J. Service Science & Management, 2010, 3, 373-382
doi:10.4236/jssm.2010.33044 Published Online September 2010 (http://www.SciRP.org/journal/jssm)
373
Intercity Bus Scheduling for the Saudi Public
Transport Company to Maximize Profit and
Yield Additional Revenue
Mohamad K. Hasan1*, Ahmad A. Al Hammad2
1Department of Quantitative Methods and Information Systems College of Business Administration, Kuwait University, Safat, Ku-
wait; 2Quantitative Analysis Department College of Business Administration King Saud University, Riyadh, Saudi Arabia.
Received April 22nd, 2010; revised May 29th, 2010; accepted July 8th, 2010.
ABSTRACT
The Saudi Public Transport Company (SAPTCO) intercity bus schedule comprises a list of 382 major trips per day to
over 250 cities and villages with 338 buses. SAPTCO operates Mercedes 404 SHD and Mercedes 404 RI-IL fleet types
for the intercity trip. The fleet assignment model developed by American Airlines was adapted and applied to a sample
of the intercity bus schedule. The results showed a substantial saving of 29% in the total number of needed buses. This
encourages the decision makers at SAPTCO to use only Mercedes 404 SHD fleet type. Hence, the fleet assignment
model was modified to incorporate only one fleet type and applied to the sample example. Due to the increase in the
problem size, the model was decomposed by stations. Finally, the modified decomposed model was applied to the whole
schedule. The model results showed a saving of 16.5% in the total number of needed buses of Mercedes 404 SHD. A
sensitivity analysis was carried out and showed that the predefined minimum connection time is critical for model effi-
ciency. A modification to the connection time for 11 stations showed a saving of 14 more buses. Considering our rec-
ommendation of performing a field study of the trip connection time for every station, the expected saving of the total
number of needed buses will be about 27.4% (90 buses). This will yield a net saving of 16.44 million Saudi Riyals (USD
4.4 million) per year for SAPTCO in addition to hiring new employees. The revenue analysis shows that these 90 sur-
plus buses will yield about USD 20,744,000 additional revenue yearly.
Keywords: Fleet Assignment Model, Bus Scheduling, Integer Programming, Transportation Service, Revenue
Management
1. Introduction
1.1. Problem Definition
SAPTCO has 382 intercity major trip departures every
day. This excludes the local services and the international
services to Egypt, Kuwait, Jordan, Sudan, Qatar, Bahrain,
Syria, Turkey, and The United Arab Emirates. This Inter-
city schedule is produced by considering an existing set of
trips, traffic revenue forecasts, available resources such as
buses, drivers’ base, maintenance shop base, and associ-
ated operating cost. SAPTCO is applying an assignment
system such that the buses and drivers are assigned to 14
main stations, i.e., each station has its own bus fleet and
drivers. According to that system’s work regulations, the
drivers are assigned to the trip schedule, then, a bus is
assigned to one or two scheduled drivers, depending on
the length of the trip to operate his (their) scheduled trip.
The work regulations require that:
a) Each driver takes a minimum number of hours off
work before he takes another trip which may be to another
station or to his original (base) station.
b) Each driver has to take one day off work per week.
According to a) of work regulations, during driver’s
rest time the bus which is assigned to him cannot be
assigned to another driver, the bus is idle at this rest time
which can sometimes be more than 12 hours, whereas, b)
means that the bus is idle for a whole day during its
driver’s rest. Since the trips are scheduled for all week
days, an additional number of buses are required to
cover this rest day for all drivers. These additional buses
are estimated to be 16.6% of the daily used scheduled
buses.

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 374
At SAPTCO, two points of view can be identified: one
is expressed by the maintenance department who wants to
retain this existing assignment system. The other point of
view is expressed by the operations department who seeks
“better” assignment system. This research paper proposes
a new assignment system which takes into account maxi-
mizing the utilization of any bus in the fleet. This means
that it should take a few hours (three are proposed for the
most stations) after the bus’s arrival at any station to be
prepared (e.g., maintenance checking, bus cleaning... etc.)
for operating any other scheduled trip by any scheduled
driver for this trip. In contrast, the proposed assignment
system first assigns buses to the trips schedule, and then
assigns drivers to those scheduled buses.
1.2. Literature Review
The fleet assignment problem for airline industries ad-
dresses the question of how to best assign aircraft fleet
types to an airline’s schedule of flight legs. A flight leg is
defined as a journey consisting of a single take off and
landing, and thus constitutes the smallest unit of opera-
tion that can be assigned an aircraft. A flight schedule is a
set of light legs with specified departure and arrival air-
ports and times (arrival times can be fleet specific). A
fleet assignment is a function that assigns a fleet type to
each flight leg [1].
