DESIGN AND EVALUATION OF A REAL-TIME FLEET MANAGEMENT SYSTEM

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 2, Issue 3 (May-June 2014), PP. 95-99
95 | P a g e
DESIGN AND EVALUATION OF A REAL-TIME
FLEET MANAGEMENT SYSTEM
Sanjay Singh1, Dr. Alka Singh2
1Research Scholar
Shri Venkateshwara University
2Asst. Professor,
Shri Ram Swaroop University, Lucknow
Abstract— A supply chain consists of all parties involved
directly or indirectly, in fulfilling a customer request. The supply
chain includes not only the manufacturers and suppliers, but also
transporters, workhouse, retailers and even customers
themselves. Within each organization, such as a manufactures,
the supply chain includes all functions involved in receiving and
filling a customer request. These functions include, but are not
limited to, new product development, marketing operations,
distributions, finance, and customer service. Supply chain
management (SCM) is the management of an interconnected or
interlinked between network, channel and node businesses
involved in the provision of product and service packages
required by the end customers in a supply chain. Supply chain
management spans the movement and storage of raw materials,
work-in-process inventory, and finished goods from point of
origin to point of consumption. It is also defined as the "design,
planning, execution, control, and monitoring of supply chain
activities with the objective of creating net value, building a
competitive infrastructure, leveraging worldwide logistics,
synchronizing supply with demand and measuring performance
globally.
Key words— real – Time traffic information; time dependent
travel time, dynamic vehicle routing.
I. INTRODUCTION
Fleet management is the management of a company's
transportation fleet. Fleet management includes commercial
motor vehicles such as cars, ships, vans and trucks, as well as
rail cars. Fleet (vehicle) management can include a range of
functions, such as vehicle financing, vehicle maintenance,
vehicle telemetric (tracking and diagnostics), driver
management, speed management, fuel management and health
and safety management. Fleet Management is a function which
allows companies which rely on transportation in business to
remove or minimize the risks associated with vehicle
investment, improving efficiency, productivity and reducing
their overall transportation and staff costs, providing 100%
compliance with government legislation (duty of care) and
many more. These functions can be dealt with by either an in-
house fleet-management department or an outsourced fleet-
management provider. According to my research from the
independent analyst firm M/s Birla Corporation Ltd, the
number of fleet management units deployed in commercial
fleets in India will grow from 1.5 million units in 2009 to 4
million in 2014. Even though the overall penetration level is
just a few percent, some segments such as road transport will
attain adoption rates above 30 percent.
The most basic function in all fleet management systems, is the
vehicle tracking component. This component is usually GPS-
based, but sometimes it can be based on GLONASS or a
cellular triangulation platform. Once vehicle location, direction
and speed are determined from the GPS components, additional
tracking capabilities transmit this information to a fleet
management software application. Methods for data
transmission include both terrestrial and satellite. Satellite
tracking communications, while more expensive, are critical if
vehicle tracking is to work in remote environments without
interruption. Users can see actual, real-time locations of their
fleet on a map. This is often used to quickly respond on events
in the field.
II. REAL TIME FLEET MANAGEMENT
Fleet Controller is Paragon's fleet management software
solution that enables real-time vehicle activity to be tracked
automatically against the planned routes and schedules. This
gives transport managers real-time visibility of how the day's
plan is progressing and provides an accurate picture of
transport and service performance.
Use Paragon Fleet Controller to:
 Compare planned versus actual performance reports at
the end of each day, highlighting any significant
deviations, changes or anomalies for continuous
performance improvement.
 Manage customer's delivery expectations in real time
and reduce service failures.
 Automatically pre-advise customers of an updated
delivery time when the vehicle is an agreed number of
minutes away.
 Refine scheduling parameters to tighten up planning
for greater consistency and improved fleet
management efficiency.
Fleet Controller is available with a standard certified
interface for connection with a range of leading tracking
systems (normally requiring just an internet connection).
Companies can significantly improve their customer service
achievement, respond efficiently to problems or delays that
arise, ensure delivery schedules are legal and achievable, and
unearth hidden inefficiencies for continuous performance
improvement.
This fleet management software can be used as a standalone
transport and customer service management system, or in
conjunction with any of the Paragon planning systems.

