The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

Research Article

Cost Optimization of Supply Chain Distribution Model Using Genetic Algorithm

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  • @INPROCEEDINGS{10.4108/eai.24-10-2018.2280536,
        author={Ratih Nindyasari and Anastasya Latubessy and Alif Catur Murti and Wibowo Hary Sugiharto and Muhammad Malik Hakim},
        title={Cost Optimization of Supply Chain Distribution Model Using Genetic Algorithm},
        proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus},
        publisher={EAI},
        proceedings_a={ICCSET},
        year={2018},
        month={11},
        keywords={supply chain genetic algorithm cost optimation supply chain distribution},
        doi={10.4108/eai.24-10-2018.2280536}
    }
    
  • Ratih Nindyasari
    Anastasya Latubessy
    Alif Catur Murti
    Wibowo Hary Sugiharto
    Muhammad Malik Hakim
    Year: 2018
    Cost Optimization of Supply Chain Distribution Model Using Genetic Algorithm
    ICCSET
    EAI
    DOI: 10.4108/eai.24-10-2018.2280536
Ratih Nindyasari1,*, Anastasya Latubessy1, Alif Catur Murti1, Wibowo Hary Sugiharto1, Muhammad Malik Hakim1
  • 1: Department of Informatic Engineering, Faculty of Engineering, Universitas Muria Kudus
*Contact email: ratih.nindyasari@umk.ac.id

Abstract

Supply chain is the enterprise network that working together to develop and distributing products to customers. After the products have been produce, they will sending to distributor, and then forward to retailer and finally arrived in customer. Consequently, the role of supply chains distibution are very important. Supply chain enable the product moving from the plant to customers that separated by distance. So it is needed a tools that can solving on supply chain problems in order to minimize distibution cost. In this research, the propose metodology is using genetic algorithm to get result the distribution cost that minimize. The distribution cost has been resulted from this algorithm will be compared with result from TORA. And then we will know if genetic algorithm can be able to solving distribution problems more than optimal