Development Strategy for the Master Plan of Maize Commodities

Supply Chain Network Infrastructure in Madura, Indonesia

Abdul Azis Jakfar

, Muhammad Syarif

, Rachmad Hidayat

, Sabarudin Akhmad

, Kukuh Winarso

and Anis Arendra

Departement of Agro-industrial Technology, Faculty of Agriculture,, University of Trunojoyo Madura, Indonesia

Departement of Management, Faculty of Economics and Business,, University of Trunojoyo Madura, Indonesia

Industrial Engineering Departement, University of Trunojoyo Madura, Indonesia

Mechanical Engineering Departement, University of Trunojoyo Madura, Indonesia

kukuhutm@gmail.com, anis.arendra@trunojoyo.ac.id

Keywords: Food Supply Chain Network, Greenfield Analysis Method, Maize Farmer.

Abstract: The problems faced by maize farmers in Madura are (1) Poor handling of maize post-harvest; (2)

Transportation operating costs are expensive; (3) Delay in delivery time. All of these problems can be resolved

using the Food Supply Chain Network (FSCN) method. Therefore, it is necessary to develop the FSCN model

for maize harvest in Madura. Supply chain performance is measured to determine how optimal marketing

activities are carried out by members of the supply chain. The Food Supply Chain Network consists of four

elements, which include Network Structure, Chain Business Processes, Chain Management and Chain

Resources. This study aims to develop the distribution model for the maize supply chain in Madura, East-Java

using the FSCN framework, developing the performance model for the maize supply chain network in

Madura. The results can be used as a recommendation to develop an optimal maize supply chain master plan

in Madura. The development of a maize supply chain model can be assessed using the FSCN framework

which consists of supply chain targets, supply chain structures, supply chain management, supply chain

resources, supply chain business processes, and supply chain performance. Meanwhile, the optimization

model is solved using the Greenfield Analysis method.

1 INTRODUCTION

The logistics of the food supply chain plays an

important role in the continuity of business

performance in the food sector. After several periods,

the food business sector prioritizing responsiveness,

they now experience vulnerability to supply chain

threats (Bloemhof et al., 2015). The food supply chain

network is a framework and tool for the food sector

to take steps to change its operational practices. The

food supply chain network provides a clear and

concise overview of the current state of performance

indicators for the food sector in corporate

sustainability strategies, supply chain reformulation

strategies currently applied in practice for continuous

improvement(Sembiring Meliala et al., 2019).

The food product industry still focuses on delivery

time to consumers, high quality products and low

production costs(Banasik et al., 2017). In order to

remain competitive, FSCN is expected to be able to

adopt new technologies that can improve the

performance of food product companies.

Performance improvement can be started with a

quantitative assessment of economic, selection of

alternative technologies, production options, and

environmental benefits(Ferreira and Arantes, 2015).

Meanwhile, the adoption of new technology is

expected to increase performance levels and facilitate

managerial decision making.

The agribusiness sector plays an important role in

the national economy, being one of the main

contributors to Gross Domestic Product (GDP) in

many developing countries including Indonesia, even

the share contribution of this sector in GDP reaches

as much as 50%(Wajszczuk, 2016). In contrast to the

other economic sectors, apart from the need for

efficient logistics, food distribution must ensure safe

delivery of food to end consumers(Akhmad et al.,

2019). In addition, the transportation of food

products, especially agricultural products, requires

Jakfar, A., Syarif, M., Hidayat, R., Akhmad, S., Winarso, K. and Arendra, A.

Development Strategy for the Master Plan of Maize Commodities Supply Chain Network Infrastructure in Madura, Indonesia.

DOI: 10.5220/0010307600003051

In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 277-285

ISBN: 978-989-758-501-2

 2022 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

277

the application of a special logistics infrastructure. To

overcome this problem, it is necessary to develop a

distribution system or special logistics for maize crop

commodity. This distribution network system is

commonly called the Food Supply Chain Network. It

is hoped that the Food Supply Chain Network

specifically for maize crop can increase National

Gross Domestic Product.

2 ACTUAL CONDITIONS OF

MAIZE FARMING IN MADURA

An initial survey of maize farming in Madura

revealed that maize farmers in Madura had several

problems: Harvested maize cannot be sold to the

maize processing mill because it does not meet the

requirements for the quality of moisture content and

levels of afla toxin. The feed processing mill also

demands sustainable large quantities while maize

production in Madura is only twice a year. Other

problems faced by maize farmers in Madura are (1)

Low human resource or farmer skills; (2) Low quality

of maize seeds; (2) Low productivity; (3) The

quantity of maize shipments not as expected; (4)

Inadequate agricultural equipment; (5) Poor handling

of maize post-harvest; (6) Transportation operating

costs are expensive; (7) Unclear payment system (8)

Delay in delivery.

