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All Journal Sosiohumaniora Jurnal Mahupiki ENGINEERING Students' Journal of Engineering Jurnal Ilmu Kesehatan Masyarakat Jurnal technoscientia Jurnal Teknologi JBTI : Jurnal Bisnis : Teori dan Implementasi Jurnal Ilmiah Administrasi Publik (JI@P) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Adi Widya : Jurnal Pengabdian Masyarakat Edukasi Islami: Jurnal Pendidikan Islam Jurnal Pengabdian Masyarakat AbdiMas Jurnal Sains dan Teknologi Abdimasku : Jurnal Pengabdian Masyarakat El-Qist : Journal of Islamic Economics and Business (JIEB) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Social Research Jurnal Pendidikan Hayati STLISTIKA: Jurnal Bahasa, Sastra, dan Pengajarannya Jurnal Cakrawala Ilmiah Indonesian Journal of Community Empowerment (IJCE) Matemar Jurnal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Ilmu Ekonomi, Manajemen dan Bisnis Jurnal Bakti Humaniora International Journal of Technology and Modeling
Adi Purwanto
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Journal : International Journal of Technology and Modeling

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Optimizing Supply Chain Management with Reinforcement Learning: A Data-Driven Approach Purwanto, Adi; Maesaroh, Siti; Sulistyo, Agung
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.113

Abstract

Effective supply chain management (SCM) is crucial for improving efficiency, reducing costs, and enhancing responsiveness in dynamic market conditions. Traditional SCM optimization methods often rely on static models that struggle to adapt to uncertainty and real-time changes. In this study, we propose a data-driven approach using reinforcement learning (RL) to optimize decision-making in SCM. By leveraging historical and real-time data, our RL model dynamically learns optimal inventory policies, demand forecasting strategies, and logistics planning to minimize costs and maximize service levels. We evaluate the performance of our approach through simulations and real-world case studies, demonstrating significant improvements over conventional optimization techniques. The results highlight the potential of RL in transforming SCM by enabling adaptive, intelligent decision-making in complex and uncertain environments.
Co-Authors Addini, Puteri Fajar Ade Makmur k Afriyani, Luvi Dian Agus Duniawan, Agus Agustin . Ahmad Ali Aji Pranoto Alamsyah, Alvar Ridho Andri Niswandri Prasojo Aris Tri Haryanto Beni Agus Setiono Budi Witjaksana, Budi Carlos Lazaro Cuk Hartono D. Saputra, Toto Dian Esti Nurati Didik Purwiyanto Dwi Iryaning Handayani Eka Pujo Ekka Pujo Ariesanto Eko Roedianto Erni susilawati Eti Salafas Farida Wahyu Ningtyias, Farida Wahyu Fitri, Khoiru Nur Hardiyo, Joko Hartono, Adji Sarwo Hary Wibowo, Hary Ida Widaningrum, Ida Isnawati, Setya Indah Joko Pramono Karon Suprapto Lazaro, Carlos Madiasa Ablisar Maulidi, Achmad Fikih Mury Ririanty Mustaqim . Niswandri Prasojo, Andri Noor Laila Ramadhani Novia Winda Nurmala Waty S, Achmad Taufiq Satria Avianda Nurcahyo SATRIYAS ILYAS Simamora, Riandy Debataraja Siswiyanti, Siswiyanti Siti Maesaroh Sudiyono Sudiyono Sudiyono Sulistyo, Agung Suwardi Suwardi Teki Tjendani, Hanie Toto Rusianto Widagdo, Teguh Harso WINDY WINDY