The scale and complexity of fleet assignment problems
and their large cost implications have motivated the de-
velopment of optimization-based methods to solve them.
Abara [2] presents a formulation for a general flight Net-
work based on a partial enumeration of “feasible turns”,
that is, connection opportunities between pairs of fl ight
legs. The model was formulated and solved the fleet
assignment problem as an integer linear programming
model, permitting assignment of two or more fleets to a
flight schedule simultaneously. The objective function of
the model can take a variety of forms including profit
maximization, cost minimization, and the optimal utili-
zation of a particular fleet type. Several departments at
American Airlines use the model to assist in fleet plan-
ning and schedule development.
Subramanian et al. [3] developed a model for Delta
Airlines that assigns fleet types, not individual aircraft
tail numbers, to the flight legs. They showed that actual
aircraft are routed after the model is solved to ensure
that the solution is operational. Because of the hub-
and-spoke nature of operations and large fleet sizes, it is
always possible to obtain a feasible tail routing from the
assignment recommended by the model.
Kontogiorgis and Acharya [4] developed schedule plan-
ners for US Airway that balanced between meeting week-
end passenger demand, which is different from weekday
demand, and also minimize the costs of realigning airport
facilities and personnel that we would incur by changing
fight patterns too much. They built a specialized fleet-
assignment model and integrated it into a graphical en-
vironment for schedule development. The US Airway’s
planners used the system to create safe, profitable, and
robust flight plans.
Rexing et al. [5] developed a generalized fleet assign-
ment model for simultaneously assigning aircraft types to
flights and scheduling flight departures. Their model, a
simple variant of basic fleet assignment models, assigns a
time window to each flight and then discretizes each
window, allowing flight departure times to be optimized.
Because problem size can become formidable, much
larger than basic fleet assignment models, they devel-
oped two algorithmic approaches for solving the model.
Ahuja et al. [6] developed a new approach that is ba-
sed on generalizing the swap-based neighborhood search
approach of Talluri [7] for FAM, which proceeds by
swapping the fleet assignment of two flight paths flown
by two different plane types that originate and terminate
at the same stations and the same times. An important
feature of their approach is that the size of our neighbor-
hood is very large; hence the suggested algorithm is in
the category of very large-scale neighborhood search al-
gorithms.
Sherali and Zhu [8] proposed a two-stage stochastic
mixed-integer programming approach in which the first
stage makes only higher-level family-assignment deci-
sions, while the second stage performs subsequent family
based type-level assignments according to forecasted
market demand realizations. By considering demand un-
certainty up-front at the initial fleeting stage, they in-
jected additional flexibility in the process that offers
more judicious opportunities for later revisions. They
conducted a polyhedral analysis of the proposed model
and developed suitable solution approaches. Their results
of some numerical experiments were presented to exhibit
the efficacy of using the stochastic model as opposed to
the traditional deterministic model that considers only
expected demand, and to demonstrate the efficiency of
the proposed algorithms as compared with solving the
model using its deterministic equivalent.
Jacobs et al. [9] presented a new methodology for in-
corporating origin and destination (O&D) network ef-
fects into the fleet assignment process. The methodology
used a decomposition strategy to combine a modified
version of a leg-based fleet assignment model (Leg-FAM)
with the network flow aspects of probabilistic O&D yield
management. By decomposing the problem, the nonlin-
ear aspects of the O&D market effects and passenger
flow were isolated in O&D yield management and in-

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 375
corporated in FAM using linear approximations to the
total network revenue function.
Barnhart et al. [1] presented a subnetwork fleet assign-
ment model that employs composite decision variables
representing the simultaneous assignment of fleet types to
subnetworks of one or more flight legs. Their formulation
is motivated by the need to better model the revenue side
of the objective function. They presented a solution me-
thod designed to balance revenue approximation and
model tractability. Computational results suggested that
the approach yields profit improvements over comparable
models and that it is computationally tractable for prob-
lems of practical size.
In addition to the above literature reviews, many local
public transport studies that were done for SAPTACO
were reviewed.
The main objective of this paper is to evaluate the two
points of view of the maintenance department and the
operations department through developing a new fleet
assignment model (new bus schedule).