The description above indicates the maize supply

chain operation in Madura was poor. It is necessary

to improve the supply chain in its implementation so

that the marketing supply chain is more optimal in

delivering products from producers to

consumers(Dellino et al., 2015), as well as consumers

more easily to get products from producers. Madurese

maize must have high competitiveness in order to

compete with imported maize. Competitiveness is

influenced by the effectiveness and efficiency of

supply chain performance(Berti and Mulligan, 2016).

So it can be concluded that the supply chain plays an

important role in winning the market competition for

agricultural products(Akhmad et al., 2020) and

(Winarso and Rohim, 2019). To win market

competition, it is necessary to optimize distribution

channels in the supply chain and added value to

institutions related to corn marketing. Therefore,

research on the development strategy of a Food

Supply Chain Network Model for optimizing Madura

maize distribution channels by using the

metaheuristic method is necessary.

3 RESEARCH PROBLEMS AND

THE OBJECTIVES

The problems faced in developing the Madura maize

business are as follows: (a) How is the mid-range

master plan for the Madura maize business? (b) How

to prepare supporting facilities, especially facilities

for the distribution of maize, from farmers to

consumers?

Table 1: The farmer’s location data snippets.

Farmer

Coordinate Village Sub-

District

Latitude Longitude

Tunas Muda -6.951992 112.847800

Arosbaya Arosbaya Bangkalan

Renggujeng Tani

-6.949366 112.837712

Arosbaya Arosbaya Bangkalan

Omber Ramah Luhur Manis

-6.979578 112.831809

Balung Arosbaya Bangkalan

Makmur I -7.003364

112.848950

Batonaong Arosbaya Bangkalan

Makmur II -7.007311 112.854367

Batonaong Arosbaya Bangkalan

Makmur III -7.015500 112.856059

Batonaong Arosbaya Bangkalan

Makmur IV -7.000672

112.847046

Batonaong Arosbaya Bangkalan

Tani Sejahtera

-7.003527

112.858652

Batonaong Arosbaya Bangkalan

Gerbung -7.005282

112.851820

Batonaong Arosbaya Bangkalan

Berbeluk Timu

-6.964212 112.852019

Berbeluk Arosbaya Bangkalan

Pancor Emas -6.957035

112.863154

Berbeluk Arosbaya Bangkalan

… … … … … … …

1628

Cinta Damai Nonggunong -7.116775 113.886881

Tanjung Pragaan Sumenep

1629

Indah Jaya Tanjung -7.126045 113.890709

Tanjung Pragaan Sumenep

1630

Sekar Wangi Tanjung -7.128898 113.890878

Tanjung Pragaan Sumenep

1631

Karya Usaha Nonggunong -7.120453 113.882070

Tanjung Pragaan Sumenep

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The general objectives of this study are to prepare

a master plan for the Madura maize business 2020-

2030, while the specific objectives are: (1) Develop a

Madura maize Supply Chain Network model. (2)

Determine the location of the Aggregation warehouse

in the Madura maize distribution line. (3) Determine

the minimum distribution channel for Madura maize.

4 RELATED WORK

Supply Chain Management (SCM) has been part of

the corporate management agenda since the 1990s,

especially in the retail industries and

manufacturing(Chopra and Meindl, 2013). More

recently, interest in SCM has also grown in the agri-

food industry, in developed and developing

countries(Bustos et al., 2017). Bloemhof et al., (2015)

and Banasik et al., (2017) state that agrifood company

executives realize that the successful coordination,

integration, and management of key business

processes in the supply chain network will determine

the success of their market competitiveness.

Sustainable Food Supply Chain Management

(SFSCM) refers to all forward processes in the food

chain, such as material procurement, production and

distribution, as well as reverse processes for

collecting and reprocessing used and unused

products.

Parallel or sequential processes can occur

simultaneously in the food supply chain so that more

than one business process in the food supply chain

network can be identified(Cruz and Rosado da Cruz,

2019). For example, the business process of maize for

animal feed is channeled from farmers to various

parties such as middleman traders and then forwarded

to the final consumer. In the flow process, the supply

chain members involved carry out the business

process as needed. Suppose a middleman trader

carries out a different process with regard to maize

being sent to the livestock industry and maize to be

sent to the food industry.

