نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشگاه علامه طباطبایی.

2 استاد، دانشگاه علامه طباطبائی.

3 دانشیار، دانشگاه علامه طباطبائی

چکیده

انتخاب و تخصیص در زنجیره  تأمین تاب آور، زمانی که اختلال زنجیره تأمین را تهدید می‌کند، به‌عنوان یک تصمیم استراتژیک و به کانون پژوهش­های بسیاری تبدیل شده است؛ از سوی دیگر افزایش کاربردهای یادگیری ماشین در سراسر مطالعات زنجیره تأمین به ظهور روش‌های تصمیم‌گیری سریع‌تر و مطمئن‌تر منجر شده است، بااین‌حال در مطالعات کمی از یادگیری ماشین برای مقابله با مشکل انتخاب و تخصیص تأمین‌کننده به مشتری در حالت تاب­آور استفاده شده است. هدف پژوهش حاضر برداشتن گامی در جهت رفع این شکاف با استفاده از الگوریتم‌های یادگیری ماشین بر روی داده‌های دنیای واقعی از زنجیره تأمین خودرو در ایران است. بدین منظور از داده‌های عملکردی441 تأمین‌کننده و 7 مشتری در سال 1401 استفاده شد. در این پژوهش از دو الگوریتم خوشه‌بندی برای تولید برچسب بر اساس مفهوم ظرفیت تاب‌آوری استفاده شده است؛ سپس ازآنجاکه تفسیرپذیری نتایج در اولویت قرار داشت، بر اساس لیبل­گذاری خوشه‌ها توسط خبرگان از درخت تصمیم برای طبقه‌بندی تأمین‌کنندگان بر اساس عملکرد آن‌ها استفاده شد. نتایج نشان داد که درختK-means عملکرد بهتری نسبت به درخت DBSCAN دارد و معیارهای چون تحویل به‌موقع، درصد تأمین، توقف خط تولید، اخطارهای کیفی، عملکرد لجستیکی و عملکرد کیفی بر تاب‌آوری تأمین‌کنندگان مؤثر هستند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Classification and Allocation of Suppliers to Customers in Resilince Supply Chains Using Machine Learning

نویسندگان [English]

  • Mahdi Esmaeili 1
  • Laya Olfat 2
  • Maghsoud Amiri 2
  • Iman Raeesi Vanani 3

1 PhD student, Allameh Tabatabai University.

2 Professor, Allameh Tabatabai University.

3 Associate Professor, Allameh Tabatabaei University

چکیده [English]

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.

کلیدواژه‌ها [English]

  • Supply Chain Resilience
  • Resilient Supplier Selection
  • Machine Learning
  • Supplier-to-Customer Allocation
  • Automotive Supply Chain
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