In next section, the fleet assignment problem (the
proposed assignment system) is formulated and solved as
an Integer Linear Programming (ILP) problem. This was
done by adapting the fleet assignment model developed at
American Airlines [2]. Section 3 shows the result of the
assignment model application on a sample example and
the whole schedule. It also shows model efficiency, cost
analysis, sensitivity analysis and revenue management
that are conducted for both existing and proposed as-
signment systems. Section 4 summarizes and identifies
the main findings and conclusions. Finally, Section 5
gives some directions for futher reaearch.
2. The Assignment Model
The existing assignment system, first, assigns the drivers
to the service schedule, then assigns a bus to one or two
scheduled drivers to operate his (their) scheduled trip in
this way the bus and the driver is one unit that cannot be
separated even at the driver’s rest time. Therefore, a
proposed assignment system should take into account
splitting this unit to maximize the utilization of any bus in
the fleet. In another words, the proposed system should
first assign the buses to the service schedule, then assign
drivers to those scheduled buses. This means that the bus
can be used by more than one or two scheduled drivers
during one day cycle. This can be done by taking into
account after the bus finished its scheduled trip to any
station , it should take a few hours (three are proposed by
maintenance department for most stations) to be prepared
(e.g., normal maintenance checking, bus cleaning,...etc.)
to operate any other scheduled trip by any scheduled
driver for this trip. In case of major maintenance repair
that takes more than three hours, the bus should be re-
placed by another unscheduled bus.
To design this proposed assignment system, the fleet
assignment model developed at American Airlines was
adapted. The goal of our fleet assignment model is to
assign as many trip segments as possible in the SAPTCO’s
intercity bus schedule to one or more bus fleet types.
(SAPTCO operates Mercedes 404 SHD and Mercedes
404 RHL fleet types for the Intercity trip) while optimiz-
ing some objective (e.g., maximize the utilization of
Mercedes 404 SHD fleet type, minimize the total number
of needed buses, minimize the cost of imbalance schedule)
and meeting sets of constraints (e.g., trip coverage, con-
tinuity of equipment, schedule balance, and bus count).
The model uses integer linear programming to solve the
fleet assignment problem. Given a service schedule, with
departure and arrival times indicated, it determines which
bus trip should be assigned to which bus types to optimize
the objective function.
2.1. Model Formulation
2.1.1. Constraints
1) Trip coverage:
After many interviews with the decision makers of
maintenance department of SAPTCO, it was determined
that a minimum of three hours time will be enough for any
arriving trip at a specific station to finish normal bus
maintenance checking and bus cleaning, so that this trip
(bus) can be connected with any departing trip from the
same station whose departure time permits this minimum
three hours for connection. We will refer to Trip-to-Trip
by turns. Typically, an arriving trip can turn to more than
one departing trip. Figure 1 shows four arriving trips to
the Riyadh station (Trips: 1318, 763, 765 and 1931) and
five departing trips (Trips: 768, 772, 1313, 1317 and 1948)
from the same station. Allowing a minimum connection
time of three hours, 22 turn variables per bus type are
possible as follows:
0000-768, 0000-772, 0000-1313, 0000-1317, 0000-
1948, 1318-772, 1318-1313, 1318-1317, 1318-1948, 763-
772, 763-1313, 763-1317, 763-1948, 765-1313, 765-1317,
765-1948, 1931-1317, 1931-1948, 1318-0000, 763-0000,
765-000, 1931-0000, where the turn 1318-0000 represents
a termination trip in Riyadh, i.e.. the bus that operated trip
1318 should be overnighting in Riyadh and cannot be
connected to any other departing trip on the same day.
While, the turn 0000-768 represents an origination trip
from Riyadh, i.e. , the bus that will operate trip 768 is
already in the Riyadh station from last night and not ar-
riving from any other station on the same day.
Now, we can define the decision variable
,,
ijk
X to
represent a feasible turn where the arriving trip
i
turns to
the departing trip
j
on bus type
k
. If
0
i
=
, then
j
is
a sequence origination and, if
0
j
=
, then
i
is a sequence

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 376
Arriving Trip Arriving Departing Trip Departing
From No Time Time No To
Jeddah 1318 11:00 12:30 768 Dammam
Dammam 763 12:30 15:30 772 Dammam
Dammam 765 14:00 18:00 1313 Jeddah
Abha 1931 19:00 22:00 1317 Jeddah
Arrivals 23:00 1948 Abha
Departures
Figure 1. Four arrivals and five departures trips (Riyadh Station).
termination where sequence represents the daily routing
for a bus. If
1
k
=
represents Mercedes 404 SHD bus
type and
2
k
=
represents Mercedes 404 RHL bus type,
then 1318.772.1
1
X
=
means that the trip 1318 arriving to
Riyadh from Jeddah can be turned (connected) to trip 772
departing from Riyadh to Dammam using Mercedes 404
SHD bus type. While 1318.772.1
0
X
=
means that the trip
1318 cannot be connected to trip 772 using Mercedes 404
SHD bus type (i.e., it may use Mercedes 404 RHL bus
type or connected to any trip other than 772). Hence,
,,
0
1
ijk
Xor


=



.
