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Dynamic Dispatching and Transport Optimization - Real-World Experience with Perspectives on Pervasive Technology Integration.


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Abstract This paper discusses a technological solution to real-time road transportation optimization using a commercial multi-agent based system, LS/ATN, which has been proven through real-world deployment to reduce transportation costs for both small and large fleets in the full and part load business. Subsequent to describing the real-time optimization approach, we discuss how ,the platform is currently evolving to accept live data from vehicles in the fleet in order to improve optimization accuracy. A selection of the predominant pervasive technologies available today for enhancing intelligent route optimization is described.
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Dynamic Dispatching and Transport Optimization – Real-World Experience
with Perspectives on Pervasive Technology Integration
Dominic Greenwood
Whitestein Technologies AG
Christian Dannegger
Whitestein Technologies GmbH
Klaus Dorer
Hochschule Offenburg
This paper discusses a technological solution to
real-time road transportation optimization using a
commercial multi-agent based system, LS/ATN, which
has been proven through real-world deployment to
reduce transportation costs for both small and large
fleets in the full and part load business. Subsequent to
describing the real-time optimization approach, we
discuss how the platform is currently evolving to
accept live data from vehicles in the fleet in order to
improve optimization accuracy. A selection of the
predominant pervasive technologies available today
for enhancing intelligent route optimization is
1. Introduction
In today’s fast paced, data-intensive and uncertain
global markets, road freight transportation is a high-
pressure environment. Competition is fierce, margins
are slender and coordination can be both distributed
and intensely complex. Supply relations are
transnational, often global, and goods must reach their
destinations faster than ever before as they are now
often directly entered into production processes
without passing through transitional storage [1].
As a result, many companies are seeking to control
costs by enhancing their traditional human-centric
dispatching methods with technology capable of
intelligent, real-time freight capacity and route
optimization [2]. The capacity aspect ensures efficient
use of transport capacity while the route aspect ensures
that trucks take the most efficient route between order
pickups and deliveries. These are tractable, yet
complex optimization problems because plans can
effectively become obsolete the moment a truck leaves
the loading dock due to unforeseen real-world events.
It is thus mission-critical to assist human dispatchers
with the computational systems to quickly and
accurately re-plan capacity and routing. The Living
Systems® Adaptive Transportation Networks
(LS/ATN) software, see Figure 1, developed by
Whitestein Technologies in close collaboration with
worldwide logistics providers (e.g. DHL), is a concrete
example of how automatic, real-time optimization and
execution capabilities lead to reductions in
transportation operating costs while improving service
quality to the customer [3].
A key feature of LS/ATN is the integration of real-
time track and trace data feeds from on-route vehicles,
which act as feedback measures to the optimizer
engine. This allows continuous adaptation and
regeneration of dynamic route plans based on the real
world environment. Close integration with key
pervasive technologies such as GPS and reliable multi-
network communication offers the ability to enhance
core system intelligence with fast, timely and accurate
measures of the live environment [4]. Continuous
transmission of vehicle state and location information
provides live feedback metrics for the optimization
platform, allowing human dispatchers to improve the
efficiency of entire fleets. This flexibility enables
logistics providers to react quickly to new customer
requirements, rapidly altering transport routes to
accommodate unexpected events and new orders.
Figure 1. Details of a route in the LS/ATN dispatching
support system, as suggested by an optimizer agent
This paper examines these issues by first
describing the core optimization process of the
LS/ATN planning and scheduling system for road
freight transportation. A selection of the most relevant
pervasive technologies currently available to support
this decision support system with real-time data-feeds
is then reviewed, followed by an analysis of how these
technologies can integrate with LS/ATN to bring about
direct benefits to the route optimization process. The
article concludes with a discussion of progress and
open challenges.
