Pembangunan Model Pengoptimuman Pengeluaran Minyak Sawit dan Isirong Sawit Menggunakan Pendekatan Genetik Kabur

2012 ◽  
Author(s):  
Lily Amelia ◽  
Dzuraidah Abd. Wahab ◽  
Azmi Hassan
Keyword(s):  
Genetic Algorithm ◽  
Expert System ◽  
Palm Oil ◽  
Programming Model ◽  
Palm Kernel ◽  
Fuzzy Expert System ◽  
Crude Palm Oil ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Palm Oil Mill

Pengeluaran minyak sawit dan isirong sawit sering kali menghadapi masalah, antara lain kadar kehilangan minyak sawit dan isirong sawit yang tinggi semasa pemprosesan dan penggunaan sumber-sumber yang tidak optimum. Model pengoptimuman pengeluaran minyak sawit dan isirong sawit perlu direka bentuk untuk menyelesaikan permasalahan tersebut sehingga dapat memaksimumkan hasil, meminimumkan kos pengeluaran serta meminimumkan kehilangan minyak sawit dan isirong sawit. Model yang dibangunkan adalah gabungan antara model sistem pakar kabur dengan model pengaturcaraan pelbagai objektif. Pengoptimuman model dilakukan dengan menggunakan kaedah algoritma genetik. Model disimulasikan menggunakan data daripada sebuah kilang minyak sawit di Indonesia dan hasil kajian membuktikan pencapaian pengeluaran yang lebih baik serta kehilangan minyak sawit dan isirong sawit yang lebih kecil berbanding keadaan pengeluaran sedia ada di kilang minyak sawit tersebut. Kata kunci: Minyak sawit mentah, pengoptimuman pengeluaran, sistem pakar kabur, model pengaturcaraan pelbagai objektif, algoritma genetik The production of crude palm oil and palm kernel are frequented by problems, among others the high loss of crude palm oil and palm kernel during processing and the consumption of resources that are not optimised. An optimisation model for crude palm oil and palm kernel production has to be developed to solve these problems so as to maximise revenue, minimise production costs as well as to minimise palm oil and palm kernel losses. The developed model is an integration between fuzzy expert system models and multi objective programming model. Model optimisation is performed using the genetic algorithm method. The model was simulated using data from a palm oil mill in Indonesia and results from the study show that the model produces an optimum quantity of production and capable of reducing palm oil and palm kernel losses compared with the existing production conditions in the palm oil mill. Key words: Crude palm oil, production optimisation, fuzzy expert system, multi objective programming model, genetic algorithm

scholarly journals Synthesis of Fe and Zn Organic Fertilizer From Palm Oil Waste

2020 ◽  
Vol 3 (2) ◽  
pp. 57
Author(s):  
Lelita Rosanna Banjarnahor ◽  
Siti Rahmah ◽  
Marini Damanik ◽  
Moondra Zubir
Keyword(s):  
Palm Oil ◽  
Organic Waste ◽  
Positive Impact ◽  
Organic Fertilizer ◽  
Palm Kernel ◽  
Oil Industry ◽  
Crude Palm Oil ◽  
Kernel Oil ◽  
Palm Oil Mill ◽  
Fresh Fruit Bunches

Indonesia has been placed as the world's first producer of crude palm oil and crude palm oil.  In producing crude palm oil (CPO) and palm kernel oil (PKO), the palm oil industry relies heavily on processing fresh fruit bunches (FFB) at palm oil mills (POM) and is traded internationally. However, this process also produces solid organic waste [  i.e. empty bunches (EFB)], which reach up to 25 %% of FFB.  The analysis shows that the application of empty bunches as organic fertilizer has not been able to increase the amount of nutrients in palm oil leaves and increase palm oil production.  Application of palm oil mill effluent which is able to increase the amount of nutrients in palm oil, especially nitrogen and phosphate, and a positive impact to increase the production of oil palm plantations, especially on productivity (tons / ha).

A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

2021 ◽  
pp. 1-10
Author(s):  
Zhaoping Tang ◽  
Wenda Li ◽  
Shijun Yu ◽  
Jianping Sun
Keyword(s):  
Optimization Model ◽  
Programming Model ◽  
Ideal Point ◽  
Optimal Solution ◽  
Multi Objective ◽  
Emergency Rescue ◽  
Satisfaction Degree ◽  
Multi Objective Programming ◽  
Parameter Interval ◽  
Objective Programming

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

A Multi-Objective Programming Model of Dynamic Adjusting Flight Traffic

2014 ◽  
Vol 538 ◽  
pp. 127-133 ◽  
Author(s):  
Zhao Ning Zhang ◽  
Zhong Zhou Hao ◽  
Zheng Gao
Keyword(s):  
Load Balance ◽  
Optimal Allocation ◽  
Least Squares Method ◽  
Programming Model ◽  
Limiting Factors ◽  
Flight Delays ◽  
Traffic Demand ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

To alleviate the conflicts between the current flight traffic demand and the resource constraints of airspace, we need to improve the restrictions of flow allocation caused by the static air traffic flow allocation mode. The author analyzes the optimal allocation problem of dynamic adjusting flight flow and draws the conclusion that the problem should satisfy multiple targets, such as low flight delays, low flight cost and balancing the load of the route. Then consider a variety of limiting factors, such as the capacity of the route, flight planning, emergency situations, etc. Then establish multi-objective programming model of dynamic adjusting flight traffic. The objective function is determined by the flight cost, the flight delays and the value of the load balance. And the value of the load balance was first proposed according to the idea of least squares method. Then solve the model based on linear weighted technique. Finally the numerical result shows that the model can satisfy the multiple objectives and dynamic adjust the flight traffic optimally, that proves the rationality and validity of the model and the algorithm.

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