To prevent trips being counted twice, each trip must be
served exactly once. That is, each departing trip must be
a turn of only one arriving trip or being an originating
trip and use only one bus type. This means, for example,
neither 1318.772.1
1
X
=
and 763.772.1
1
X
=
nor 1318.772.1
1
X
=
and 1318.772.2
1
X
=
can happen. Suppose that
T
repre-
sents the total number of scheduled trips, then the first
constraint can be written as follows:
2
,,
01
=1
Tk
ijk
ik
Xj
=
==
∀
∑∑ (1)
2) Continuity of equipment:
To assure the integrity of the network, each trip being
served must begin (sequence origination or continued
from another trip) and end (sequence termination or turn
into another trip) on the same bus type. This constraint
can be stated as follows:
,,,,
00
,
TT
ilkljk
ij
XXlk
==
=∀
∑∑ (2)
3) Schedule balance by station and bus type:
An excess of arrivals over departures at a given station
results in a sequence origination shortage; the reverse
situation leads to a sequence termination shortage. Figure
2 shows an example of this case where there are three
sequences for three stations Riyadh, Dammam, and Jed-
dah: 1350-771-1317, 761-1309-1352, 765-1313. Riyadh
station is balanced while Dammam and Jeddah Stations
are not. Jeddah station has one origination trip represented
by OR (trip 1350) and two termination trips represented
by TE (trip 1313 and trip 1317), i.e., there is a sequence
origination shortage at Jeddah station. There is a reverse
situation at Dammam station where a sequence termina-
tion shortage happens.
A difference between the schedule’s total departures
and total arrivals represents a physical imbalance. To
overcome this imbalance, we introduce the following
decision variables:
sk
O
= No. of origination shortages at station
s
for
bus type
k
.
sk
R
= No. of terminations shortages at station
s
for
bus type
k
.
Therefore, the sum of sequence originations and the
origination shortage variable (
sk
O
) must be equal to the
sum of sequence terminations and the termination short-
age variable (
sk
R
) for each station
s
and bus type
k
.
Hence, the third constraint can be written as follows:
0,,,0,
= ,
ss
jkskiksk
jDiA
XOXRks
∈∈
++∀
∑∑
(3)
where
s
D
= Set of departures from station
s
s
A
= Set of arrivals at station
s
.
4) Bus count
The main objective of the assignment model, as we
will see later, is to minimize the number of buses used.
Therefore, if the schedule is too small for the available
buses, only the number needed should be used. In con-
trast, if the schedule is too large, then the available buses
of the two types should be exhausted before any addi-
tional buses can be added. The constraint can be stated as
follows:
0,,
1
T
jkkk
j
XEMk
=
−≤∀
∑ (4)
where
k
M
= Number of available buses of type
k
in all

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 377
Figure 2. Imbalance schedule.
stations.
k
E
= Number of the additional buses of type
k
in
all stations that are needed beyond the available number
to cover the service schedule.
2.1.2. Objective Function
After many interviews with the decision makers in the
SAPTCO operations department, the following goals that
should be satisfied by the model were determine:
1) Mercedes 404 SHD bus type must be used for a
specific trip (e.g., Riyadh-Jeddah, Riyadh-Makkah, Ri-
yadh- Madinah, etc). After covering all of these specified
trips, it is preferred to use this bus type for any others trips
until it is exhausted. Then, Mercedes 404 RHL bus type
should cover the remaining trips.
To incorporate this goal into the model the following
parameter is defined:
jk
b
= Benefit of operating trip
j
on bus type
k
where values of
jk
b
for the following cases can be as-
sumed:
• If Mercedes 404 SHD bus type must be used, then
1
4
j
b
=
and 2
0
j
b
=
.
• If Mercedes 404 SI-1D bus type is preferred to be
used, then 1
3
j
b
=
and 2
2
j
b
=
.