2. Intelligent Route Optimization
Today most logistics companies use computational
tools, collectively known as Transport Management
Systems (TMS), such as Transportation Planner from
i2 Logistics, AxsFreight from Transaxiom, Cargobase,
Elit and Transflow, to plan their transportation network
from a strategic level all the way through to sub-daily
route schedules. However, many TMS are unable to
adequately handle unexpected events and generate plan
alterations in real-time. When dealing with large
numbers of distributed customers, limited fleet size,
last minute changes to orders, or unexpected non-
availability of vehicles due to traffic jams, breakdowns
or accidents, static planning systems suffer from
limited effectiveness. Significant human effort is
required to manually adapt plans and control their
In addition, vehicles can be of different types and
capacities, are usually available at different locations
and drivers must observe regulated drive time
restrictions. To cope with all this, new, intelligent
approaches to route planning are emerging that are
capable of continuously determining optimal routes in
response to transportation requests arriving
simultaneously from many customers. The key
challenge lies in allocating a finite number of vehicles
of varying capacity and available at different locations
such that transportation time and costs are minimized,
while the number of on-time pick-ups and deliveries,
and therefore customer satisfaction, are maximized.
2.1.Real-Time mPDPSTW Route Optimization
One means to tackle this optimization problem is
by considering it as a multiple Pick up and Delivery
Problem with Time Windows (mPDPTW) problem [5]
which concerns the computation of the optimal set of
routes for a fleet of vehicles in order to satisfy a
collection of transportation orders while complying
with available time windows at customer locations.
To solve the real-world challenge to an acceptable
degree it is even necessary to add another two aspects.
First the capability to react in real-time and second to
deal with time constraints in a flexible manner, use
penalty costs to decide between a new vehicle or being
late. This results in the even more complex Multiple
Pick up and Delivery Problem with Soft Time
Windows in Real Time (R/T mPDPSTW).
Thus, in addition to a pickup and delivery location,
each order includes the time windows within which the
order must be picked up and delivered. Vehicles are
dispatched from selected starting locations and routes
are computed such that each request can be
successfully transferred from origin to destination. The
goal of R/T mPDPSTW is to provide feasible
schedules, which satisfy the time window constraints
for each vehicle to deliver to a set of customers with
known demands on minimum-cost vehicle routes.
Another aspect is the capability to suggest charter
trucks (dynamically add resources) when appropriate –
when charter trucks are cheaper than own existing
To dynamically solve the R/T mPDPSTW problem,
the LS/ATN transportation optimizer [3] used by DHL
throughout Europe, segments and distributes the
problem across a population of goal-directed software
agents. Built on a bottom-up optimization philosophy,
goal-directed agents cooperate to exchange client
orders between one another, adjusting their schedules
accordingly with the goal of minimizing the overall
cost. Similar to human decision-making, solutions to
problems arise from the interaction of individual
decision makers (represented by software agents), each
with its own local knowledge. The centralized, batch-
oriented nature of traditional IT systems imposes
intrinsic limits on dealing successfully with
unpredictability and dynamic change. Multiagent
systems are not restricted in this way because
collaborating agents quickly adapt to changing
circumstances and operational constraints. For real-
time route optimization, it is simply not feasible to re-
run a batch optimizer to adjust a transport plan every
time a new event is received. Reality has shown that
events such as order changes occur, on average, 1.3
times per order. Distributed, collaborating software
processes, i.e., agents, can however work together by
partitioning the optimization problem and following
the bottom-up approach, thereby solving the
optimization in near real-time.
In Figure 2, we show the integration of LS/ATN
into the main dispatching process and how it interacts
with the transportation environment.
Figure 2. The main operating process of LS/ATN.
Client orders are received by the LS/ATN system
through communication with the Transportation
Management System (TMS) Order Entry functionality.
The client data is processed by the Dispatching
Support module of the LS/ATN system. Routes are
computed based on inter-agent cooperation, and
improved with the agent-based optimization algorithms
described below. The plan obtained from this
collaborative processing is then reported to the
dispatcher for execution, and optionally for manual
dispatching. The final routes accepted for execution
and potentially adapted with manual dispatching are
ordered for execution to a carrier and reported to the
Tracking module of the LS/ATN system. The tracking
module supports tracking and event handling for orders
and trucks during the execution phase of the transport.
