Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
J. Environ. Treat. Tech. ISSN:  
309-1185  
2
Journal weblink: http://www.jett.dormaj.com  
Dynamic Pricing Strategy of Indonesia Air Cargo  
Carriers in the Domestic Market  
*
Iftitah Putri Haditia, S.T. , Alberto Daniel Hanani, MBA  
Faculty of Economics and Business, Magister Management, Universitas Indonesia, Indonesia  
Received: 18/04/2019  
Accepted: 25/09/2019  
Published: 29/09/2019  
Abstract  
Most of air cargo carriers in Indonesia domestic market are using single pricing strategy and not taking booking time into  
considerationfor space pricing. It could be a challenge to optimize cargo space revenue of air cargo carriers. Thispaper aims to optimize  
cargo space revenue using dynamic pricing strategy by considering different booking time or sales period. This research conducts  
empirical analysis on air cargo pricing strategiesfor Garuda Indonesia on certain significant routes. It concludes that Garuda Indonesia  
generate more revenues using optimized dynamic pricing strategy than using single pricing strategy. In practice, this paper provides  
references for air cargo carriers in their decision making of applying the dynamic pricing strategy.  
Keywords: Revenue Management; Air Cargo Carrier; Dynamic Pricing; Single Pricing; Sales Period.  
1
and cargo types. Most of them do not take the booking time  
1
Introduction  
into consideration for pricing decision. As such, air cargo  
carriers in domestic market in Indonesia stuck using single  
pricing strategy overall sales periods.  
Revenue management is about analyzing the situation to  
predict the behavior of the customers, which is uncertain, and  
provide the right amount of products or services for the  
customers in order to maximize the revenue. The main  
objective of revenue management is to sell the right products  
or services to the right customers in the right time (13). In the  
late 1970s, American airlines has adopted the concept of  
revenue management, the essence of which was to sell a right  
number of products to suitable customer segments at the right  
prices during the right time frames to maximize the sales  
revenue and increased their revenue by 40% (13). Nowadays,  
revenue management is widely recognized and utilized. Many  
companies and industries have adopted revenue management  
techniques in order to increase their profit, especiallyin airline  
industry.  
Over the past decade, there has been continuous growth in  
worldwide air cargo transportation. According to the forecasts  
of Boeing and Airbus, the growth and contribution of air cargo  
industry to economic development is expected to more than  
double within the next 20 years. Air cargo transportation  
becomes very important for cargos that need short  
transportation time and high reliability. The application of  
revenue management become very important for airline in  
managing rapid growth of air cargo business. Recently, almost  
all air cargo carriers in Indonesia, specifically domestic  
market, always make their price according to the shipper types  
The single pricing mode strategy may have the following  
shortcomings. Firstly, a single pricing mode neglects the fact  
that potentialdemands in the air cargo market at different sales  
periods are not the same. Secondly, this pricing mode ignores  
that the degrees of demand changes in response to space prices  
are different at different sales periods. Lastly, the single  
pricing mode may not match the relationshipbetween booking  
demands and space supplies on segments with a supply  
shortage. As such, it is necessaryfor air cargo transportcarriers  
to apply the innovative pricing strategy for the limitedspace in  
the air cargo market. Similar to the air passenger industry, a  
dynamic pricing mode referring to different booking periods  
can be utilized to maximize cargo revenues.  
This study will analyze about air cargo revenue  
management in Garuda Indonesia (GA), the Indonesian legacy  
carrier. Currently, GA adopt single pricing strategy to manage  
its air cargo revenue. Domestic route has dominant  
contribution by 70% of total air cargo revenue and unique  
characteristic of market as well that will be our main  
discussion. From the market demand side, the rise of  
ecommerce in Indonesia is supported by Indonesian  
purchasing power of domestic market and positive economic  
growth. Those positive condition lead to the increasing of air  
Corresponding author: Iftitah Putri Haditia, S.T., Faculty of  
Economics and Business, Magister Management, Universitas  
Indonesia, Indonesia. E-mail: iftitah.ph@gmail.com.  