The diversity of supply chain structures can be

analyzed qualitatively, including in analyzing the

resulting performance. Qualitative supply chain

performance analysis needs to be supported by

quantitative performance measures in order to

produce more measurable and objective performance

results. As an integrated process between members

who are joined, supply chain performance

measurement needs to use a certain approach. Supply

chain performance is defined as the break event point

between consumers and stakeholders where both

requirements have been met with the relevance of the

attributes of performance indicators over time.

Increasing the added value of primary agricultural

commodities is one step in order to increase farmers'

income, especially in rural areas(Desiana and

Aprianingsih, 2018).

A supply chain that is incorporated in a complex

network is called the Food Supply Chain Network. To

analyze a complex supply chain, a term that can

describe the supply chain, the parties involved, the

process, the product, the resources, management, the

relationship between attributes and other things is

defined. Network and chain management is the

coordination of the network management structure

that facilitates related institutions in the supply chain

to make decisions using chain resources so that the

objectives of FSCN can be achieved(Taghikhah et al.,

2020).

Asmarantaka et al., (2018) stated that the

characteristics of agricultural products are broadly

large volume, take up large space, and perishable. It

can be concluded that the characteristics of

agricultural supply chains in particular are: perishable

products; short shelf life of products; production

depending on the season, harvest and famine; long

production time; need storage handlers; the quality

and quantity of production is affected by weather and

season, plant diseases and pests; and consumer

demand for food safety(Xue et al., 2019).

Characteristics like these need special handling in

Supply Chain Management (Dou et al., 2020).

5 RESEARCH METHODS

Supply chain management for agricultural products

represents the management of the entire production

process from plantation, processing activities, to

distribution, marketing, until the desired product

reaches consumers. Agricultural supply chain

management is different, more complex, probalistic

and dynamic compared to non-agricultural supply

chain management. The differences are in the

characteristics of perishable agricultural products and

varying product sizes, production processes that

depend on seasons and climate, and changes in

consumer behavior towards food safety.

As a description of the supply chain scheme, each

actor is in the network layer that has at least one

supply chain. Each supply chain usually has suppliers

and consumers at the same time and at different times.

Other actors in the network affect the performance of

the supply chain. Each actor may enforce different

rules in different chains and cooperate with different

chains which may become competitors in other

Development Strategy for the Master Plan of Maize Commodities Supply Chain Network Infrastructure in Madura, Indonesia

279

chains. Therefore, a supply chain analysis that is

evaluated in the context of a complex network in the

food supply chain is called the Food Supply Chain

Network (FSCN).

5.1 Research Time and Location

The research begins by identifying how the Madurese

maize distribution channels flow, through in-depth

interviews with the farmers and the stakeholders

involved in the Madura maize distribution channel.

The research was conducted in 4 Districts in Madura,

namely: Bangkalan, Sampang, Sumenep and

Pamekasan. The research was carried out in May -

December 2020. All the required data and

information are obtained through the following steps:

 Observation, making direct observations of the

socio-economic conditions of the community

and maize farmer groups. So that we get an

overview of the patterns of life of the maize

farming community.

 Interviews, conducting a series of in-depth

interviews with key informants. Interview

activities were carried out in depth by adhering

to the guidelines so that the information

obtained was focused on the research focus. The

interview activity was carried out in a friendly

atmosphere in order to obtain in-depth

information.

 Focus Group Discussion, conduct a series of

discussions with related stakeholders, including

the local community, corn farmers, local

governments. This method was effective in

obtaining an overview of the problems faced

and leading to the formation of the Madura

FSCN model.

Table 2: The grouping location center of maize farmers.