Once this is finished it sends the routes back to the
dispatcher for post execution administrative operations.
The final decisions regarding costs are reported to the
accounting module of the TMS system.
For R/T mPDPSTW optimization, an agent
represents a geographical region, or business unit, with
freight movement modeled as information flow
between the agents - see Figure 3. Incoming
transportation requests are distributed by an
AgentRegionBroker (not shown) to the AgentRegion-
Manager governing the region containing the pickup
location. The number of such agents depends on the
customer’s setup of (regional) business units and varies
between 6 and 60 for current deployments. In the
larger case, 10000 vehicles and up to 40000 order
requests are processed daily. This implies that no more
than a few seconds are available to re-optimize a
transportation plan when, for example, a new order
must be integrated. Each AgentRegionManager
generates a transportation plan specifying which orders
to combine into which routes and which vehicles
should be assigned to those routes. Agents exchange
information using a negotiation protocol to
sequentially insert transportation requests, while
continually verifying vehicle availability, capacity and
Figure 3. Illustration of freight transportation in
Europe partitioned into 6 regions each with its own
AgentRegionManager. Blue circles represent major
transport hubs and red lines indicate example routes
connecting hubs.
The optimization process incrementally reflects the
dynamics of the underlying mPDPSTW settings.
Whenever a new transport order is entered into the
system, the current delivery plan is updated. This is
achieved in a two-phase approach: (1) a new, valid,
solution is generated including the new transportation
request, then, (2) the obtained solution is improved
through negotiation between the agents over the
possibility of exchanging orders in order to reduce
overall costs. The algorithm used to assign an order to
a truck is a sequential insertion of orders [13]. All
available trucks under control of the AgentRegion-
Managers are checked as to whether they are able to
transport the order, and the implied level of costs
incurred. The order is assigned to the truck with the
least additional costs.
Sequential insertion with requests for quotes to all
trucks potentially produces suboptimal solutions. See
for instance the example given in Figure 4. Order 1 is
the first to arrive in the system and is assigned to truck
1’s route. Order 2 is also optimally assigned to truck
1’s route since that produces least additional costs (and
kilometres). When order 3 arrives truck 1 is fully
loaded, therefore a new truck 2 is used for order 3 and
later for order 4. In order to improve the solution a
further optimization step is performed by cyclic
transfers between trucks. A cyclic transfer is an
exchange of orders between routes. Figure 5 shows
how the suboptimal example in Figure 4 is improved
by a transfer of order 2 from route 1 to 2 while order 4
is transferred from route 2 to 1.
Delivery all orders
Pickup order 1
Pickup order 2
Pickup order 3
Pickup order 4
Truck 1 start Truck 2 start
Figure 4. Example of a suboptimal solution given by
the insertion algorithm for 4 orders.
Delivery all orders
Pickup order 1
Pickup order 2
Pickup order 3
Pickup order 4
Truck 1 start Truck 2 start
Figure 5. Optimal solution, for the example given in
Figure 4, after an order transfer.
The optimization procedure must determine which
transfers should be triggered. The AgentRegion-
Manager therefore starts a negotiation process with the
truck that was most recently changed. That truck is
initiating transfer requests to all other trucks under
control of the AgentRegionManager. From all the
requests the most significant cost-saving transfer is
performed. This changes the routes of both trucks
involved. This hill climbing process is then continued
with all changed routes until no more cost-saving
exchanges can be achieved.