Hence, this goal can be written as:
2
,,
001
TTK
jkijk
ijk
MaximizebX
=
===
∑∑∑
2) A minimum number of buses must be used to mini-
mize the total operation cost or to maximize the total
profit (the revenue is fixed). This goal consists of two
parts; in the first part, the use of the available buses (the
origination trips) must be reduced. In the second, the use
of the additional buses (
k
E
) must be reduced by impos-
ing a large cost or penalty of using it. This can be stated
as follows:
22
0,,1
111
Tkk
jkk
jkk
MinimizeXCE
==
===
+
∑∑∑
where
1
C
is a large penalty value, say, 1
1000000
C=.
3) Shortages in sequence originations and terminations
result in dead-heading and incur costs. To reduce the
chance of producing an imbalance schedule, a large pen-
alty value is imposed for the decision variables
sk
O
and
sk
R
in the objective function as follows:
( )
2
211
Sk
sksk
sk
MinimizeCOR
=
==
+
∑∑
where
S
is the total number of stations and
2
C
is a
large penalty value , say, 2
500000
C=.
Combining all of the above model ingredients, the ILP
assignment model is:
ILP:
( )
22
,,2
00111
22
0,,1
111
TTKSk
jkijksksk
ijksk
Tkk
jkk
jkk
MaximizebXCOR
XCE
==
=====
==
===
−+
−−
∑∑∑∑∑
∑∑∑

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 378
Subject to:
2
,,
01
1
Tk
ijk
ik
Xj
=
==
=∀
∑∑
,,,,
00
= ,
TT
ilkljk
ij
XXlk
==
∀
∑∑
0,,,0,
= ,
ss
jkskiksk
jDiA
XOXRks
∈∈
++∀
∑∑
0,,
1
T
jkkk
j
XEMk
=
−≤∀
∑
where
,,,
kskskk
EORandE
are decision variables taking
the following values:
,,
0,1
ijk
X= and
,, 0,1,2,3,...
skskk
ORandE =
3. The Results
3.1. Sample Example Results for Two Fleet
Types
To validate the model before its application to the whole
schedule, we selected a sample of 40 trips which satisfy
all the model requirements. This sample example con-
sists of five main stations: Riyadh, Jeddah, Dammam,
Madinah, and Abha, and four minor stations: Bishah,
Jawf, Khafji, and Qurayyat. A three-hour period was
chosen as minimum time for any arrival trip to turn (be
connected) to any departure trip at the same station. Two
buses types Mercedes 404 SHD (
1
K
=
in the model)
and Mercedes 404 RHL (
2
K
=
in the model), were
used.
The results of this application showed that all con-
straints were satisfied, that is, for each bus type each de-
parting trip was indeed, a connection (turn) of only one
arriving trip or an origination trip which satisfies the first
trip coverage constraint. Each arriving trip was turned
(connected) to only one departing trip, or it was a termi-
nation trip on the same bus type, which satisfies the sec-
ond constraint. Constraint (4), bus count, was satisfied
for each station where the number of origination trips
never exceeded the number of the available buses plus
the additional ones. Table 1 shows that the number of
origination (orig.), connection (con.), termination (term.),
and departure (dep.) trips for each main and minor sta-
tion where the number of origination trips plus the num-
ber of connection trips is equal to the total number of
departure trips, while the number of termination trips
plus the number of connection trips is equal to the total
number of arrival trips. Since the total number of depar-
ture trips is equal to the total number of arrival trips for
each station and the satisfaction of constraint (3) of the
assignment model, in addition to forcing the values of
sk
O
and
sk
R
in the last part of the objective function to
be zero through the penalty value
2
500000
C=, then the total number of origination
trips is always equal to the total number of termination
trips. This means that for any terminated arrival trip, the
bus will be overnighting in the station then operate the
next day origination trip. This makes a balanced sched-
ule.
To compute the needed number of bused to cover the
40 trips sample example, as shown in Table 2, we added
the trip time (the time that the trip took from the depar-
ture station to the arrival station) and the connection time
(the actual time that elapsed for any arrival trip to a spe-
cific station to connect another departure trip from the
same station) for all trips, then divided these number of
hours by 24. That is, the total number of needed buses is
given by the following:
24
438.75329.25 32
24
Totalnumberofneededbuses
TotaltripstimeTotaltripsconnectiontime
buses
+
=
+
==
Using the existing assignment system, the actual
number of needed buses was 45 buses of both types. This
means 13 (29% saving) buses were saved using the pro-
posed assignment system.
3.2. Model Modification to Incorporate Only
One Bus Type (Mercedes SHD 404)
The good saving in the total number of needed buses
encourages the decision makers at SAPTCO to decide to
use only Mercedes 404 SHD bus type. Therefore, we
Table 1. Summary results of the application of the assignment model on the sample example.
