مدل‌سازی تصادفی انتخاب تأمین‌کننده تاب‌آور و تخصیص سفارش در شرایط جنگ، تحریم و پاندمی: تحلیل آن در شبکه بیزی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی صنایع، مجتمع دانشگاهی مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران

2 دانشیار گروه مهندسی صنایع، مجتمع دانشگاهی مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران

چکیده

اختلالاتی مانند جنگ، تحریم و پاندمی‌ها علاوه‌بر تأثیر روی تأمین‌­کنندگان و تولید­کننده؛ می‌­توانند برروی یکدیگر در ابتدای زنجیره یا بر تقاضای مشتری در انتهای زنجیره تأثیرگذار باشند. مدل‌سازی این روابط پیچیده با استفاده از شبکه بیزی انجام شده و میزان اثرگذاری اختلالات در هر نقطه از زنجیره را نشان می‌دهد. از نرخ تورم برای پیش‌­بینی و کاهش عدم قطعیت‌­های تقاضا استفاده می‌شود. قابلیت اطمینان تأمین‌کنندگان که در زنجیره‌تأمین بسیار اهمیت دارد در یک مدل برنامه‌ریزی تصادفی عدد صحیح مختلط دوهدفه با دو تابع هدف افزایش پراکندگی جغرافیایی و کاهش هزینه کل (حمل‌ونقل، خرید و سفارش­دهی و...) لحاظ می‌شود. در این مدل تأمین‌­کنندگان و تولیدکننده برای افزایش تاب‌آوری زنجیره‌تأمین مشارکت می‌­کنند. برای اولین‌بار مفهوم سطح تاب­‌آوری تأمین‌کننده پیشنهاد می‌­­شود. مدل ارائه شده برای تخصیص سفارش علاوه‌بر قیمت و سایر هزینه‌های سفارش‌دهی، هزینه بهبود سطح تاب­‌آوری تأمین‌کنندگان را نیز درنظر می­‌گیرد. همچنین سطح رضایت مشتری به‌صورت ضمنی با کاهش هزینه عدم ارضای تقاضا محاسبه می‌شود. به‌همین منظور یک مطالعه موردی در یکی از شرکت‌­های خودروسازی ایران انجام گرفت. برای اعتبارسنجی مدل پیشنهادی یک مثال عددی حل و تحلیل حساسیت آن انجام شد. برای کاهش سناریو از روش خوشه­‌بندی c-میانگین فازی و تحلیل متوازن اثر متقابل استفاده شد. مدل ارائه شده می­‌تواند تولیدکنندگان را برای تصمیم‌­گیری و برنامه‌­ریزی بهتر در مواجه با ریسک‌­ها و عدم قطعیت‌­های آینده آماده کند.‌

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Stochastic Modeling of Resilient Supplier Selection and Order Allocation Under Conditions of War, Sanctions, and Pandemic: An Analysis Using Bayesian Networks

نویسندگان [English]

  • Mohammad Khosroabadi 1
  • Jafar Gheidar-Kheljani 2
  • Mohammad Hosein Karimi-Gavareshki 2
1 PhD Candidate, Department of Industrial Engineering, Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran
2 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran
چکیده [English]

Disruptions such as war, sanctions, and pandemics not only impact suppliers and manufacturers but also influence each other at the beginning of the supply chain or affect customer demand at the end of the chain. This study employs Bayesian networks to model these complex relationships and demonstrate the extent of disruptions at each point in the supply chain. Inflation rates are utilized to predict and mitigate demand uncertainties. The reliability of suppliers, a critical aspect in supply chains, is incorporated into a bi-objective stochastic mixed-integer programming model with objectives of increasing geographical dispersion and reducing total costs (including transportation, purchasing, and ordering costs). In this model, suppliers and manufacturers collaborate to enhance supply chain resilience. For the first time, the concept of supplier resilience level is introduced. The proposed model for order allocation considers not only prices and other ordering costs but also the costs of improving suppliers' resilience levels. Additionally, customer satisfaction is implicitly calculated by reducing the cost of unmet demand. To validate the model, a case study was conducted at an automotive company in Iran, followed by a numerical example and sensitivity analysis. Scenario reduction was achieved using the fuzzy c-means clustering method and balanced impact analysis. The proposed model equips manufacturers with better decision-making and planning capabilities in the face of future risks and uncertainties.

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نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 12، شماره 24
شهریور 1403
صفحه 93-113

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