ارائه مدل پیش‌بینی انتشار کربن با در نظر گرفتن نقش تأمین‌کنندگان در مدیریت زنجیره تأمین با استفاده از الگوریتم تقویت گرادیان سبک (مطالعه موردی صنایع شیمیایی تهران(خاوران))

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار، گروه مدیریت صنعتی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران

3 دانشیار، گروه مهندسی عمران، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران

چکیده
هدف از این مقاله، ارائه مدل پیش‌بینی انتشار کربن با درنظرگرفتن نقش تأمین‌کنندگان می‌باشد که می‌تواند نقش بسزایی در کاهش دی‌اکسیدکربن منطقه داشته باشد. برای تعیین متغیرهای تأثیرگذار از ادبیات تحقیق و نظرات خبرگان (۲۰ نفر) با روش دلفی فازی استفاده شد که در نهایت ۸ متغیر تأثیرگذار انتخاب شدند. برای مدل‌سازی، الگوریتم تقویت گرادیان سبک (LGB) به دلیل توانایی در ثبت وابستگی‌های غیرخطی و بهینه‌ساز جستجوی عروس دریایی (JSO) برای تنظیم دقیق ابرپارامترها انتخاب شدند. نوآوری پژوهش در زمینه تلفیق LGB و JSO برای افزایش دقت پیش‌بینی، و همچنین، در نظر گرفتن هم‌زمان نقش تأمین‌کنندگان به عنوان متغیرهای تأثیرگذار در پیش‌بینی انتشار کربن می‌باشد. مدل‌ها با ۵۹۹۷ داده از صنایع شیمیایی تهران (خاوران) (۲۰۲۲-۲۰۲۵) در پایتون اجرا شدند. نتایج نشان داد LGB_JSO با R² برابر ۹۹۱۸/۰، ۹۵۳۸/۰ و ۹۶۰۶/۰ در مراحل آموزش، اعتبارسنجی و آزمایش، عملکرد بهتری نسبت به سایر مدل‌ها دارد. دما، فشار و زمان ذخیره‌سازی مهم‌ترین پارامترهای مؤثر شناسایی شدند.

کلیدواژه‌ها


عنوان مقاله English

Presenting a Carbon Emission Prediction Model Considering the Role of Suppliers in Supply Chain Management Using Light Gradient Boosting Algorithm (Case Study: Chemical Industries of Tehran (Khavaran))

نویسندگان English

Hadi Ebrahimi 1
Maryam Shoar 2
Ali Hajiha 2
Mahzad Esmaeili-Falak 3
1 Department of Industrial Management, North Tehran Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Management, North Tehran Branch, Islamic Azad University, Tehran, Iran
3 Department of Civil Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده English

This paper aims to present a model for predicting carbon emissions by considering the role of suppliers, which can significantly contribute to reducing carbon dioxide in the region. To determine the influential variables, literature review and expert opinions (20 experts) were used through the fuzzy Delphi method, ultimately selecting 8 influential variables. For modeling, the Light Gradient Boosting (LGB) algorithm was chosen due to its ability to capture nonlinear dependencies, and the Jellyfish Search Optimizer (JSO) was employed for precise hyperparameter tuning. The research innovation lies in two areas: the integration of LGB and JSO to increase prediction accuracy, and simultaneously considering the role of suppliers as influential variables in carbon emission prediction. The models were implemented using 5,997 data points from Tehran's chemical industries (Khavaran region) during 2022-2025, utilizing Python programming language. Results showed that the hybrid LGB_JSO model, with R² values of 0.9918, 0.9538, and 0.9606 in training, validation, and testing phases respectively, performed better than other models. Temperature, pressure, and storage time were identified as the most important influential parameters.

کلیدواژه‌ها English

Light Gradient Boosting Algorithm
Carbon Emission
Prediction
Suppliers
Supply Chain Managementnt
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