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
cargo demand. The increasingtrendis also captured by Garuda  
Indonesia management to their cargo revenue target 2019. The  
revenue target increase 59% from previous year. In other side,  
Garuda Indonesia has to face efficiency program by using  
threshold Seat Load Factor (SLF) in passenger market, and the  
flight with SLF no more than decided threshold will be  
canceled. The cancel flight policy due to efficiency causes gap  
between capacity planning and realization for cargo belly  
capacity that still demand on belly space of passenger aircraft.  
The capacity for cargo in first quarter 2019 is decreasing by  
contracts and the decision of accepted spot booking were  
optimized by Moussawi-haidar and tying mechanism of hot-  
selling routes and underutilized routes was developed by Feng  
et al (6,11).  
Pricing studies include relationship between price and  
demand of air cargo, and some matters regarding joint price  
and quantity optimization. For relationship between price and  
quantity of demand, many previous research studied the  
demand function as monotonically descend with the price, for  
example Xi, Xuefei, & Hua (16). Some air ticket pricing  
studies assume that the demand depends on the customer  
arriving process and the buying probability of customers  
affected by price (12). Regarding the issues of joint price and  
quantity optimization, dynamic programing models are  
developed. Such as, Chew et al. proposed a dynamic  
programming model to jointly determine the price and  
inventory allocation for a product with two-period lifetime (4).  
In addition, Cizaire and Belobaba took both the fares and  
booking limits as decision variables based on a two-period  
booking time and two fare classes (5). Yoon et al. studied the  
joint pricing and seat control in air passenger industry with the  
consideration of cancellation in booking processes and a mark-  
up policy in pricing strategy under uncertain demands (17).  
The above literature about demand forecasting,  
overbooking level and capacity control mainly considers the  
determinationof space price with prices beingfixed ratherthan  
being taken as decision variable. This research aims to explore  
the pricing strategies of air cargo carriers in the competitive  
market. Therefore, the next section deeply reviews the current  
research of pricing strategies in air cargo industry.  
Pricing studies include relationship between price and  
demand of air cargo, and some matters regarding joint price  
and quantity optimization. For relationship between price and  
quantity of demand, many previous research studied the  
demand function as monotonically descend with the price, for  
example Xi, Xuefei, & Hua (16). Some air ticket pricing  
studies assume that the demand depends on the customer  
arriving process and the buying probability of customers  
affected by price (12). Regarding the issues of joint price and  
quantity optimization, dynamic programing models are  
developed. Such as, Chew et al. proposed a dynamic  
programming model to jointly determine the price and  
inventory allocation for a product with two-period lifetime (4).  
In addition, Cizaire and Belobaba took both the fares and  
booking limits as decision variables based on a two-period  
booking time and two fare classes (5). Yoon et al. studied the  
joint pricing and seat control in air passenger industry with the  
consideration of cancellation in booking processes and a mark-  
up policy in pricing strategy under uncertain demands (17).  
The above-mentioned studies mainly focus on the  
monopolistic market, i.e. only one firm is in the market. Then  
the demand depends on the pricing of the firm only. While this  
research focuses on the pricing strategies in the occasion of  
two carriers in the market, thus a competitive market is  
considered. But this study does not consider the case that  
domestic air freight competes with road freight transport. The  
following part reviews the current pricing studies in the  
competitive market. Current studies regarding pricing in the  
competitive market are mainly based on the game theory, such  
as the game pricing with considering the two price classes  
20%. As such it is necessary for GA to apply an innovative  
pricing strategy for limited space. Same as air passenger  
industry, a different booking periods can be utilized to  
maximize air cargo revenue. This paper aims to optimize cargo  
space revenue using dynamic pricing strategy by considering  
different booking time or sales period.  