96 | P a g e
III. DIGNITY OF PROPOSED REAL-TIME FLEET MANAGEMENT
SYSTEM
Simple dignity. If this sounds all soft and squishy. A chance
meeting outside my company where the truck parking is
available Show last month provides a clue on how to avert a
pending disaster.
I had moderated the Fleet Forum panel discussion on
“Managing our Fleet in the Real World” with transporters 2013
Truck Fleet Innovators: Aman Road Lines, Raebareli; Bansal
Shifters; Rajdhani Transport Ltd.; and Prakash Road Lines Ltd.
These guys are no slouches in the fleet management world, but
one reason for the shortage of qualified drivers was absent from
the discussion at the forum.
The fellow I was talking with my company a software
engineer with a stake in trucking's success. He had attended the
Fleet Forum and indicated he found the discussion
enlightening. He told me he was surprised that none of the
panelists had mentioned the very basic requirement for
fulfillment in life: the need to be treated with respect and
dignity.
He suggested that drivers are not treated with a great deal of
respect overall, and he was quite sure that anyone coming from
almost any other trade or profession would find the drivers’
world nearly intolerable in a very short time.
He mentioned specifically the insanely narrow delivery
windows drivers face despite weather and traffic conditions, the
constant hounding by Traffic inspectors, and being told — in
words and deeds — that their time is worth nothing unless they
are running down the road under a full head of steam.
This man made it quite clear that he'd need to be nearly
destitute before he'd consider driving as way of making a
living. He said there's no dignity in driving a truck. And he's
right.
To those of us steeped in the culture, irritants like not being
paid for loading and unloading, vehicle inspections and the like
are standard operating procedure. To someone outside trucking,
that would be abhorrent.
Another example: To run 2,500 miles in a week but to be
paid only 2,450 because that's how far the computer says it is
between two points — despite factors like construction-related
detours — is beyond disrespectful.
We accept it because it has always been that way, or worse.
But outsiders — those we are looking to recruit to fill truck
seats — expect to be paid for the work they do, even if (and
perhaps especially if) it's outside the normal call of duty.
The way drivers are treated by law enforcement is another
cause for concern.
On a whim, any police officer can pull a truck over and
strip a driver of half a week's pay with just a couple of
citations, warranted or not. What's a driver to do, travel a
thousand miles and miss a week's work to fight a Rs.- 500
ticket? The cops know the driver is not coming back to fight
the ticket, so it's easy money.
That sort of treatment is dehumanizing, but we rarely hear
industry leaders decrying that kind of behavior. Driving
certainly isn't a glamorous job, but drivers don't need to be
treated like criminals.
Actually, criminals have more rights than drivers in some
respects. They are at least assumed innocent until proven
guilty. Drivers give up a lot in the name of safety.
It's clear that the crowds of people who are not becoming
truck drivers are not prepared to sacrifice their dignity to earn
just a living wage.
IV. PROPOSED FLEET MANAGEMENT SYSTEM
Current fleet management systems are used mainly for
monitoring purposes and are unable to handle in a systemic
fashion various unexpected events that occur during delivery
execution. Current research in the area of dynamic incident
handling focuses mainly on the creation and testing of efficient
algorithms that are able to handle dynamic events usually in an
optimal or near optimal manner. However, such algorithms
give a partial solution to the problem as in order to be effective
they must be implemented in a fleet management system. The
latter is able to provide real-time information about traffic or
vehicle’s status which acts as input data to the rerouting
algorithms. There is thus a need for a holistic approach in the
problem of dynamic incident handling, through the design of a
real-time fleet management system that would be able not only
to monitor certain vehicles but also detect possible deviations
from the initial plan, and suggest new routes by using well
known rerouting algorithms from the literature.
,One of the basic prerequisites for detection of possible
deviation from the initial plan is to be able to predict the arrival
time in the remaining customers. This can be achieved by using
a travel prediction method during delivery execution. We
propose a method for travel time estimation which is based on
historical data from previous delivery schedules. Such methods
can give very accurate results when traffic patterns at the
moment of travel time prediction are similar to the historical
ones retrieved from the database. However, as in urban settings
there are cases where travelling times vary over time and
depend on when a vehicle is traversing a particular segment we
propose a second travel prediction method that uses real-time
data to compute the network travel time in a dynamic manner.
As the vehicle is travelling towards its destination, travel time
is predicted sequentially by summing the travel time derived
from speed measurements at different sections of the road. The
system has an intelligent mechanism that monitors the traffic
situation in consecutive time steps and decides which method
gives the most accurate results. It is worthwhile to mentioning
that both methods have been evaluated by using an innovative
testing framework that included the design of a series of
experiments which demonstrate how certain variables affect the
prediction accuracy of each method.
Even if a real-time fleet management system uses accurate
travel time prediction techniques, there should be a mechanism
that would be able to decide whether a detected deviation is
significant or not. For that reason we propose and evaluate two
methods that can be used to assure that vehicle rerouting will
be recommended only when the deviation from the initial plan
is significant. In other words, these methods ensure that a
vehicle will not be rerouted when it is not needed (i.e. when
there is not a significant time violation).