While the optimization function is 100% cost-
based, other objectives must be satisfy when
calculating routes. Some of these constraints are
compulsory (hard), such as capacity and weight
limitations of the vehicle, customer opening hours, that
pickup date is before delivery date, and that pickup and
delivery are performed by the same vehicle. Other soft
constraints can be violated with a cost penalty, such as
missing the latest possible pickup time or delivery
2.2. Performance in the Field
The major goal of logistics companies is reduction
in transportation costs, although there are other KPIs
such as kilometers driven and solution quality are also
highly relevant as optimization targets. Transportation
cost is most readily reduced by achieving higher
utilization of transportation capacity, naturally
resulting in a reduced number of driven kilometers and
fewer required vehicles.
A partial dataset from our major customer, DHL
Freight, contains around 3500 real-business
transportation requests. In terms of the optimization
results gained by comparing the solution of manual
dispatching this requests against processing the same
orders with LS/ATN, a total of 11.7% cost savings was
achieved, where 4.2% of the cost savings stem from an
equal reduction in driven kilometers. An additional
achievement is that the number of vehicles used is
25.5% lower compared to the manual solution. The
cost savings would even be higher if fixed costs for the
vehicles were feasible, which is not the case in the
charter business, but possibly in other transportation
LS/ATN agent-based optimization guarantees a
higher service level in terms of results quality. The
high solution quality corresponds to a reduced number
of violated constraints. The system allows the desired
level of service quality to be fine-tuned. Figure 6
presents results obtained from LS/ATN relative to the
manual dispatching solution. The first proposed
solution (ATN1) provides a reduction of 8.33 % in
driven kilometers at the same service level with no
more than 25% of violated constraints. Moreover, this
solution provides also a reduction of 8% in terms of
kilometers driven with empty trucks. The second
solution (ATN2) provides a reduction in driven
kilometers of 0.78% relative to the manual dispatching
solutions, while providing a significant higher service
level: only 2.5% of violated constraints with more than
6h delay. The third solution (ATN3) provides an
increase of only 1.66% in terms of driven kilometers,
while meeting all the constraints to 100%.
Through the use of the automatic optimization a
lower process cost is being achieved. This is due to
automatic handling of plan deviations and evaluation
of solution options in real-time. Moreover, through
automation the communication costs in terms of
dispatcher’s time and material is reduced. Better
customer support can be guaranteed through fast,
comprehensive and up-to-date information about order
execution. Automation also allows processing of a
higher number of orders than with manual dispatching.
This is an important issue as the size of data to be
managed is increasingly growing. Other advantages of
using LS/ATN are cost transparency and seamless
integration with various TMS.
2.3. Integrating Real-time Vehicle Data
Although capacity and route optimization tools are
proven to produce significant reductions in operating
costs, many in the transportation industry are acutely
aware that one key and often missing component of the
optimization strategy is the provision of real-time
feedback from en route vehicles. The objective is an
intelligent transportation management system with
every vehicle providing up-to-date information of
progress through a pickup/delivery schedule and with
on-board sensors detecting, for example, when freight
is loaded and unloaded, and whether its condition (e.g.,
temperature) is within tolerance limits.
The ‘intelligent transportation management
systems’ model [7] developed within the transportation
industry is grounded with the principle of vehicle
tracking and incorporation of real-time information
into the transportation management process using
available pervasive technologies. The emerging
approaches to realizing this model involve various
combinations of pervasive technologies, some of which
are all highlighted in the following section.
Figure 6. Improvements obtained with LS/ATN over
manual dispatching
3. Pervasive Technologies
To provide live data feeds to a route optimizer such
as LS/ATN, freight vehicles are equipped with a
variety of pervasive technologies capable of
measuring, coordinating and communicating
information. This section highlights some of the most
relevant technologies in use today, or in the early
phases of adoption. LS/ATN is able to make use of
data sourced from, manipulated or transmitted by any
of these technologies to enhance the route optimization
3.1. On-Board Units and Vehicle State Sensors
An OBU, otherwise known as ‘the black box’, is a
vehicle-mounted module with a processor and local
memory that is capable of integrating other on-board
technologies such as load-status sensors, digital
tachographs, toll collection units, on-board and fleet
management systems, and remote communications
facilities. The majority of OBUs in use today, such as
the VDO FM Onboard series from Siemens, the
CarrierWeb logistics platform and EFAS from
DelphiGrundig, are typically used to record vehicle
location, calculate toll charges and store vehicle-
specific information such as identity, class, weight and
configuration. Some emerging OBUs will have
increased processing capabilities allowing them to
correlate and pre-process collected data locally prior to
transmission. This offers the possibility of more
computational intelligence installed within the vehicle
enabling in-situ diagnostics and dynamic coordination
with the remote planning optimizer such that the
vehicle becomes an active participant in the planning
process, rather than simply a passive provider and
recipient of data.