2
Literature Review  
Revenue management of air transport mainly focused on  
four key important points, they are demand forecasting,  
overbooking level, capacity control, and pricing. The first part  
reviews many previous studieson the research area of demand  
forecasting, overbooking level and capacity control in the field  
of revenue management and the following part explores the  
recent studies about innovative pricing strategies to further  
address dynamic condition in air cargo industry.  
For the point of air cargo demand forecasting, previous  
research mainly focused on developing forecast models and  
main determinantvariablesin demandforecasting. Such as, the  
dynamic simulation model is developed for forecasting air  
cargo demand (14) and the evaluation of forecasting model is  
conducted to obtain accuracy of air cargo demand forecasting  
(
9). More relevant research studiedof other main determinants  
such as the influence of air freight yield and oil price in future  
market development of air cargo (10,11).  
Overbooking is the practice of intentionally selling more  
cargo space than the available capacity in order to minimize  
spoilage cost due to the occurrance of no show (8). However,  
if the real cargo show up at the flight departure exceeds the  
available capacity, the offloading cost occur (3). Other  
research subsequently addressed this dilemma by optimizing  
the air cargo overbooking level with the objective of minimize  
the spoilage and offloading costs (15).  
For the point of capacity control, most previous research  
focused on optimizing the selling capacity with objective of  
maximizing revenue. For example, Amaruchkul and  
Lorchirachoonkul, studied the optimum allocation of air cargo  
capacity for multiple freight forwarders by using a discrete  
Markov chain and dynamic programing method (92). The  
recent studies of air cargo capacity control is considering two-  
dimensionality, the weight and volume of cargo, in objective  
of maximize expected revenue. Some solution methods are  
proposed to solve the problem, such as heuristics based  
methods, among which the best one is to separate two-  
dimension problem into two one-dimension state space (1). In  
addition, a heuristic algorithm to estimate the expected  
revenue from weight and volume is developed by Huang and  
Chang (7). Further research also explored many initiatives  
regarding capacity allocation, for example determination of  
total weight and volume capacity to sell through allotment  
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
between the competition of two airlines. Considering the  
booking periods in the competitive market, investigated the  
prices and capacity allocation decisions in the duopoly market  
over an early discount period and a regular full-fare season.  
From the analysis in this part, current studies mainly  
consider the case of two price classes and seldom consider the  
case of multi-periods. This research will fill the current  
research gap by considering more price classes and containing  
several sales periods in the competitive market for air freight  
transport industry with considering overbooking level to  
maximize revenue. As such, this research aims to study the  
pricing decision with consideration of multi-classesand multi-  
periods in the competition market. It focuses on the air cargo  
space pricing strategy over multi-periods in the market under  
the game between two carriers. In addition, different attributes  
between potential market demands and degrees of demand  
changes in response to space prices exist at different sales  
periods. Therefore, this research develops a pricing model to  
determine space prices for two carriers at each sales period.  
1. the amount of available spaces that owned by both  
carriers  
2. the pricing strategy of its competitor if the carrier's  
space pricing strategy is determined  
3. the degrees of demand changes in response to space  
prices of two carriers at each sales period  
4. the corresponding revenue after determining the price  
in each period.  
Suppose the two carriersare E  
at a certainperiod being affected by both the carrier's price and  
its competitor's price. In this case, if the price of E in sales  
, then function  
1 2  
and E , with theirdemands  
1
1
2
period t is p  
t
2
and E 's price in salesperiod t is p  
t
(1) denotes the booking demand of E  
1
in period t when the  
.
1
2
price of E  
1
is p  
t
and the price of E  
2
is p  
t
Dt  
= F (p  
1
, p  
2
)
(3.1.)  
1
t
t
Similarly, function (2) denotes the booking demand of E  
2
2
1
t 2 t  
and the price of E is p  
in period t when the price of E  
1
is p  
t
1
, p  
2)  
t
D
2
= G(p  
t
(3.2.)  