Main Stations Minor Stations
Riyadh Jeddah Dammam Abha Madinah Bishah Jawf Khafji Qurayyat Total
Orig. 2 1 1 0 0 2 0 2 0 8
Con. 7 4 6 5 3 1 3 0 3 32
Term. 2 1 1 0 0 2 0 2 0 8
Dep. 9 5 7 5 3 3 3 2 3 40

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 379
Table 2. Computation of the needed number of buses.
Sequence Trip No. Trip Time Connection Time Total Time Station
1 236 5 4 9 Jeddah
2 1502 8.5 10.5 19 Jeddah
3 1350 18.5 15.5 34 Jeddah
4 512 9 10.5 19.5 Jeddah
5 1316 12 6 18 Jeddah
6 1930 13 8.5 21.5 Riyadh
7 762 4.5 3 7.5 Riyadh
8 1305 11 4.75 15.75 Riyadh
9 11.6 14 13 27 Riyadh
10 1962 12 4 16 Riyadh
11 2310 14 3.75 17.75 Riyadh
12 2282 7 7 14 Riyadh
13 2302 17 3 20 Riyadh
14 1204 12 7.5 19.5 Riyadh
15 761 4.5 16.5 21 Dammam
16 2214 3.5 5 8.5 Dammam
17 1250 15 5.5 20.5 Dammam
18 1351 18.5 3 21.5 Dammam
19 2202 17 3 20 Dammam
20 2553 16 8 24 Dammam
21 2208 13 8.5 21.5 Dammam
22 501 9 3 12 Abha
23 1506 3.5 4.5 8 Abha
24 1941 12.75 15.5 28.25 Abha
25 2553 16 7.5 23.5 Abha
26 1949 13 4.25 17.25 Abha
27 1201 12.5 3 15.5 Madinah
28 229 5 15.5 20.5 Madinah
29 1251 16 12.5 28.5 Madinah
30 1507 3 13 16 Bishah
31 1961 12 7 19 Bishah
32 1505 8.5 20.5 29 Bishah
33 2209 14 4 18 Jawf
34 2313 14 9 23 Jawf
35 2210 5 7 12 Jawf
36 2211 5 7 12 Qurayyat
37 2201 17 7 24 Qurayyat
38 2303 17 10 27 Qurayyat
39 2281 7 19.5 26.5 Khafji
40 2213 3.5 9 12.5 Khafji
Total 438.75 329.25 768
Total no. of buses = 768/24 = 32

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 380
modified the fleet assignment model by deleting the first
part of the objective function and let all the decision va-
riables not depend on bus type. This modified model was
applied to the same sample example using only Mercedes
404 SHD bus type.
3.2.1. Problem Size and Decomposed Model
As mentioned in Abara’s paper, the approximate number
of
,,
ijk
X variable decision variables is
5
TK
, where
T
is the total number of trips and
K
is the total number of
fleet (bus) types. There are also 2
KS
shortage variables
(
,
sksk
OR
) and
K
additional bus variables (
k
E
). To
compute the total number of constraints, the first con-
straint comprise
T
trip coverage constraints, the second
constraint comprises
TK
continuity of equipment con-
straints, the third constraint comprise
KS
schedule ba-
lance constraints, and the forth constraint comprises
K
bus count constraints.
Table 3 shows the actual number of variables and con-
straints for the application of the original (two bus types
used) and modified (one bus type used) models on the
sample example. It also show the expected number of
decision variables and the number of constraints if the
modified model applies to whole schedule which consists
of 382 trips and 28 stations.
From Table 3, the problem size becomes larger for the
application of the modified model to the whole schedule.
This encouraged the decomposition of the modified model
by station. The assignment results for the three models,
original, modified and decomposed, were different but the
total connection times were the same (329.25 hours). This
means that the three models utilized the same number of
buses to operate the given schedule.
3.2.2. Application of the Decomposed Modified Model
to the Whole Schedule
SAPTCO’ intercity bus schedule comprise a list of 382
major trips per day to over 250 cities and villages utiliz-
ing 328 buses of the Mercedes 404 SHD and Mercedes
404 RHL types (using the existing assignment system).
This schedule consists of 14 main and 14 minor stations.
From the previous discussion the decomposed modi-
fied model was applied to this whole schedule using only
Mercedes 404 SHD bus type taking 4 hours as the mini-
mum time for connection in Riyadh and Jeddah stations
Table 3. Problem size for different models.
