Proceedings of the 1st International Conference on Business, Law And Pedagogy, ICBLP 2019, 13-15 February 2019, Sidoarjo, Indonesia

Research Article

Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization

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  • @INPROCEEDINGS{10.4108/eai.13-2-2019.2286495,
        author={Muhammad Yusak Anshori and Teguh Herlambang and Dinita Rahmalia},
        title={Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization},
        proceedings={Proceedings of the 1st International Conference on Business, Law And Pedagogy, ICBLP 2019, 13-15 February 2019, Sidoarjo, Indonesia},
        publisher={EAI},
        proceedings_a={ICBLP},
        year={2019},
        month={10},
        keywords={linear integer programming constrained optimization genetic algorithm particle swarm optimization production planning optimization},
        doi={10.4108/eai.13-2-2019.2286495}
    }
    
  • Muhammad Yusak Anshori
    Teguh Herlambang
    Dinita Rahmalia
    Year: 2019
    Genetic Algorithm and Particle Swarm Optimization on Fertilizer Production Planning Optimization
    ICBLP
    EAI
    DOI: 10.4108/eai.13-2-2019.2286495
Muhammad Yusak Anshori1,*, Teguh Herlambang2, Dinita Rahmalia3
  • 1: Management Department, University of Nahdlatul Ulama Surabaya
  • 2: Information System Department, University of Nahdlatul Ulama Surabaya
  • 3: Mathematics Department, University of Islam Darul Ulum Lamongan
*Contact email: yusak.anshori@unusa.ac.id

Abstract

Production planning is the important part of controlling the cost spent by the company. In this research, production planning model is linear integer programming model with constraints : production, worker, and inventory. Linear integer programming as optimization problem can be solved by exact method like branch and bound, cutting plane or heuristic method. In this paper, we use heurisitic like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving production planning optimization in approaching. GA uses natural selection process of chromosomes while PSO is inspired by the behavior of flocks of birds, swarm of insects, or school of fish. The simulations show that both GA and PSO can find optimal solution of fertilizer production planning in approaching.