3
Methodology/Materials  
3
.1 Problem description  
1 2  
If the pricing strategy of E is determined, E will  
determine its own pricing strategy according to E  
and demand function (2). In turn, it will affect E  
This paper studies the dynamic pricing of air cargo space  
1
's strategy  
's booking  
pricing on a certain significant flight route. In this case, the  
space pricing during the sales process for each carrier is as  
follows. At the beginning of the sales period, the carrier owns  
its available space for the market and makes the pricing  
strategy for different sales periods. At each period, the amount  
of space booking demand dependson both the potentialmarket  
demand and the price in this period. The carrier then accepts  
these requirementsand obtain the revenue. Then the amount of  
available space reduces gradually. If all the available space is  
sold out before the end of the sales period, the carrier cannot  
accept new booking demand. On the other hand, if the  
available space is not sold out by the end of the sales period,  
the remaining space is wasted without gaining any revenue.  
The selling time in the air cargo domestic market is short.  
Once the price strategyis determinedbyone aircargo transport  
carrier, it is hard to adjust. Therefore, air cargo transport  
carriers need to make the price for each period in advance.  
Consideringthe fairness, carriersusuallydo not allow the price  
going down as the flight approaches to departure (17). The  
reason is that if the price changes to a lower one in the later  
booking stage, the former customers who have booked the  
space at higher prices may feel unfair. From a long-term  
perspective, it will lead to carriers’ profit reduction.  
Therefore, it assumes that carriers adopt the “low-before-  
high” (LBH) manner for pricing at each period, i.e. the price at  
the later period is higher than or equals to the price at the  
former period. Moreover, each period can only have one price  
class so that the space price is a section function of the sales  
period.  
An air cargo carrier adopts a dynamic pricing strategy to  
increase the cargo revenue by taking advantage of different  
potential market demands and different degrees of demand  
changes in response to space prices at different periods (18).  
In this situation, an air cargo carrier needs to consider the  
following issues when implementing the differential pricing  
strategy.  
1
demand and revenue, and vice versa. Therefore, a carrier needs  
to consider the competitor's corresponding pricing strategy  
before determining its own pricing strategy. Moreover, since  
prices at different periods follow the manner of LBH, the price  
at a former period can determine the lower limit of the price at  
a latter period. In this case, carriers cannot simply follow the  
principle of maximizing the revenue of each period to decide  
their pricing strategies. It is necessary for carriers to consider  
the constraint relationshipof the prices before they decide their  
pricing strategies.  
3
.2 Model  
This paper focuses on the air cargo pricing in the  
competitive market including multiple sales periods with two  
carrierscompeting on a certainroute. The model is constructed  
with following assumptions:  
1. The two carriers provide no difference in transport  
services. This assumption is based on the fact that customers  
do not care about transport processes in air cargo industry,  
such as comfort and transshipment, butcare about cargos being  
transported to the destinations.  
2.  
The booking demands at different periods are  
independent of each other.  
The pricing strategyof each carrier follows the manner  
3.  
of LBH. As mentioned above, the manner of LBH considers  
the fairness for customers who have booked spaces earlier.  
4. The booking demand of each carrier at each period is  
linear relating to both of its own price and its competitor's  
price. The higher of its own price or the lower of its  
competitor's price, the fewer demands will be.  
5. The situation of "overbooking" and "cancellation of  
booking" are not taken into consideration.  
T = (1, 2, 3, …, t) denotes the set of sales periods. The set  
1
of air cargo carriers is N = (1, 2). The decision variables p  
t
2
and p representthe price at the period t of carrier 1 and carrier  
t
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
2
respectively. The accepted amount of booking demand of  
the manner of LBH. Similarly, the nonlinear programming  
pricing strategy model for carrier 2 can be constructed based  
on carrier 1's pricing strategy.  
The model of this paper focuses on the pricing  
optimization in the circumstance that optimizing dynamic  
pricing strategy of one company. In this stage, one carrier's  
pricing strategy is fixed, the price at each period of the other  
carrier will be decided  
2
2
carrier1 and carrier 2 at the period t are q  
The remaining available spaces can be sold at the period t  
owned by carrier 1 and carrier 2 are represented as W  
W
owned by carrier 1 and carrier 2. The booking demands of  
carrier 1 and carrier 2 at the period t as denoted as D and D  
respectively. Assume that the booking demand of each carrier  
at each period is linear relating to both of its own price and its  
competitor's price. As such, functions (3) and (4) denote the  
relationshipbetween demands and prices at each period of two  
carriers respectively, as below.  
t
and q  
t
respectively.  