97 | P a g e
V. SIMULATED STUDY OF PROPOSED SYSTEM
The system is also tested in real-life scenarios in order to
confirm the simulation results. The system is implemented in
two delivery companies in order to evaluate its performance in
the field. Again a series of tests are used that are generated by
the DoE method.
This dissertation discusses the design, testing and
evaluation of a real-time fleet management system for dynamic
incident handling in urban freight distributions. The main aim
is to present the theoretical background for understanding the
area of dynamic fleet management and at the same time to
provide all the necessary details for designing and evaluating a
novel system for tackling unexpected events that occur during
freight delivery execution.
The urban environment was presented and emphasis was
given to analyzing the unforeseen events that characterize the
dynamism of urban freight deliveries. Current methods and
techniques for incident handling were presented and the need
for a real-time fleet management system for dynamic incident
handling was identified. Then, the expected contribution of this
thesis was presented and the research methodology that has
been followed for the design, testing and evaluation of the
system was analyzed.
This feature provides detailed information relating to
vehicle and fleet costs. It assists the logistics manager by
providing analysis and information concerning individual
vehicle and overall fleet profitability. Features include vehicle
and driver cost analysis as well as overall fleet costs.
Travel time can be defined as the total time required for a
vehicle to travel from one point to another over a specified
route under prevailing conditions. Its calculation depends on
vehicle speed, traffic flow and occupancy, which are highly
sensitive to weather conditions and traffic incidents.
Nonetheless, daily, weekly and seasonal patterns can be still
observed at large scale. For instance, daily patterns distinguish
rush hour and late night traffic, weekly patterns distinguish
weekday and weekend traffic, while seasonal patterns
distinguish winter and summer traffic. It has been increasingly
recognized that for many transportation applications, estimates
of the mean and variance of travel times affect the accuracy of
prediction significantly.
Travel time data can be obtained through various
surveillance devices, such as loop detectors, microwave
detectors, and radars, though it is not realistic to have the road
network completely covered by detectors. With the
development of mobile and positioning technologies, the data
can be more reliably collected and transmitted. More
importantly, these devices can be set up on vehicles with
minimal hardware using non-sophisticated communication and
installation. However, travel time estimation is not so
straightforward because it depends not only from the
surveillance devices, but also on the prediction technique that is
being used for data processing.
To handle the complex nature of operations, the logistics
operation is being handled at Birla Cement through a multi-
tiered structure which involves logistics teams at Plant, Region
and Zonal levels. Beside this, there is a central logistics team
who set the overall policy guidelines, monitor logistics
performance and ensure segmental priorities as well as service
requirements are met.
Logistics processes are empowered by best in class SCM
processes using technology as the enabler with focus on:
Network Optimization Web Based Order Management system
with real time visibility of order status Customer Service level
measurement on real time basis Automation at secondary
service points like Railheads and Godowns.
The above charts of lead time taken by the truck. This
calculation taken from M/s Birla Corporation Ltd. engaged near
about 4000 trucks from April 13 to Sept’2013 for the purpose
of transportation of cement. This is the best system to calculate
the lead time of trucks & this calculation system was also
adopted by the company M/s Birla Corporation Ltd. after our
variable suggestion and correction taken by the company.
A. Result Analysis & Evaluation
Fig.1 Trucks of M/S Birla Corporation Ltd
VI. CONCLUSION
Information from telemetric logger recordings can provide
the input data for an analysis of driver/vehicle performance. A
number of systems are available that can read these charts and
produce a posteriori reports on rest time, driving time and break
time, as well as details of legal infringements.
This includes the monitoring of the service life of vehicles
in a fleet and the scheduling of routine and non-routine
maintenance and repairs. Typical features include service
history, maintenance schedule reports and workshop cost
analysis. In this calculation system we had taken all timing
including pick traffic time, normal traffic time, vehicle
accidental time, loading/unloading time & order availability
time then finally lead time & freight qty. generated. In this
calculation used Global Positioning System (GPS) through
internet or installed to all used trucks.