District Sub-District

Coordinate

Latitude Longitude

Bangkalan

Arosbaya -6,980555 112,847817

Bangkalan -7,020299 112,749148

Blega -7,136924 113,035548

Burneh -7,021733 112,819757

Galis -7,085200 112,956128

Kamal -7,133042 112,727939

Klampis -6,929446 112,853194

Kokop -6,974531 113,042999

Konang -7,048266 113,063518

Labang -7,146231 112,815210

Modung -7,161283 112,989492

Sepul

-6,918702 112,976240

Socah -7,080662 112,715850

Tanah Merah -7,063186 112,877926

Tanjungbumi -6,902865 113,078371

Tragah -7,094972 112,827788

Sampang Sampang -7,203033 113,240466

District Sub-District

Coordinate

Latitude Longitude

Camplong -7,187072 113,342021

Omben -7,107241 113,340859

Karang

Penang

-7,028188 113,345422

Torjun -7,160078 113,205537

Pangarengan -7,203075 113,191925

Jrengi

-7,117839 113,140954

Sreseh -7,213517 113,096182

Tambelangan -7,038848 113,161989

Kedungdung -7,073639 113,228110

Robatal -6,996763 113,298816

Ketapang -6,918296 113,298476

Banyuates -6,912160 113,176141

Sokobanah -6,917964 113,427267

Pamekasan

Kadu

-7,086042 113,568466

Palengaan -7,083073 113,458160

Pagantenan -7,042138 113,473011

Pakong -7,042297 113,569862

Proppo -7,133964 113,416850

Pademawu -7,188069 113,508544

Pasean -6,919443 113,589822

Pamekasan -7,153247 113,467242

Galis -7,144900 113,537533

Larangan -7,119570 113,560821

Batumarma

-6,945935 113,494126

Tlanakan -7,188081 113,440215

Waru -6,963174 113,560993

Sumenep

Ambunten -6,910381 113,769384

Batang-

atang

-6,960218 114,018546

Batuan -7,019821 113,811711

Batuputih -6,902189 113,905390

Bluto -7,098494 113,788739

Dungke

-6,985624 114,057068

Ganding -7,062776 113,705115

Gapura -6,996853 113,945768

Gulu

-gulu

-7,028275 113,616856

Sumenep -7,019545 113,857827

Lenteng -7,039805 113,744712

Manding -6,958953 113,879039

Pasongsongan -6,983611 113,697441

Pragaan -7,096747 113,721167

5.2 Mapping the Location of Farmers

and Farmers Grouping

We obtained the initial data for maize farmers from

the Government of the Food Crops, Horticulture and

Plantation Service. Then, we completed data on

maize farmer land area, farmer annual production

tonnage, geotagging location for each farmer, by site

visiting each corn farmer's location. The total number

of farmer groups was 1631 farmer groups. A total of

1631 farmer groups are the research objects discussed

in this study. Mapping data snippets are shown in

table 1.

Farmer grouping designs to facilitate the supply

chain structure. Grouping is done to select a

communal warehouse point that represents farmer

CESIT 2020 - International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies

280

groups in each village. Grouping uses the Center of

Gravity method. The data required for grouping are:

(1) The volume of maize transported from the point

of the farmer group to the communal warehouse. (2)

Transportation costs. (3) Coordinate of maize farmer

and communal warehouse points. The calculation of

location coordinates uses the following

equation(Uitenbroek, 2003):

 The coordinates of the selected communal

warehouse location.

x=

∑













∑









;y=

∑













∑











 The distance between maize farmers location to

the candidate communal warehouse location.

Dn = Σ





𝑥𝑋





²



𝑦𝑌









 The transport cost for maize aggregation

TC =

∑

𝑉



𝐷



𝐶









where

x = latitude of the selected location as the

communal warehouse.

y = longitude of the selected location as the

communal warehouse.

i = index of maize farmer members.

n = iteration index

= latitude of the i

maize farmer.

= longitude of the i

maize farmer.

= Tonnage of the i

farmers' maize

production.

= Transportation rate of location I

= Distance of the i

farmer member to the

selected communal warehouse location in n

iteration.

TC = Total cost.

5.3 Determining the Location Point of

Aggregation Warehouse

The location point of aggregation warehouse is

determined using the Greenfield Analysis method,

then corrected using the Network Optimization

method. The data required for the Greenfield

Analysis method are:

 The coordinates of the farmer groups

 Maize crop tonnage for each farmer group

 The number of aggregation warehouses

required

After obtaining the location of the aggregation

warehouse placement using the Greenfield Analysis

method, it is continued to improve the coordinate

points using the Network Optimization method. This

method is conducted by adding alternative

aggregation warehouses as a comparison to the initial

warehouse location, as well as additional maize

processing mill location data to be able to run this

method.

Table 3:The moving average forecast table for maize production.