1
t
and  
2
t
. W  
1
and W  
2
denote the amount of original availablespaces  
1 2  
t t  
3.3 The algorithm of differential pricing  
strategy for one Carrier  
Suppose that carrier 2's pricing strategy is fixed, then the  
demand function for carrier 1 at each period can be expressed  
as below.  
1
1
2
t
* p (b  
D
t
= a  
t
- b  
t
* p  
t
+ c  
t
t
≥ c  
t
) Ɐ t ϵ T  
(3.3.)  
Dt  
= a  
- b  
* p  
2
+ c  
* p  
1
(b  
≥ c  
) Ɐ t ϵ T  
(3.4.)  
2
t
t
t
t
t
t
t
(
3.11.)  
a
t
, b  
t
and c  
t
are parameters. b  
t
denotes the amount of  
demand decreasing if a carrier increases the price by one unit.  
While c denotes the amount of demand increasing if the other  
t
carrier increases the price by one unit. In each period, (b ≥ c )  
denotes that the booking demand of each carrier is influenced  
by its own price more than or equal to its competitor's price.  
As such, the nonlinearprogramming pricingstrategymodel for  
carrier 1 can be established in the condition that carrier 2's  
pricing strategy is confirmed.  
If the amount of booking demands of carrier 1 exceeds the  
amount of remainingavailablespace at a certainperiod, carrier  
1 can increase the price of that period to the level of satisfying  
t
t
1
1
D
t
= W  
t
to improve revenues. It means, for the optimized  
pricing strategy of carrier 1, the amount of booking demands  
cannot exceed the amount of remainingavailablespace at each  
1
1
. Therefore, equation (6) can be  
period, for example, D  
t
≤ W  
t
changed into equation (12).  
(
3.12.)  
(
3.5.)  
Then the pricing model of carrier1 can be change into equation  
13)  
(
(
(
3.6.)  
3.7.)  
(
3.13.)  
(
3.8.)  
(3.14.)  
(
3.9)  
(
(
3.15.)  
3.16.)  
(
3.10.)  
The objective of equation (5) is to maximize the total  
revenue of carrier 1 in all sales periods. Equation (6) means  
that the accepted amount of booking demand of carrier 1 at  
each period can exceed neither the booking demand nor the  
remaining available space. Equation (7) indicates that the  
booking demand of carrier 1 at each period cannot be negative.  
Equation (8) means the total accepted booking demand of  
carrier 1 in all sales periods cannot exceed its original owned  
available space. Equation (9) denotes the conversion  
relationship of each period's remaining space of carrier 1.  
Equation (10) means the pricing strategy of carrier 1 follows  
The objective function of equation (13) can be changed  
into equation (17)  
(
3.17.)  
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
These booking demands can reflect the potential market  
demands in their corresponding periods.  
4
Results and Findings  
4
.1 Data  
The case that two carriers in Jakarta, making their space  
prices on the route of Jakarta-Banjarmasin, Jakarta-Manado,  
Jakarta-Medan, Jakarta-Pontianak, Jakarta-Makassar are  
chosen for the empirical analysis. In Jakarta, the air cargo  
industry operated by two carriers, Garuda Indonesia Group  
(
GA) and Lion Group (JT). For the former one, it has the  
dominance and takes almost half of the market share. Several  
other air cargo operatingcompanies cooperate with each other,  
share the same pricing behavior and form a carrier union,  
which is referredas the latterone in this paper. The two parties,  
i.e. GA and JT, have the competitive relationship in practice.  
