks
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© Teodor Gabriel Crainic 2006
Space-Time Networks
Nodes: Facilities – terminals, customers, etc. – at given
time periods
Ó A physical point is repeated at each period & activity
à Arcs: Movements in space and time
Ó Independent, e.g., a truck moving by itself
Ó Grouped, e.g., containers on flat cars (rail) or in a
ship
Ó Holding decisions (vehicles or cargo)
à
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© Teodor Gabriel Crainic 2006
Space-Time Network (Simple)
Terminals
Time
Current period
Future periods
Inventory (holding) arc
Repositioning arc
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© Teodor Gabriel Crainic 2006
Space-Time Network
Terminals
Time
Current period
Future periods
Loaded movement
End of horizon
Inventory (holding) arc
Repositioning arc
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© Teodor Gabriel Crainic 2006
Challenges and Limitations
à
One may include
Ó Capacities
Ó Several types of resources
Ó Inventory costs
IStock out (rent, borrow, …)
IEnd of horizon value
Ó Substitutions
(and costs)
Ó Complex cost structures
à Linear programming formulations with a few
“complications”
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© Teodor Gabriel Crainic 2006
Challenges and Limitations (2)
Planning horizon length? End-of-horizon?
Ó Rolling horizon
à Everything is deterministic
Ó Times (travel, terminal operations, customer, …)
Ó Future demand, etc.
à Utilization
Ó Strictly scheduled railways with bookings
à
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The Uncertainty Factor
Times may vary
à Demand estimation is rarely precise
à Unexpected demands and events
à Current decisions impact the future state of the system
and future decisions
à Need to explicitly consider / model
Ó Uncertainty – the stochastic nature – of the system
and its environment
Ó The impact of current decisions on future system state
and decisions
à
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© Teodor Gabriel Crainic 2006
The Uncertainty Factor (2)
Stochastic formulations and solution methods
à A complex field: General approaches and, often,
custom-designed methods
à Active research field
à Formulations
Ó General stochastic programming and solution
methods :
à
IFew efficient applications to transport problems
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© Teodor Gabriel Crainic 2006
The Uncertainty Factor (3)
à
Formulations
Ó Recourse formulations and rolling horizon methods
INice application to regular-type systems (e.g.,
consolidation)
Ó Stochastic
formulation and solution strategies based
on adaptive dynamic (linear) programming and
decision/time-based decomposition
ITime-Space multicommodity networks
IRecent developments with interesting results
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© Teodor Gabriel Crainic 2006
The Uncertainty Factor (4)
Challenges of stochastic formulations
Ó Problem formulation (!!)
Ó Resolution (!!)
à Plus
Ó Representation of resources and attributes
Ó Forecasts
Ó Availability and reliability of data
Ó Validation of models and strategies
Ó Implementation and follow up
à
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© Teodor Gabriel Crainic 2006
Container (Empty) Fleet Management
Major repositioning decisions over large geographical
regions (e.g., inter-continental movements)
Ó Similar to consolidation transportation
à Allocation of empty containers to customers
Ó Similar to customized transportation
à Two applications in this talk
Ó Allocation and management of a heterogeneous fleet
of containers over a land zone
Ó Single-commodity dynamic container allocation for
liner operators (regular ocean navigation lines)
à
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© Teodor Gabriel Crainic 2006
Heterogeneous Fleet
Given region (continent)
à Loaded containers arrive (e.g., maritime network) to be
delivered to customers
à Empty containers arrive or leave to balance system-wide
operations (demand)
à Customers empty containers that must be moved out
à Customers require empty containers for future loaded
shipments
à One must manage the fleet of containers for maximum
profit, while satisfying demand
à
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© Teodor Gabriel Crainic 2006
Heterogeneous Fleet (2)
Several types of containers (e.g., 20 or 40 feet, normal
box, thermal, refrigerated, etc.)