Vehicle data, in its most common form, relates to
the state of the vehicle itself, including, for example,
tire pressure, engine condition and emissions data.
Automatic acquisition of this data by on-board sensors
and its transmission to a remote system has been
available within the automotive industry from some
years and is now gaining substantial interest in the
freight transportation business. The OBU gathers
information from sensors with embedded processors
capable of detecting unusual or deviant conditions, and
informs a central control center if a problem is
detected. Sensors also measure the status of a shipment
while on route such as detecting whether the internal
temperature of refrigerated containers is within
acceptable tolerance limits.
3.2. Automatic Freight Identification and
Many assets, including freight containers, swap-
bodies, and transport vehicles, are now being fitted
with transponders to not only identify themselves, but
also to detect shipment contents and maintain real-time
inventories. In the latter case, units are equipped with
RFID readers tuned to detect RFID tags within the
confined range of the container. Some tags, such as the
Intermec Intellitag with an operating range of 4 meters,
are specifically designed for pallet and container
tracking, where tags are attached to every item and
automatically scanned whenever cargo is loaded or
unloaded. The live inventory serves as both, local
information for the driver and as real-time feedback to
the TMS, which uses it for record keeping and as input
to the real-time route planner.
In addition, e-seals, whether electronic or
mechanical, are now often placed on shipments or
structures to detect unauthorized entry and send remote
alerts via the OBU. E-seals on a container door can
also store information about the container, the
declaration of its contents, and its intended route
through the system. They document when the seal was
opened and, in combination with digital certificates
and signatures identify whether the people accessing
the container are authorized to do so.
3.3. GPS for Automatic Vehicle Location
Automatic vehicle location uses GPS signals for
real-time persistent location monitoring of vehicles.
Both human dispatchers and route planners like
LS/ATN then can track vehicles continuously as they
move between pickup and delivery locations. Active
GPS systems allow automatic location identification of
a mobile vehicle – at selected time intervals the mobile
unit sends out its position, as well its speed and other
technical information. Passive GPS uses the OBU to
log location and other GPS information for later
upload. Accuracy can vary, typically between 2 and 20
meters, according to the availability of enhancement
technologies such as the Wide-Area Augmentation
System (WAAS), available in the U.S. The European
Galileo system will augment GPS to provide open-use
accuracies in the region of 4-8 meters within the
European region.
The adoption of GPS is growing quickly as the
technology becomes commoditized, but some
transportation companies remain reliant on legacy
equipment for measuring vehicle location. Some of the
alternatives to GPS in use today include dead-
reckoning, which uses a magnetic compass and wheel
odometers to track distance and direction from a
known starting point, and the LORAN-C (Long Range
Navigational) system which determines a vehicle’s
location using in-vehicle receivers and processors that
measure the angles of synchronized radio pulses
transmitted from at least two towers of predetermined
position. Another system in use by some transportation
companies is cell phone signal triangulation, which
estimates vehicle location by movement between
coverage cells. This only offers accuracy typically in
the region of 50-350 meters, but is a cheap and readily
available means of determining location.