Assume that the time length of the spot market is from 7  
days prior to the flight taking off to the departure day. There  
are 8 sales periods in total if each day is taken as one sales  
period. Based on nature of business and historical booking  
trend of air cargo, we assumes that the space booking in air  
cargo was mostly distributed in the 7 days prior the departure  
date. As such, the proportion of space booking of each period  
in the air cargo market can be estimated as the proportion in  
the Figure 1. Then the distribution of the booking demands in  
different sales periods can be obtained. These ratios can reflect  
the distributionof cargo space booking demands at each period  
in air cargo market. In practice, the average daily freight  
transport volume of GA carrier on each route, based on  
average distribution in the recent 1 year as seen in the table 1.  
Additionally, based on data from Angkasa Pura II, daily air  
cargo transport volume of JT carrier on that route is also  
obtained.  
Figure 1: Booking ratios at each period  
GA carrier and JT carrier mainly use the belly space of air  
passenger aircrafts for their air cargo service. From Angkasa  
Pura II, Indonesia airport authority, it can be found that for one  
day available capacity as seen in Table 4. Currently, the  
aircraft used for passenger transportation in Indonesia is  
mainly Boeing 737-800 NG and 737-900 ER with the loading  
space of its belly being 3500 kg in total for cargo capacity.  
4.2 Result  
Define Coefficient using empirical data from both airline  
Garuda Indonesia (GA) and Lion Group (JT). The data  
gathered from daily performance with range sample of April  
2018 – March 2019. The information about pricing for both  
airline as seen in the table 1.  
After taking the initial prices and the relative cargo  
transport volume of GA carrier and JT carrier into Eq. (3) and  
t t t  
Eq. (4), the parametersa , b and c can be calculated, as shown  
in Table 6. The model can be finally solved.  
Then, with the distribution of cargo space booking  
demands at each period and the average daily air cargo  
volumes of the two parties, the booking demands at each  
period of the two partiescan be obtained, as shown in Table 2.  
Table 1: Booking demand at each period for Garuda Indonesia  
Garuda Indonesia  
Route  
Booking Demand at Each Period (Kg)  
1
2
3
4
5
6
7
>7  
921  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
1.026  
613  
1.789  
861  
6.534  
4.253  
12.413  
5.825  
10.767  
2.353  
1.497  
4.370  
2.001  
3.254  
1.282  
836  
2.441  
1.537  
2.013  
1.043  
638  
1.862  
1.405  
1.573  
965  
1.038  
488  
1.423  
1.033  
1.616  
523  
374  
1.092  
930  
1.526  
1.124  
1.500  
3.050  
1.520  
Table 2: Booking demand at each period for Lion Group  
Booking Demand at Each Period (Kg)  
Lion Air Group  
Route  
1
2
3
4
5
6
7
>7  
522  
243  
440  
335  
1.045  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
581  
398  
721  
310  
2.097  
3.699  
2.759  
4.998  
2.102  
7.404  
1.332  
971  
1.760  
722  
726  
543  
983  
555  
1.384  
590  
414  
750  
507  
1.082  
546  
339  
614  
405  
1.031  
588  
316  
573  
373  
1.111  
2.238  
9
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
Table 3: Available Capacity for Garuda  
Route  
Available Capacity each day (Kg)  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
15.000  
7.000  
24.500  
14.000  
23.500  
Table 4: Cargo price of Garuda Indonesia  
Single Price per kg (IDR)  
Route  
1
2
3
4
5
6
7
>7  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
19.400  
41.800  
17.000  
18.500  
28.700  
Table 5: Cargo price of Lion Group  
Price Per Kg (IDR)  
Route  
1
2
3
4
5
6
7
>7  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
