A hybrid model of system dynamics and genetic algorithm to increase crude palm oil production in Malaysia

Palm oil industry in Malaysia is facing a stagnant growth in terms of crude palm oil (CPO) production as compared to Indonesia due to three issues namely (i) the scarcity of plantation area, (ii) labour shortage, and (iii) the rising demand from palm-based biodiesel industry. Focusing on these issues, previous studies have been adopted various approaches. However, these non-hybridized methods have some shortcomings and can be improved by hybridization method. Hence, the objective of this research is to determine the optimal policy options to increase CPO production in Malaysia. In this research, a hybrid model of system dynamics (SD) and genetic algorithm (GA) was developed to determine the optimal policy in increasing the CPO production in Malaysia. Five policy variables namely mechanization adoption rate, average replanting, biodiesel mandates in transportation, industrial and 4 other relevant sectors were examined to determine optimal policy values. These five policy variables were tested in three scenarios: year 2017, year 2020, and in phases until 2050. From all the scenarios, the phase optimization emerged as the most effective in producing suitable policy variable values in order to obtain the best possible value of CPO production in year 2050 up to 20 GA population runs. The hybrid of SD-GA through phase optimization process is capable to recommend policies that are plausible to be implemented to avoid unwarranted shock to the industry. Furthermore, the hybrid model provides the ability of identifying the policy variables related to the objective function at any specific time line. From the managerial perspectives, this research helps the stakeholders in palm oil industry towards making a better future investment decision

Maximizing crude palm oil production in Malaysia: a search for an optimal policy using system dynamics and genetic algorithm approach

Palm oil industry in Malaysia is experiencing a stagnant crude palm oil (CPO) production and has been lagging as compared to Indonesia. This situation can jeopardize Malaysia’s position in world palm oil marker since Malaysia needed to secure its export revenue and fulfilling increasing demand of palm oil both locally and globally in the future. The factors that influence the CPO production are many. Among others are the scarcity of plantation area, labour shortage, and demand from palm-based biodiesel industry. This study presents an integrated of system dynamics (SD) and genetic algorithm (GA) (SD-GA) model to find the optimal policy to improve CPO production in Malaysian palm oil industry. SD offers the platform to evaluate and to test policy while GA facilitate the process of searching the best solutions to achieve the maximum CPO production in 2050. The proposed model has produced five optimal values for five policy variables namely average replanting rate, mechanization adoption rate, and biodiesel mandate in transportation, industrial and other sectors respectively. The best solution suggested that CPO replanting rate need to be increased to 251743.5 hectares per year to decrease the accumulation of ageing area by optimizing all these policy variables. This study is expected to help policy makers in designing related policies and drawing the road map towards improving CPO production in Malaysian palm oil industry