3.4. Mobile Communications
Electronic communication is the key enabler of
pervasive technologies. In transportation the most basic
form is use is the SMS, which is commonly used to
communicate job status such as when a driver has
delivered an order. Technology is already in place to
automatically process SMSs and input the data into the
route planner. Also now in relatively widespread use is
Dedicated Short-Range Communications, DSRC,
operating in the short-range 5.8-5.9 GHz microwave
band for use between vehicles and roadside
transponders. Its primary use in Europe and Japan is
for electronic toll collection. DSRC is also used for
applications such verifying whether a passing vehicle
has a correctly operating OBU.
Currently, the technology with the greatest utility is
Machine-to-Machine [9] (M2M) communication,
which is the collective term for enabling direct
connectivity between machines (e.g., a vehicle’s OBU
and the remote planning engine) using widespread
wireless technologies. Legacy 2G infrastructure is most
commonly used as 3G technologies enter the
mainstream for day-to-day human telecommunications.
M2M is quickly emerging as a principle enabler of
networked embedded intelligence, the cornerstone of
pervasive computing. It can eliminate the barriers of
distance, time and location, and as prices for the use of
2G continue to drop due to continued roll-out of 3G
technologies, many transportation companies are
taking advantage and adopting M2M as their primary
means of electronic communication.
Emerging solutions take M2M to another level by
enabling always-on and highly reliable communication
through automatic selection of connection technology,
e.g., GPRS, EDGE, UMTS, Satellite Services and
WiFi according to availability. The LS/ATN route
optimizer, for example, can be augmented with a
remote connection agent module [10] installed in
vehicles that offers seamless M2M over cellular
technologies, Wireless LAN and even short-range ad-
hoc connections if available. The particular selection of
communication technology can be made either
manually or automatically, depending on several
metrics including location, connection availability,
transmission cost and service type or task. For
example, a fleet operator may prefer the use of satellite
to directly communicate with a driver, but then a
combination of cellular technologies for remote
monitoring, trailer tracking, and diagnostics. Low cost
GPRS might be selected to download position
coordinates from an on-board GPS; whereas, a higher-
bandwidth (and cost) option such as UMTS/WCDMA
might be preferred for an over-the-air update to the
OBU or on-board sensors.
4. Route Optimization with Integrated
Pervasive Technologies
Transportation route optimizers can take advantage
of real-time data sourced from vehicles equipped with
pervasive technologies by incorporating information
relating to vehicle location, state and activity into their
planning processes. The LS/ATN optimizer uses the
following sequence of operations to sense, process and
act on this data, as illustrated in Figure 7.
Figure 7. Information flow from on-board sensors is
processed to produce a re-optimized route that is
issued back to a vehicle as an updated schedule.
Sense – Periodically gather data from on-board
vehicle sensors. Consolidate this data with an OBU and
perform pre-processing if necessary or possible, i.e., to
improve precision and reduce error rate by integrating
across multiple readings. This data is then transmitted
to the remote route planner using M2M technology
over a selected mobile network.
Collect – At the TMS, collect incoming data from
vehicles on the road and shape it using codecs into
forms suitable for the live route optimizer, e.g., by
mapping geographical coordinates onto known
positions. The position information of a single vehicle
is used to immediately adjust dispatching plans in the
case of deviations (described below in more detail). If
the number and density of vehicles in a region is high
enough, this ‘floating vehicle data’ may be integrated
into a map containing real-time traffic flow
information [11]. This data is input to real-time routing
systems to plan the fastest route taking into account
current traffic flow and congestion information. Other
than information sourced from vehicles themselves,
additional external data feeds can help tune the route
optimization yet further. These include, for example,
third-party sources providing traffic congestion
information, location of road works, weather forecasts
and delays at shipping or airfreight hubs.
Simulate – A secondary layer of the optimization
process consists of background simulations where
alternative schedules are proposed, offering projections
of future routes based on real-time data, predicted
events and probabilistic estimates of pickup and
delivery times. Simulations are conducted as separate
optimization processes extrapolating from a live
session, but with the occasional (random or planned)
introduction of hypothetical event data, such as a new
order or a vehicle breakdown. Results can be used
either as rolling input to the scheduling decision
process or to assist with strategic decision making
when determining the impact of, for example,
adjustments in vehicle numbers, staffing levels and
scheduling of freight consolidation at distribution hubs.