98 | P a g e
Name Of the
District
Distance Despatches Freight
Avg.
Unloadi
ng Time
in
Minute
Avg.
Speed of
Trucks
(Minute)
Lead
Time
(Minute)
Lead
Time
in
Hrs.
No. of
Trucks
Used
Avg.Km. Ts. Per Ts.
0-150 KM.
Raebareli 28 23097 202 180 0.67 263.96 4 1155
Lucknow 99 56872 459 180 0.67 476.15 8 2844
Sultanpur 118 24780 471 180 0.67 532.98 9 1239
Fatehpur 116 670 489 180 0.67 526.45 9 34
Pratapgarh 129 11146 458 180 0.67 565.37 9 557
Barabanki 118 27555 432 180 0.67 532.30 9 1378
Kanpur Nagar 112 1136 386 180 0.67 512.94 9 57
Unnao 130 13177 447 180 0.67 566.79 9 659
100 158432 417 180 0.67 478.16 8 7922
151-300 KM.
Kanpur Dehat 158 632 497 180 0.67 652.49 11 32
Faizabad 155 2181 450 180 0.67 642.30 11 109
Sitapur 178 7778 613 180 0.67 712.67 12 389
Ambedkar Nagar 220 9516 626 180 0.67 836.01 14 476
Hardoi 195 8535 647 180 0.67 762.39 13 427
Bahraich 233 27162 689 180 0.67 876.44 15 1358
Gonda 242 11894 690 180 0.67 901.52 15 595
Lakhimpur 244 6147 751 180 0.67 908.42 15 307
Sidhharatnagar 246 1006 685 180 0.67 913.61 15 50
Gorakhpur 0 0 0 180 0.67 180.00 3 0
Kannuj 257 750 637 180 0.67 945.97 16 38
Shahjahanpur 284 266 829 180 0.67 1028.10 17 13
Mainpuri 290 90 684 180 0.67 1046.27 17 5
Basti/Sant Kabir
N. 224 525 604
180 0.67 847.85 14
26
222 76482 665 180 0.67 841.67 14 3824
301-450 KM.
Farrukhabad 286 1416 681 180 0.67 1033.37 17 71
Mau 0 0 0 180 0.67 180.00 3 0
Etawah 309 148 730 180 0.67 1101.61 18 7
Deoria 0 0 0 180 0.67 180.00 3 0
Padrauna 0 0 0 180 0.67 180.00 3 0
Bareilly 320 2886 796 180 0.67 1135.22 19 144
Pilibhit 344 666 860 180 0.67 1207.46 20 33
Rampur 0 0 0 180 0.67 180.00 3 0
Badaun 395 20 954 180 0.67 1359.10 23 1
314 5136 771 180 0.67 1116.41 19 257
451 & Above 0 0.67 0.00 0 0
Moradabad 0 0 0 0 0.67 0.00 0 0
J.P.Nagar 0 0 0 0 0.67 0.00 0 0
Bijnor 0 0 0 0 0.67 0.00 0 0
Grand Total 143 240049 504 180 0.67 1198 30 4001