19.800  
49.470  
17.460  
18.964  
34.920  
19.800  
49.470  
17.460  
18.964  
34.920  
49.470  
17.460  
18.964  
34.920  
Table 6: Coefficient a b c  
Coeficient at each Period  
Rout  
Coefficient  
1
8.072.84  
0.74  
2
51.418.14  
4.72  
3
18.518.64  
1.70  
4
10.088.07  
0.93  
5
8.204.01  
0.75  
6
7.595.86  
0.70  
7
>7  
7.250.05  
0.67  
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
8.168.23  
CGK-  
BDJ  
0.75  
0.38  
0.37  
2.36  
0.85  
0.74  
0.38  
0.35  
0.33  
932.55  
0.02  
6.468.95  
0.13  
2.277.38  
0.05  
1.271.91  
0.03  
970.41  
0.02  
795.12  
0.02  
741.83  
0.01  
569.35  
0.01  
CGK-  
MDC  
0.01  
0.06  
0.02  
0.01  
0.01  
0.01  
0.01  
0.01  
14.600.31  
1.55  
101.280.07  
10.75  
35.655.50  
3.78  
19.913.51  
2.11  
15.193.11  
1.61  
12.448.69  
1.32  
11.614.39  
1.23  
8.913.88  
0.95  
CGK-  
MES  
0.77  
5.37  
1.89  
1.06  
0.81  
0.66  
0.62  
0.47  
7.987.65  
0.79  
54.070.28  
5.35  
18.569.59  
1.84  
14.2683.54  
1.41  
13.039.68  
1.29  
10.431.74  
1.03  
9.585.19  
0.95  
8.629.40  
0.85  
CGK-  
PNK  
0.40  
2.67  
0.92  
0.71  
0.65  
0.52  
0.47  
0.43  
4.197.11  
0.10  
14.818.66  
0.36  
4.479.01  
0.11  
2.770.23  
0.07  
2.164.68  
0.05  
2.063.75  
0.05  
2.223.84  
0.05  
2.091.60  
0.05  
CGK-  
UPG  
0.05  
0.18  
0.05  
0.03  
0.3  
0.3  
0.3  
0.3  
9
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Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
Table 7: Pricing result under dynamic pricing strategy  
Dynamic Pricing prioe Per Kg (IFR)=p1  
Rout  
1
2
3
4
5
6
7
<7  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
19.420.0236  
50.028.5732  
17.122.0074  
18.556.4307  
31.390.7439  
19.420.0236  
50.028.5732  
17.122.0074  
18.556.4307  
31.390.7439  
19.420.0236  
50.028.5724  
17.122.0074  
18.556.4307  
31.390.7439  
19.420.0236  
50.028.5724  
17.122.0074  
18.556.4307  
30.818.7415  
19.400.0000  
50.028.5724  
17.122.0074  
18.556.4307  
30.818.7415  
19.400.0000  
50.028.5724  
17.000.0000  
18.556.4307  
28.700.000  
19.400.0000  
50.028.5724  
17.000.0000  
18.556.4307  
28.700.000  
19.400.0000  
41.800.0000  
17.000.0000  
18.500.0000  
28.700.0000  
The single pricing mode currently adopted by two carriers  
is optimized to test whether the dynamic pricing strategy can  
make more revenue than the single pricing strategy. The  
pricing resultsunderthe single pricing strategyfor two carriers  
are shown in Table 4 and Table 5. The dynamic pricing results  
achieved for the GA are shown in Table 7. Under this pricing  
strategy, the total revenue of GA is IDR 2.040.379.402 for the  
sample 5 routes. It shows the revenue has increased by using  
the proposed dynamic pricing strategy compared to the  
revenue IDR 1.933.555.220, gained under the initial pricing  
strategy. The results indicate that if GA carrier change its  
initial pricing strategy to the dynamic pricing strategy,  
revenues can increase by 6%. By generating dynamic pricing  
strategy which impact the booking demand as one of our  
decision variable, Table 8 shows the accepted booking demand  
at each period for the 5 sampe route. It shows that dynamic  
pricing causes different demand at each period and result  
suggestion of optimum accepted booking demand at each  
period. Figure 2 shows revenues of GA carrier at each sales  
period by using the optimal dynamic pricing strategy and the  
single pricing strategy. It can be found that 1) sales revenues  
under the single pricing strategy are lower than the  
corresponding revenues under the dynamic pricing strategy.;  
2) under the dynamic pricing strategy, dynamic pricing  
strategy match the relationship between booking demands and  
space supplies on segments with a supply shortage.  