à Substitutions allowed: conditions and costs
à “Massive” inter-depot balancing movements
à Due-dates at some terminals (e.g., ship schedules)
à Time windows at customers
à Demand (at least part of) fluctuates in time and is
forecasted only
à Unloading time at customer: Uncertain
à Travel times may be uncertain as well
à
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© Teodor Gabriel Crainic 2006
Heterogeneous Fleet (3)
Containers may be damaged partially (repairs) or totally
à External sources (buy, rent) of empty containers
à Centralized empty container fleet management
à Loaded movements not “managed”
à Associated problem: global management of the empty &
loaded container movements together with vehicle
routes
à A single model not computationally feasible ⇒
hierarchical DSS
à
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Main Movements
(No Time/Container Type Specifics)
Demand
customer
Depot j
Harbour
Supply
customer
External
pool of
empty
containers
Depot k
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© Teodor Gabriel Crainic 2006
Formulations
Crainic, Dejax, Gendreau (93)
à Single and multicommodity deterministic formulations
à A two-stage, restricted recourse single commodity,
stochastic model
à Multicommodity stochastic formulations may be defined
à
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© Teodor Gabriel Crainic 2006
Formulations (2)
Space-time diagram
Ó Generalized network (substitutions)
Ó Multiple-period transportation arcs
Ó Holding arcs (depots)
Ó Inter-depot balancing arcs
à Stochastic elements
Ó Demand (of known and possible customers)
Ó Release time from supply customers
⇒ Inventory levels
⇒ Import and export (border points, external pool)
à
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© Teodor Gabriel Crainic 2006
Formulations (3)
Minimize total cost over the time horizon
à Flow conservation (over time and space, including
access to external pool)
Ó Supply at supply customers
Ó Demand
Ó Container substitution
à Depot (and port) inventories (each container type)
à Bounds on inter-depot balancing flows
à “End-of-horizon” restrictions (e.g., limits on “final”
inventories)
à
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© Teodor Gabriel Crainic 2006
Single Commodity Liner
Cheung and Chen 1998
à A container liner company offers regular service among
a number of ports
à Carries loaded and, space permitting, empty containers
à Ship schedules known and fixed
à 1 ship / period between 2 ports
à Demand less than ship capacity
à 1 container type
à
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© Teodor Gabriel Crainic 2006
Formulation
Two-stage stochastic model
Ó Time-space network with random arc capacities
Ó Minimize the (expected) total cost
Ó Rolling-horizon mode
à Sources of randomness
Ó Ship residual capacity for taking empty containers
(given port and time period)
Ó Demand for containers at each port/time
Ó Supply of containers at each port /time before
unloading from ships
à
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© Teodor Gabriel Crainic 2006
Formulation (2)
Minimize total expected (cost – revenue from demand)
à Ship container conservation: containers unloaded
à Ship container conservation: containers loaded for
repositioning
à Port container conservation / demand satisfaction
à Ship residual capacity for repositioning
à Number of leased containers
à
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© Teodor Gabriel Crainic 2006
Plan
What are we talking about?
à Brief overview of freight transportation
à Container-based intermodal transportation
à System design (location)
à Fleet Management (empties)
à Perspectives
à
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© Teodor Gabriel Crainic 2006
Perspectives
Intermodal transportation
Ó Growing steadily & should continue to grow
Ó Containers and other modes
à Profound modifications to the economic, regulatory,
technological, social and political environment of
industry
à Globalization, automation, ITS, e-logistics, security, …
à Need for innovative and enhanced planning and
management procedures
à Opportunities for Operations Research and
Transportation Science
à
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© Teodor Gabriel Crainic 2006
Perspectives (2)
A number of research efforts and important results
à Much more work is needed!
à Many issues application areas not/little addressed
à Industry evolution ⇒ New problems
à
à
Ports & terminals
Ó Planning (all levels)
Ó Integration of operations & equipment types
Ó Automation
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© Teodor Gabriel Crainic 2006
Perspectives (3)
à
Carrier strategic & tactical planning
Ó More studied than terminals, but
Ó Better representation/integration of “local” operations
and characteristics
Ó Integration of employee scheduling impacts/relations
Ó Better representation of time dependencies
Ó Better integration of stochastic aspects into long-term
planning models
Ó Algorithmic developments
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© Teodor Gabriel Crainic 2006
Perspectives (4)
à
Short-term planning
Ó Time-dependent, stochastic formulations and
algorithms
Ó Integrated models, e.g.,
IContainer fleet management over land and sea
IVehicles, power, crews, …
à
Modelling of ITS and e-logistics and integration to
planning and management models; e.g.,
Ó Flow of information
Ó Impact on uncertainty
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© Teodor Gabriel Crainic 2006
Perspectives (5)
Modelling the impact of security measures and
addressing the new issues
à Logistic networks
Ó Coordination & synchronization
Ó Information flows and uncertainties
à Methodology
Ó Models
Ó Methods exact and (meta) heuristic
Ó Parallel computation
à
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© Teodor Gabriel Crainic 2006