Deliberate – Input collected, and optionally
simulated, data into the route optimization process to
produce schedule updates to be transmitted to affected
vehicles. The most common use of this input data by
the planner is for the management of delays. Once the
planner has knowledge of a delivery delay, it checks if
the plan remains valid or if some orders with later
delivery times can now be better transported with other
vehicles. The dispatcher is immediately informed if
this is the case, with the real-time planner generating
various deployment options.
Real-time data also improves optimization if the
system receives a new order. In this case a transport
planner without real-time data feeds can assign the
order only to vehicles that have yet to start a trip or, if
at least pickup or delivery feedback is available after
the next pickup or delivery location. This is because
the planner has no knowledge of where on the road the
vehicle is currently located. With real-time feedback
the exact position is known at all times, implying of
course that re-planning is possible at any time. For
example, a truck has an order to pick up in Zurich and
deliver to Berlin. After the truck has left Zurich, a new
order arrives to be picked up in Stuttgart and also
delivered to Berlin. Without exact knowledge of the
truck’s position at the time the order arrives, it is not
possible to precisely check if the order may be
transported with this truck. A perfect opportunity for
co-loading is missed.
In a similar fashion, the feedback of real-time data
is used to exploit opportunities when the truck is ahead
of schedule. This is usually the case if loading or
unloading takes less than the expected time, or, on
occasion, if the drive time is less than expected. In
such cases, orders that did not originally fit into the
truck’s schedule, and thus had to be transported
separately, may now be transported with this truck.
Again, the faster the planner knows of such situations
the less likely it is that opportunities are missed.
Update – Once new route plans have been devised,
verify them and upload them to vehicles via their M2M
connection. Additional information such as firmware
upgrades can also be transmitted if and when
Act – On board the vehicle, whenever new
scheduling is received, locally stored information is
updated to inform the driver of re-planning. If the
container has an RFID scanner installed, a scan of
current freight will be compared against the new
schedule to verify compliance. If other data, e.g.,
firmware upgrades, are received, they are installed
In particular, during the deliberation phase, route
optimization and the derivation of schedules can
directly use both information relating to vehicles
movements as they proceed through delivery schedules
and feedback from RFID transponders notifying when
orders have been added to or removed. This real-time
component implies that time windows can be more
finely tuned according to current events, resulting in
alternative schedules that can either compensate for
delays or take advantage of time saved. Preliminary
results with a prototype demonstrate that employing
real-time data in the optimization process can further
reduce transportation operating costs by up to 3%
beyond the 5-10% achieved from the standard
optimization process described earlier - depending on
the particular business case, order structure and system
5. Challenges and Conclusions
There remain many scientific and practical
challenges related to the design and use of real-time
dispatching and optimization systems. A selection of
these we consider as relevant to LS/ATN and for
consideration by the community at large are as follows:
A major challenge is the effective handling of inter-
company, inter-region and inter-modal transportation.
Transportation intrinsically involves multiple carriers
operating both within and across sectors (i.e., road, air,
and shipping) and across geographical boundaries.
Each carrier has their own, often proprietary, systems
that do not necessarily integrate easily with one
another. Addressing this integration problem is a
significant engineering issue to be faced as the
technologies addressed in this article come into more
widespread use.
The integration of transportation planners into
supply chain and production systems is also important.
As previously mentioned, freight is now often
delivered directly to manufacturing plants without
passing through transitional storage. Integration of
these systems thus becomes a priority when shaping
dynamic supply chains, and supply networks.
OBUs in use today typically consist of a simple
processor, memory and communication interfaces.