Periode Year

MaizeProduction(Ton/year)

Bangkalan Sampang Pamekasan Sumenep Total Forecast

1 2007 140,984 141,679 76,339 298,880 657,882

2 2008 148,463 176,095 92,443 314,855 731,856

3 2009 151,933 116,462 114,856 353,022 736,273

4 2010 159,748 120,285 110,494 529,258 919,785 761,449

5 2011 174,455 113,265 147,192 310,056 744,968 783,221

6 2012 120,993 161,738 150,308 420,795 853,834 813,715

7 2013 127,527 108,645 95,338 359,689 691,199 802,447

8 2014 136,712 95,332 113,245 324,330 669,619 739,905

9 2015 132,884 98,332 93,793 396,067 721,076 733,932

10 2016 144,752 124,145 135,993 339,254 744,144 706,510

11 2017 132,586 149,219 187,672 325,384 794,861 732,425

12 2018  748,127

13 2019  754,889

14 2020  757,575

15 2021  748,254

16 2022  752,249

Development Strategy for the Master Plan of Maize Commodities Supply Chain Network Infrastructure in Madura, Indonesia

281

6 RESULTS AND DISCUSSION

The harvest of Madura maize by farmer groups is

usually sold to middlemen, farmers do not get a price

deal from the company but rather from the

middleman. The middlemen offer prices ranging from

3000 to 3500 IDR / kg for whole corn and IDR. 4000

- Rp. 4200 / kg for shelled corn. Middlemen sell corn

to producers in the form of shelled corn for around

5000 to 5500 IDR / kg. Farmers sell to middlemen,

because they give cash in cash.

6.1 Data on Maize Farmers in Madura

After conducting interviews with the Government of

the Food Crops, Horticulture and Plantation Service

in each four District. Data were obtained consisting

of farmer groups, cultivated land area and annual

production crop. The total number of farmer groups

was

1631 farmer groups.

6.2 Maize Farmer Grouping

We conducted site surveys in determining the point

of the maize farmer groups and recapitulated the

latitude and longitude coordinate data of each farmer

group. The determination of the coordinates of each

farmer group is conducted with the Google Maps

application on a Smartphone device. This data is

needed in the calculation of the Center of Gravity

using a mathematical model (1-3). The grouping

results of maize farmers can be seen in table 2.

6.3 Forecast of Maize Production

The maize production data that we have obtained was

only up to 2017. Meanwhile, the construction and the

use of aggregated warehouses is projected for 2022.

Therefore, forecasts of maize production are carried

out until 2022. Forecasts are carried out using the 4-

period moving average method. The moving average

forecast table for maize production is shown in Table

3. Based on the specified warehouse capacity and

maize corp, we set the warehouse capacity to be

200,000 tons/warehouse. Based on forecasting in

2022 of 752,249 tons/year with a warehouse capacity

of 200,000 tons, it can be determined that 4

aggregation warehouses will be needed.

6.4 Location Point of Aggregation

Warehouse

Based on the GFA method using Anylogistix

software, the coordinates of each aggregation

warehouse were found, along with the coverage area

of the maize farmer groups. A summary of the

aggregation warehouse contained in the table 4.

Based on the forecast that has been done, it is

estimated that in 2022 the Madura maize corp will be

752,249 tons/year. It has been determined the number

of warehouses of 4 with a capacity of 200 tons each.

After determining the number of aggregation

warehouses and the center point of farmers in 57

farmer groups, then the next step is to determine the

coordinates of the aggregation warehouse. Here we

use Anylogistix software with the Greenfield

Analysis (GFA) method. A summary of the

aggregation warehouse contained in the table 4 and

figure 1.

Figure 1: The location of each aggregation warehouse and

the coverage area.

Table 4: The coordinates of each aggregation warehouse

and the coverage area.

Aggregation

Warehouse

Latitude Longitude

Warehouse

Coverage

GFA DC 1 -7,055735 112,862790

Blega

Tragah

Arosbaya

Tanah Merah

Sepulu

Kamal

Labang

Bangkalan

Burneh

Galis

Klampis

Kokop

Socah

Modung

GFA DC 2 -6,989219 113,870459

Batuan

Sumenep

Ambunten

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Aggregation

Warehouse

Latitude Longitude

Warehouse

Coverage

Batang-batang

Gapura

Lenteng

Manding

Dungkek

Bluto

Batuputih

GFA DC 3 -7,078855 113,220558

Banyuates

Tambelangan

Robatal

Tanjungbumi

Omben

Karang

Penang

Camplong

Kedungdung

Pangarengan

Jrengik

Konang

Sampang

Torjun

Sreseh

Ketapang

GFA DC 4 -7,068322 113,548234

Galis

Ganding

Waru

Sokobanah

Guluk-guluk

Batumarmar

Proppo

Pasean

Pagantenan

Pasongsongan

Tlanakan

Palengaan

Pamekasan

Pakong

Pademawu

Larangan

Pragaan

Kadur

6.5 Correction of Aggregation

Warehouse Location

The GFA method provides coordinate location for

aggregation warehouses along with the coverage of

farmer groups, where the resulting coordinate was the

optimal point based on maize crop tonnage and the

distance between aggregation warehouse coordinates

to farmer groups. The calculated distance was the

euclidian distance between coordinates, not based on

the actual distance. Therefore it is necessary to

improve using the Network Optimization (NO)

method using the same software, Anylogistix.