Original Model Modified Model
Models
Problem Size Sample
Example Sample Example Whole Schedule
Total No. of
Variables 432 21 6 1 967 (expected)
Total No. of
Constraints 140 90 793
and three hours for other stations, The results showed that
all constraints were satisfied as mentioned in the sample
example and the total number of needed buses to cover
the whole schedule was 274 buses of Mercedes 404 SHD
type.
1) Model Efficiency:
The existing assignment system uses 328 buses to
cover the 382 trips per day. The total trip time (working
hours) was 2951 hours. For the proposed assignment
system, the total connection times (lay-over hours) was
3625 hours. To compare the existing and proposed as-
signment system, the following measures of effectiveness
(MOE) were computed:
For the existing assignment System:
Average working hours per bus per day 2951
9
328
==
hours
Average lay-over hours per bus per day = 15 hours
Percent of daily working time 9
10037.5%
24
=×=
For the proposed assignment system:
Average working hours per bus per day =
2951
274
=
10.77 hours
Average lay-over hours per bus per day
3625
274
= =
13.23 hours
Percent of daily working time= 10.77
10044.88%
24
×=
The increase in the percent of daily working time =
7.38%
Model efficiency 328274
16.5%
328
−
==
The above results shows that increasing the average
working hours per bus per day using the proposed as-
signment system by only 1.77 hours (or 7.38%) saved 54
buses (16.5% of the existing used buses).
2) Sensitivity Analysis
The predefined minimum connection time (four hours
for Riyadh and Jeddah stations and three hours for others
stations) was judgmental and was not based on any field
studies. The predefined minimum connection time for 11
stations (most of them are minor stations that the people
at SAPTCO think that they really do not need three hours
as a connection time) were reduced to one hour instead of
three hours and the proposed assignment model was re-
applied to these stations. Then, the total real connection
time for each station were computed and compared to
that before modification. The results showed that there
was a saving of 336 hours for the 11 stations. This means
that 14 more buses were saved. Moreover, the predefined

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 381
minimum connection time for Abha and Makkah stations
were reduced to two hours and the results of the reappli-
cation of the proposed assignment model showed that
there was a saving of 3 more buses. This gives us the
following MOE for the proposed assignment model:
Average working hours per bus per day
2951
257
= =
11.48 hours
Average lay-over hours per bus per day
3217
257
= =
12.52 hours
Model efficiency 328257
21.6%
328
−
==
The more interested results from the above sensitivity
analysis are:
• The real connection times varies from its minimum,
3 hours, to more than 20 hours and few of them were
3 hours. This means that the rush demand for
maintenance is not really true.
• For Makkah station when the predefined minimum
connection was reduced to two hours, only 5 de-
parture trips out of 43 ( the total number of depar-
ture trips) needed real connection time less than
three hours. The same was happen for Abha station
where only 5 departure trips out of 33 needed real
connection time less than three hours. If these spe-
cific departure trips take a high priority for bus
checking and cleaning, the reduction of the prede-
fined minimum connection will not be very critical
for the maintenance department.
• For Abha station when we try to reduce the prede-
fined minimum connection by one more hour (after
it was reduced to two hours), the total connection
time was the same as for two hours predefined
minimum connection. This means that the prede-
fined minimum connection limit behind it we can-
not save any buses
From the above results, by performing a field study of
this predefined minimum connection time for every sta-
tion, the expected saving of the total number of needed
buses will be about 90 buses (Model efficiency = 27.4%).
This will yield a net saving of 16 million Saudi riyals per
year for SAPTCO as will be illustrated in the cost analy-
sis next.
3) Cost Analysis
Since the revenues are the same for the existing and the
proposed systems as both systems operate the same
number of daily trips (i.e., the same intercity schedule), the
comparison between both systems concentrate on the
operation cost for both systems. The operation costs con-
sist of two parts, the first is the direct (variable) costs
which are divided to kilometer cost that equal to 0.32
SR/km and hour cost that equal to 35.15 SR/hr. The sec-
ond part is the fixed cost which is counted for the daily (24
hours) use of the bus. This fixed cost estimated to be 668
SR/day. That is, for example, a trip from Riyadh to Jeddah
take about 12 hours and its length about 1000 kilometers
will cost:
0.32100035.15126681409.8 SR
×+×+=
.
Since the existing and the proposed systems operate
the same number of kilometers and the same number of
hours, then our comparison will depend on the fixed
cost that depend on the number of buses used. The ex-
isting system use 328 buses to cover the service sched-
ule, while from the model results the proposed system
need 238 buses. This means that there is a saving of
(
)
32823866860120
−×= SR/day or about 21.94 million
SR per year.
