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50 American-Eurasian Journal of Sustainable Agriculture, 7(2): 50-53, 2013 ISSN 1995-0748

ORIGINAL ARTICLE A Goal Programming Approach for Rubber Production in Malaysia Nasruddin Hassan, Hazwa Hanim Mohamed Hamzah, Siti Maisarah Md Zain School of Mathematical Sciences, Faculty of Science and Technology Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia Nasruddin Hassan, Hazwa Hanim Mohamed Hamzah, Siti Maisarah Md Zain: A Goal Programming Approach for Rubber Production in Malaysia ABSTRACT Rubber industry is one of the main contributors to Malaysia’s economic growth. Rubber estates and smallholdings are major sources of rubber production in Malaysia. Rubber estates are the focus of this study as private contributors involved in the development of the rubber. A study was conducted to maximize rubber production and planted areas of rubber by using the goal programming approach. This approach has been taken to find the optimal solution to the problem. Key words: Goal programming;optimal solution; rubber plantations Introduction Rubber industry is one of the most important economic crops in Malaysia since 1970s. Malaysia was the largest producer of rubber in the world until late 1980’s(Balsiger et al., 2000). Indonesia then took over as the biggest rubber cultivator in the world followed by Thailand (Ratnasingam et al., 2011). Even though export of natural rubber has been inconsistent but it is still one of the biggest contributors for Malaysian economy. Due to the development of industrial technologies, rubber plantation plays a new role to provide raw material for rubber based product and also for product made of rubber wood (Rafain et al., 2012). In the interval of 2000 to 2010,Malaysia produced the most natural rubber in 2006, which is believed due to the advance in rubber industries technologies and rise in the price of rubber. However, the production decreased after 2006 due to dwindling demands of rubber during the global economic crisis.As in October 2012, the production of rubber decreased by 2.6% compared to the previous year (Department of Statistics, 2012). One of the factors that result in the decrement of the production isthe decrement in the rubber plantation area. Hence, the authorities are promoting the optimization of land for rubber plantation in order to increase the production of rubber since rubber industry is still one of the biggest contributors to the country’s economy. The planted areas for rubber are operated by smallholders and estates. The number of estates in the country has been decreasing which in turn cause the planted area to continue its downtrend. In 2009, the total production of natural rubber fell 340,638 tonnes (-28.4%) to 857,562 tonnes compared to 198200 tonnes in 2007 (Department of Statistics, 2010). Goal programming is one of the models which have been developed to deal with the multiple objectives decision-making problems (Sen and Nandi, 2012b). The increasing popularity of goal programming and usefulness for decision-making policies has been aimed at optimizing agricultural land and other natural resources (Senand Nandi, 2012a; Hassan and Mohammad Basir, 2009; Hassan and Sahrin, 2012; Hassan and Abdul Halim, 2012; Hassan and Ayop, 2012; Hassan and Loon, 2012; Hassan et al., 2012a; Hassan et al., 2012b). Goal programming approach has been proposed for production optimization a few times previously. Leung and Ng (2007) used a preemptive goal programming model for production planning where three objectives are optimized hierarchically. Nja and Udofia (2009) formulated mixed-integer goal programming model for a flour producing company based on production times of three products. Kopanos et al. (2011) also developed a mixed discrete/continuous-time mixed-integer linear programming model for resource-constrained production planning in semicontinuousfood industries. Sen and Nandi (2012a) proposed a goal programming approach for rubber plantation planning in 7 years by taking into consideration on the number of tree survived within the time span, expenditure and fertilizer used. This paper discussed on suggestion to increase rubber production based on the increment of the area of rubber trees planted in estates for different states in Malaysia. Corresponding Author: Nasruddin Hassan, School of Mathematical Sciences, Faculty of Science and Technology UniversitiKebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia E-mail: [email protected]

51 Am.-Eurasian J. Sustain. Agric. 7(2): 50-53, 2013

A goal programming model with two objectives is developed to optimize the rubber planted areas and its production. LINDO 6.1 software is used to execute this model based on data of 2010.

Methodology: The general form of a goal programming problem can be expressed as Minimize

subject to

such that

where : the amount by which goal is overachieved : the amount by which goal is underachieved : the corresponding weights of the over/under achievement variables. The first goal is to maximize rubber productionfor each state group (Johor, Kedah and Perlis, Kelantan and Terengganu, Melaka, Negeri Sembilan, Pahang, Perak, Sabah and Selangor) by increasing the production up to 10%. The second goal is to maximize rubber planted area in the state groups where the total area is aimed to reach 50000 hectare. There will be no decrement in each of the planted area. However, because of the difficulty on acquiring data, the scope of this paper is limited to increase the planted area without covering the economic perspective.The weights are assigned based on the percentage of rubber production per area in each state group. Table 1: Planted areas and production Rubber planted areas State (hectare) Johor 5592 Kedah & Perlis 11675 Kelantan & Terengganu 8559 Melaka 1452 Negeri Sembilan 9386 Pahang 7354 Perak 3344 Selangor & Sabah 2378

Production (tonne) 7428 12793 7247 2003 13304 7837 4374 1785

Production per area (tonne/hectare) 1.32833 1.09576 0.84671 1.37948 1.41743 1.06568 1.30801 0.75063

Weight 0.14 0.12 0.09 0.15 0.15 0.12 0.14 0.08

Target Production (tonne) 8170.8 14072.3 9414.9 2203.3 14634.4 8620.7 4811.4 1963.5