99 | P a g e
REFERENCES
[1] Abdulhai, B., Porwal, H. and Recker, W. (2003) “Short-term
Freeway Traffic Flow Prediction Using Genetically-optimized
Time-delay-based Neural Networks” UCB, UCB-ITS-PWP–99-
1 (Berkeley, CA: Institute of Transportation Studies, University
of California, Berkeley)
[2] Aronson L. D., and Van der Krogt, R. P. J. (2003), Incident
Management in Transport Planning, TRAIL Research School,
Faculty of Information technology and Systems, Delft
University of Technology, Nederland, White paper.
[3] Campbell, A., Clarke, L., Kleywergt, A., Savelsbergh, M. (1998)
“The inventory routing problem”, In Laporte G., Crainic, T.G.
(Eds.) Fleet Management & Logistics,Kluwer, Boston, US
[4] Chien, S. I. J. and Kuchipudi, C. M. (2005) “Dynamic travel
time prediction with real- time and historical data”, in:
Proceedings of the Transportation Research Board 81st Annual
Meeting, Washington, DC.
[5] Fleischmann, B., Gietz, M., Grutzmann, S. (2006), “Time-
varying Travel Times in Vehicle Routing”, Transportation
Science 38 (2).
[6] Leveine, S.Z., McCasland, W.R., Smalley, D.G., (1999)
“Development of a Freeway Traffic Management Project
through a Public-Partnership” in Transportation Research
Record 1394, Transportation Research Board, National Research
Council, Washington.
[7] Psaraftis, H.N. (2007), “Dynamic vehicle routing problem”, In
Golden, B.L., Assad, A.A (Eds), Vehicle Routing: Methods and
Studies, Elsevier Science, Amsterdam.
[8] Slater, A. (2009) “Specification for a dynamic vehicle routing
and scheduling system”, International Journal of Transportation
Management 1.
[9] Solomon., M.M. (2010) "Algorithms for the Vehicle Routing
Problem with Time Windows". Transportation Science, 29(2).
[10] Regan, A.C., Herrmann, J., Lu, X. (2002) “The relative
performance of heuristics for the dynamic travelling salesman
problem”, Proceedings of the 81st Annual Meeting of the
Transportation Research Board, Washigton, DC
[11] Reimann, M., Doerner, K., Hartl, R.F. (2003) D-Ants: Savings
Based Ants divide and conquer the vehicle routing problem,
Computers & Operations Research.
[12] Rhalibi, A. Kelleher, G. (2003) “An Approach to Dynamic
Vehicle Routing Rescheduling and Disruption Metrics”,
Proceedings of Systems Man & Cybernetics (SMCC),
Washington D.C.
[13] Robinson, J.R., Ewald, R.C., Gravely, T.B., Carter, E. (1993)
“CAPITAL IVHS Operational Test” in the Proceedings of the
63rd Annual ITE Meeting, Institute of Transportation Engineers,
Washington D.C.
[14] Rosenhead, J. (Ed.) (1989), “Rational Analysis for a Problematic
World: Problem Structuring Methods for Complexity,
Uncertainty and Conflict”, John Wiley & Sons, New York, NY.
[15] Ruiz, R. Maroto, C., Alcaraz, J. (2004) “A decision support
system for a vehicle routing problem”, European Journal of
Operational Research
[16] Rushton A., Oxley, J., Croucher P. (2000), “The Handbook of
Logistics and Distribution Management”, 2nd Edition, © The
Institute of Logistics and Transport, UK.
[17] Savelsbergh, M.W.P., Sol, M. (1991) “Drive: Dynamic routing
of independent vehicles”, Operations Research.
[18] Solomon, M.M. (1995) "Algorithms for the Vehicle Routing
Problem with Time Windows". Transportation Science.
[19] Ulbricht, C. (1994) “Multi-recurrent networks for traffic
forecasting”, Proceedings of the Twelfth National Conference on
Artificial Intelligence, AAAI
[20] Van Arem, B., Van der Vlist, M. J. M., Muste, M. and Smulders,
S. A. (1997) “Travel time estimation in the Gerdien project”,
International Journal of Forecasting
[21] Yin, H., Wong, S. C. and Xu, J. (2002)” Urban traffic flow
prediction using fuzzy- neural approach”, Transportation
Research Part C.

DESIGN AND EVALUATION OF A REAL-TIME FLEET MANAGEMENT SYSTEM

More Related Content

What's hot (15)

Viewers also liked (20)

Similar to DESIGN AND EVALUATION OF A REAL-TIME FLEET MANAGEMENT SYSTEM (20)

More from International Journal of Technical Research & Application (20)

Recently uploaded (20)

DESIGN AND EVALUATION OF A REAL-TIME FLEET MANAGEMENT SYSTEM