The proposed system incur hiring new employees for
bus checking, filing, and reporting bus status during the
connection time (the proposed three hours) before an-
other driver operates the bus for the next trip. The total
hiring costs were estimated to be about SR 5.5 million
per year. This means the net saving cost will be about SR
16.44 million (USD 4.4 million) per year.
4) Revenue Management
As we mentioned in the cost analysis the revenue from
the proposed system is not changed, but as a result of the
proposed system, SAPTCO will have 90 buses surplus
and these buses can be utilized to yield new additional
revenue as follows using the revenue data in Table 4:
• There are seasonal demands for the SAPTCO buses
for about four months during a year. Three months
for what is called “O’Mara”, which is Muslim re-
ligion custom, to visit Al Kaaba in Makkah city and
its peak demand in Ramadan, Shaban, and Ragab
months of Hagree calendar. In these months SAP-
TCO outsources buses form other transport compa-
nies. Using all or part of their surplus buses will
yield additional revenue.
Yearly (1)
90
60903800 20,520,000USD 5,472,000
AdditionalRevenue
NumberofbusesDaysbusrevenueperday
SR
=××
=××==
• There is also one month that has the highest demand
Table 4. Average daily bus revenue for different trip type.
Trip Type Revenue/Bus/day
Intercity Trip SR 2000
International Trip SR 3800
O’Mara Season Trip SR 3800
Pilgrim (Hajj) Season Trip SR 5500
City School or Company Trip SR 400

Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue 382
for buses during pilgrim (Hajj) season which is also
a Muslim religion custom to visit Al Kaaba in
Makkah city at least one time in the Muslim person
life. In this month they can use their surplus buses
instead of outsourcing.
Yearly (2)
30
60305500 9,900,000USD 2,640,000
AdditionalRevenue
NumberofbusesDaysbusrevenueperday
SR
=××
=××==
• Around the year, there are high demands from others
agencies, like schools, manpower companies, and
others small companies to outsourcing buses from
SAPTCO. Using some of their surplus buses will
yield new revenue.
Yearly (3)
240
60240400 5,760,000USD 1,536,000
AdditionalRevenue
NumberofbusesDaysbusrevenueperday
SR
=××
=××==
• Around the years there are a medium demand for
international trip to Egypt, Jordon, Iraq, Syria, Le-
banon, and Yemen countries
Yearly (4)
365
303653800 41,610,000USD 11,096,000
AdditionalRevenue
NumberofbusesDaysbusrevenueperday
SR
=××
=××==
• Therefore the total yearly additional revenue will
be:
Yearly
(1) (2)
(3) (4)
USD 20,744,000
TotalAdditionalRevenue
AdditionalRevenueAdditionalRevenue
AdditionalRevenueAdditionalRevenue
=++
+
=
4. Summary and Conclusions
In this paper, a new intercity bus schedule for the Saudi
Public Transport Company (SAPTCO) has developed.
Conversely to the existing assignment system, the new
assignment system assigns buses to the given intercity
bus schedule first, and then assigns drivers to those
scheduled buses in such way that maximizes the utiliza-
tion of buses. The main finding of this application can be
summarized as follows:
1) Only 274 out of 328 buses of Mercedes 404 SHD are
needed to cover the service schedule (a total saving of 54
buses).
2) By performing a field study of the trip predefined
minimum connection time for every station, the expected
saving of the total number of needed buses will be about
90 buses.
3) The new schedule system yielded the following for
SAPTCO:
• A net saving of USD 4.4 million per year.
• Hiring new employees with no additional cost for
bus checking, filing, and reporting bus status during
the connection time.
• Additional revenue of USD 20,744,000 per year
from the use of the 90 surplus buses.
5. Directions for Further Research
Based on the results and the analysis, directions for fur-
ther research can be summarized as follows:
1) The new assignment system is based on the given
service intercity schedule which may be optimal (it may be
built in the spirit of the existing system). This encourages
developing a new optimal service schedule and reapplying
the assignment model for it.
2) The determination of three hours as a minimum
connection time for all station is judgmental and need
field studies.
3) The existing drivers’ assignment system which used
to assign drivers to the scheduled buses need to be adapt-
ed to take into account the advantages of the new bus
assignment system.
4) Developing a maintenance bus schedule so that bus
has its maintenance schedule time depending on the pro-
posed assignment system.
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