Installed software is often designed solely for reading
data from sensors and transmitting it to the TMS. One
method of improving on this design is the integration
of an autonomous software controller into the OBU to
assist with the manipulation and coordination of
collected on-board data. Example uses include
assisting in the selection of M2M connection type in a
multi-provider environment according to the type and
volume of data to be transmitted and caching data
locally if connections are temporarily unavailable. The
controller can be further extended with a software
agent that extends the distributed intelligence offered
by the TMS optimizer. This agent essentially acts as a
remote extension of the optimization platform allowing
the agent to act as a proxy representative of the vehicle
itself within the context of route scheduling. Vehicles
can thus become active participants in the planning
process, forming a network overlay of communicating
data processors.
Further research is required on so-called ‘smart’
freight containers capable of announcing their presence
and even negotiating with external devices. For
example, a simple OBU fixed to a container will allow
it to communicate with vehicles, customs checks and
equipment at freight consolidation centers. Many major
transportation companies use such centers, distributed
at strategic locations, with the primary goal of
consolidating freight onto as few vehicles as possible
to maximize use of available capacity. With the
installation of RFID readers, incoming freight with
RFID tags can be traced as it moves through a facility,
providing TMS optimizers with complete coverage of
freight location throughout its entire lifecycle within
the business chain.
In addition, external factors also favor early
adoption of pervasive technologies, such as the
ongoing escalation of fuel prices and constant increases
in demand for fast, high-volume freight shipping. This
is recognized in the European Union white paper,
“European Transport Policy for 2010” [12] which
discusses the use of intelligent information services
integrated with route planning systems and mobile
communications to provide real-time, intelligent end-
to-end freight and vehicle tracking and tracing.
There can be little doubt that the adoption of
intelligent transportation planners capable of using
real-time data sourced from pervasive technologies,
such as those discussed in this article is a major
objective of many freight transportation operators both
in Europe and other areas of the world. With these
techniques now widely recognized as an important
means of reducing operating costs, many companies
are already well advanced on the path to adoption.
6. References
[1] TNT Logistics, “Transport Management Services”, Grids:
The TNT Logistics Benelux magazine for logistics & supply
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[2] A. Gonzalez, “Trends and Predictions in the
Transportation Management Systems Market”, ARC
Advisory Group Report, 2006.
[3] M. Pěchouček, S. Thompson, J. Baxter, G. Horn, K. Kok,
C. Warmer, R. Kamphuis, V. Marík, P. Vrba, K. Hall, F.
Maturana, K. Dorer, M. Calisti, “Agents in Industry: The
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[4] K. Seongmoon, M.E. Lewis, C.C. White, “Optimal
vehicle routing with real-time traffic information”, IEEE
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[5] M.W.P. Savelsbergh and M. Sol, “The General Pickup
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[6] N. Neagu, K. Dorer, D. Greenwood and M. Calisti,
“LS/ATN: Reporting on a Successful Agent-Based Solution
for Transport Logistcs Optimization”, Proc. 2006 IEEE
Workshop on Distributed Intelligent Systems, Prague, 2006.
[7] “General Datacom: Transportation and Wireless
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[8] X. Pouteau et al., “Robust Spoken Dialogue Management
for Driver Information Systems”, Proc. Eurospeech ’97,
Rhodes, Greece, 1997, pp.2207-2210.
[9] G. Lawton, “Machine-to-Machine technology gears up
for growth”, IEEE Computer, vol. 37, no. 9, 2004, pp. 12-15.
[10] D. Greenwood and M. Calisti, “The Living Systems
Connection Agent: Seamless Mobility at Work, Proc.
Communication in Distributed Systems (KiVS), Bern,
Switzerland, 2007, pp. 13-14.
[11] A. Gühnemann, R. Schäfer, K. Thiessenhusen, P.
Wagner, “New Approaches to Traffic Monitoring and
Management by Floating Car Data”, WCTRS [eds.]:
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[12] The European Commission, “European Commission
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[13] J.-J. Jaw, A. R. Odoni, H. N. Psaraftis, and N. Wilson.
“A heuristic algorithm for the multivehicle advance request
dial-a-ride problem with time windows”, Transportation
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