مدل راهبردی مدیریت موجودی انبار مبتنی بر یادگیری عمیق
محورهای موضوعی : مدیریت صنعتیحمیدرضا حاجعلی 1 , محمد علی افشار کاظمی 2 * , عادل آذر 3 , عباس طلوعی اشلقی 4 , رضا رادفر 5
1 - دانشجوی دکتری گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - استاد گروه مدیریت صنعتی، واحد تهران مركزي، دانشگاه آزاد اسلامی، تهران، ایران (نویسنده مسئول)
3 - استاد گروه مدیریت صنعتی، دانشگاه تربیت مدرس، تهران، ایران
4 - استاد گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
5 - استاد گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: کنترل موجودی, کلان داده, هوش مصنوعی, یادگیری عمیق, زنجیره تامین,
چکیده مقاله :
هدف:
ارائه یک مدل استراتژیک برای مدیریت موجودی با استفاده از یادگیری عمیق (LSTM) به منظور بهره‌برداری از داده‌های کلان صنعتی غیرخطی و کیفی که با روش‌های کلاسیک قابل استفاده نیستند.
روش‌شناسی پژوهش:
در این پژوهش، یک مدل یادگیری عمیق بر پایه حافظه بلندمدت کوتاه‌مدت (LSTM) طراحی شد. داده‌ها برای آموزش شبکه با استفاده از روش‌های سنتی مدیریت موجودی شامل مقدار اقتصادی سفارش (EOQ)، مقدار اقتصادی تولید (EPQ) و تحلیل ABC آماده‌سازی گردید.
یافته‌ها:
پس از ساخت و آموزش مدل، شبکه LSTM به دقت پیش‌بینی ۹۳ درصد دست یافت که نشان‌دهنده موفقیت ترکیب رویکردهای سنتی با هوش مصنوعی برای تحلیل داده‌های کلان در مدیریت موجودی است.
اصالت / ارزش‌افزوده علمی:
نوآوری این پژوهش در ترکیب روش‌های کلاسیک مدیریت موجودی با تکنیک‌های پیشرفته یادگیری عمیق نهفته است که راه‌حلی کاربردی برای چالش پیش‌بینی بر اساس داده‌های غیرخطی در زنجیره تأمین ارائه می‌دهد.
Objective: To present a strategic model for inventory management using deep learning (LSTM) to utilize non-linear and qualitative industrial big data that is unusable by classic methods.
Research Methodology: In this research, a Long Short-Term Memory (LSTM) deep learning model was designed. Data was prepared for training the network using the traditional methods of Economic Order Quantity (EOQ), Economic Production Quantity (EPQ), and ABC analysis.
Findings: After construction and training, the LSTM model achieved a prediction accuracy of 93%, demonstrating the success of integrating traditional approaches with AI for big data analysis in inventory management.
Originality/Scientific Value Added: The novelty of this research lies in combining classic inventory management methods with advanced deep learning techniques, providing a practical solution for the challenge of forecasting based on non-linear data in the supply chain.
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