5
Conclusion  
Based on the competition between two air cargo transport  
carriers on a certain route in the domestic market in Indonesia,  
this paper studies the dynamic pricing strategy applied in the  
decision making over multiple sales periods for limited cargo  
space/capacity. The pricing strategies of two carriers are  
optimized with the objective of maximize sales revenues in the  
whole periods. The empirical analysis studies the case that GA  
carrier and JT carrier compete on the certain 5 sample  
significant routes. From the empirical results, the research  
draws the conclusion that the dynamic pricing strategy can  
obtain more revenues than the single pricing strategy in the  
Indonesia air cargo market;  
The model in this study is also possible to be applied in  
other industries,such as linershippingindustry, if the available  
space is fixed and services cannot be stored in these industries  
Table 8: Accepted booking demand  
Accepted Booking Demand (Kg)=q1  
Rout  
1
2
3
4
5
6
7
<7  
921  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
1.011  
459  
6.439  
3.185  
11.102  
5.523  
9.797  
2.319  
1.121  
3.908  
1.897  
2.961  
1.263  
626  
1.043  
478  
965  
1.038  
365  
391  
374  
1.600  
816  
2.183  
1.458  
1.870  
1.665  
1.332  
1.461  
1.526  
1.066  
1.500  
1.423  
979  
1.092  
930  
2.775  
1.616  
1.520  
Table 9: Revenue under dynamic pricing strategy  
Revenue under Dynamic Pricing (IDR)  
Total  
Rout  
1
2
3
4
5
6
7
<7  
Revenue  
CGK-BDJ  
CGK-MDC  
CGK-MES  
CGK-PNK  
CGK-UPG  
19.633.738  
22.968.568  
27.401.755  
15.140.870  
87.106.545  
125.052.693  
159.329.336  
190.081.637  
102.492.043  
307.545.344  
45.038.692  
56.091.661  
66.917.971  
35.199.288  
92.956.985  
24.534.923  
31.327.057  
37.373.521  
27.046.510  
57.633.429  
20.225.011  
23.901.127  
28.514.308  
24.717.147  
45.035.168  
18.725.774  
19.583.727  
25.936.621  
19.773.717  
43.036.198  
20.136.821  
18.271.238  
24.198.364  
18.169.044  
46.374.588  
17.873.267  
15.647.078  
18.571.902  
17.198.949  
43.616.788  
291.220.919  
347.119.792  
418.996.079  
259.737.568  
723.305.044  
9
7
Journal of Environmental Treatment Techniques  
2020, Volume 8, Issue 1, Pages: 91-98  
Figure 2: The comparison of GA’s revenue carrier at each period under dynamic pricing strategy and single pricing strategy  
For similarcases and industries,insteadof using one single  
pricing strategy, operators can utilisethe adopted model in this  
paper and design their differential pricing strategies to obtain  
more revenues. As any research has the limitation, this  
research provides ample room for future studies. For example,  
it would be interesting for future studies to develop pricing  
optimization models among three or more carriers in the air  
freight transport industry. In addition, future research can  
consider the situationof overbooking and cancellation by large  
forwarders which can impact on the space selling in the spot  
market.  
10. Kupfer F, Meersman H, Onghena E, Van de Voorde E. The  
underlying drivers and future development of air cargo. Journal of  
Air Transport Management. 2017 Jun 1;61:6-14.  
1
1. Moussawi-Haidar L. Optimal solution for a cargo revenue  
management problem with allotment and spot arrivals.  
Transportation Research Part E: Logistics and Transportation  
Review. 2014 Dec 1;72:173-91.  
12. Otero DF, Akhavan-Tabatabaei R. A stochastic dynamic pricing  
model for the multiclass problems in the airline industry.  
European Journal of Operational Research. 2015 Apr  
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