Improvement is done by providing alternative

warehouse points which are then compared with

warehouse points generated by the GFA method. The

NO method requires the coordinates of the maize

processing mill as the final destination for the maize

to be distributed. We set the maize processing mill

PT. Charoen Pokphand Indonesia Tbk. In improving

the placement of aggregation warehouse points with

the NO method, we set 3 alternative warehouses for

each initial warehouse as a comparison to the

determination of 4 warehouses.

Figure 2: The location of GFA DC1 (alt2) aggregation

warehouse and the coverage area.

The coordinates of the GFA DC 1 warehouse were

obtained by the GFA method at -7.056, 112.863. As

candidates, alternative warehouse 1 was assigned at

coordinates -7.084, 112.876; alternative warehouse 2

at coordinates -7.073, 112.84; alternative warehouse

3 at coordinates -7.079, 112.855. The results of the

NO method show that the best warehouse location is

in alternative warehouse 2, namely GFA DC 1 (alt 2)

at the coordinate point -7.073, 112.84. The warehouse

location on the map can be seen in the figure 2.

Figure 3: The location of GFA DC2 (alt2) aggregation

warehouse and the coverage area.

The coordinates of the GFA DC 2 warehouse were

obtained by the GFA method at -6.989, 113.87. As

candidates, alternative warehouse 1 was assigned at

coordinates -7.001, 113.871; alternative warehouse 2

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283

at coordinates -7.003, 113.849; alternative warehouse

3 at coordinates -7.013, 113.859. The results of the

NO method show that the best warehouse location is

in alternative warehouse 2, namely GFA DC 2 (alt 2)

at the coordinate point -7.003, 113.849. The

warehouse location on the map can be seen in the

figure 3.

Figure 4: The location of GFA DC3 (alt3) aggregation

warehouse and the coverage area.

The coordinates of the GFA DC 3 warehouse were

obtained by the GFA method at -7.079, 113.221. As

candidates, alternative warehouse 1 was assigned at

coordinates -7.08, 113.208; alternative warehouse 2

at coordinates -7.09, 113.253; alternative warehouse

3 at coordinates -7.072, 113.184. The results of the

NO method show that the best warehouse location is

in alternative warehouse 3, namely GFA DC 3 (alt 3)

at the coordinate point -7.072, 113.184. The

warehouse location on the map can be seen in the

figure 4.

Figure 5: The location of GFA DC4 aggregation warehouse

and the coverage area

The coordinates of the GFA DC 4 warehouse were

obtained by the GFA method at -7.068, 113.548. As

candidates, alternative warehouse 1 was assigned at

coordinates -7.045, 113.59; alternative warehouse 2

at coordinates -7.046, 113.57; alternative warehouse

3 at coordinates -7.045, 113.59. The results of the NO

method show that the best warehouse location is in

initial GFA DC 4 warehouse at the coordinate point

-7.068, 113.548. The warehouse location on the map

can be seen in the figure 5.

7 CONCLUSION AND THE

FUTURE WORK

It has been determined the coordinates of the

aggregation warehouse using the GFA method and

corrections to find the optimal point using the NO

method. Based on the calculation using these two

methods, the coordinates of the proposed aggregation

warehouse locations are obtained. Following in table

5 and figure 6, a summary of the coordinates location

of aggregation warehouse. The location of the

aggregation warehouse is located in the highlands. In

fact, this location makes transportation difficult.

Further research can be carried out by adding priority

constraints for coastal areas for the location of

aggregation warehouses.

Table 5: The coordinates of the proposed aggregation

warehouse locations.

Aggregation

Warehouse

Coordinate Location

Latitude lon

itude

GFA DC1

(

alt2

)

-7.073 112.840

GFA DC2 (alt2) -7.003 113.846

GFA DC3 (alt3) -7.072 113.184

GFA DC4 -7.068 113.548

Figure 6: The coordinates of the proposed aggregation

warehouse locations.

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