Decision variables refers to the rubber planted area in hectare, where the index refers to the state groups (Johor, Kedah & Perlis, Kelantan & Terengganu, Melaka, Negeri Sembilan, Pahang, Perak, Sabah & Selangor). The formulation for this goal programming model can then be expressed as Minimize

such that (Total production in tonnes)

52 Am.-Eurasian J. Sustain. Agric. 7(2): 50-53, 2013

(Total rubber planted area) (Production per areas for state groups )

(No decrement in each of the planted area)

Results and Discussion The values obtained for the underachievement variables are

= 0 for all

. This implies that

there is no decrement in planted areas. On the other hand, the values of the overachievement variables are . For

0 for

, the values of

=

are given in the fourth column of Table 2. Hence it is

suggested that each state groups increase their planted areas by their corresponding and are 0.1 and 4714.00. These suggest that yielding values in the third column.The values of there is a production surplus of 0.1 tonne from the aspired value of 62448 tonnes and that the total planted area exceeded the target value of 50000 hectares by 4714 hectares. Table 2: Deviation from goals State Johor Kedah & Perlis Kelantan & Terengganu Melaka Negeri Sembilan Pahang Perak Selangor & Sabah

Rubber planted areas in 2010 (hectare) 5592 11675 8559 1452 9386 7354 3344 2378

Suggested rubber planted area (hectare) 6151.18 12842.50 9414.91 1597.20 10324.60 8089.39 3678.41 2615.80

559.18 1167.50 855.91 145.20 938.60 735.39 334.41 237.80

0 0 0 0 0 0 0 0

Conclusion: This model shows that rubber production can be increased by increasing the area for rubber plantation. However, there are other factors that can affect the production such as climate, rubber trees conditions, number

53 Am.-Eurasian J. Sustain. Agric. 7(2): 50-53, 2013

of workers, development of industrial technologies and sufficient budget. Therefore it is suggested that these factors be taken into consideration for further research. Acknowledgement We are indebted to Universiti Kebangsaan Malaysia for funding this research under the grant UKM-GUP2011-159. References Balsiger, J., J. Bahdan, A.Whiteman, 2000. The utilization, processing and demand for rubberwood as a source of wood supply.APFC-Working Paper No.PFSOS/WP/50. Bangkok: FAO Department of Statistics, 2012. Monthly Rubber Statistics. Putrajaya: Department of Statistics. Department of Statistics, 2010. Annual Rubber Statistics. Putrajaya: Department of Statistics. Hassan, N. & Z. Ayop, 2012. A Goal Programming Approach for Food Product Distribution of Small and Medium Enterprises.Advances in Environmental Biology, 6(2): 510-513. Hassan, N. & B. Abdul Halim, 2012. Mathematical Modelling Approach to the Management of Recreational Tourism Activities at Wetland Putrajaya.SainsMalaysiana,41(9): 1155-1161(in Malay). Hassan, N. & L.L. Loon, 2012. Goal Programming with Utility Function for Funding Allocation of a University Library. Applied Mathematical Sciences, 6(110): 5487-5493. Hassan, N. & S.B. Mohammad Basir, 2009. Goal Programming Model for Scheduling Political Campaign Visits in Kabupaten Kampar, Riau, Indonesia. Journal of Quality Measurement and Analysis, 5(2): 99-107 (in Malay). Hassan, N., S. Safiai, N.H. Mohammad Raduan & Z. Ayop, 2012a.Goal Programming Formulation in Nutrient Management for Chilli Plantation in Sungai Buloh Malaysia.Advances in Environmental Biology, 6(12): 4008–4012. Hassan, N. & S. Sahrin, 2012. A Mathematical Model of Nutrient Management for Pineapple Cultivation in Malaysia.Advances in Environmental Biology, 6(5): 1868-1872. Hassan, N., L.W. Siew & S.Y. Shen, 2012b. Portfolio Decision Analysis with Maximin Criterion in the Malaysian Stock Market. Applied Mathematical Sciences, 6(110): 5483-5486. Kopanos G.M., L. Puigjaner & M.C. Georgiadis, 2011. Resource-constrained production planning in semicontinuous food industries.Computers and Chemical Engineering, 35(12): 2929-2944. Leung S.C.H. & W.I. Ng, 2007.A goal programming model for production planningof perishable products with postponement.Computers & Industrial Engineering, 53(3): 531-541. Nja M.E., G.A. Udofia, 2009. Formulation of the Mixed-Integer Goal Programming Model for Flour Producing Companies. Asian Journal of Mathematics and Statistics, 2(3): 55-64. Rafain A., D. Zaimah & N.M. Mohd, 2012. MembinaSenarioMasaHadapanIndustriGetah Malaysia: Road Map dan Pemacu UtamaIndustri. Prosiding PERKEM VII, 1: 27-43. Ratnasingam J., F. Ioras & W. Lu, 2011.Sustainability of the Rubberwood Sector in Malaysia.Not Bot HortiAgrobo 39(2): 305-311. Sen N. & M. Nandi, 2012a. A Goal Programming Approach to Rubber Plantation Planning in Tripura.Applied Mathematical Sciences, 6(124): 6171-6179. Sen, N. & M. Nandi, 2012b. Goal Programming, its Application in Management Sectors–Special Attention into Plantation Management: A Review. International Journal of Scientific and Research Publications, 2(9): 1-6.