الگوریتم‌های فراابتکاری گورکن عسل خوار و ژنتیک چندهدفه برای حل مدل ریاضی استوار زنجیره تأمین پایدار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه آزاد اسلامی واحد تهران جنوب، ایران.

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و افتصاد، دانشگاه تربیت مدرس، تهران، ایران.

چکیده
با افزایش عدم‌قطعیت‌ در بازار و پیچیدگی فزاینده‌ی محیط رقابتی، پایداری زنجیره‌های تأمین بیش‌ازپیش در معرض اختلال قرار گرفته و هرگونه نوسان در تقاضا یا کاهش ظرفیت اثرات منفی گسترده‌ای بر عملکرد کل زنجیره تأمین ایجاد می کند. لذا توسعه مدلهای ریاضی متناسب از پیچیدگی بیشتری برخوردار شده و نیازمند رویکردهای مناسب حل می باشد. این پژوهش با هدف ارائه رویکرد مناسب حل مدل ریاضی چندهدفه استوار و سناریومحور توسعه یافته نظری و همکاران( 1403) به منظور کاهش هزینه‌های کل سیستم و آلاینده‌های زیست‌محیطی، که نوآوری عمده آن در ترکیب هم‌زمان عدم‌قطعیت تقاضا ناشی از نرخ تورم و کاهش ظرفیت در اثر اختلال بوده، اجرا شده است. از آنجا که با افزایش ابعاد و چندهدفه شدن مسئله، استفاده از روش‌های دقیق حل امکان‌پذیر نیست لذا برای حل مدل از دو الگوریتم فراابتکاری قدرتمند شامل توسعه الگوریتم نوظهور گورکن عسل‌خوار (HBA) به چند هدفه و الگوریتم ژنتیک چندهدفه (NSGA-II) استفاده شد. برای ارزیابی کارایی مدل و الگوریتم‌های پیشنهادی، یک مثال عددی طراحی و اجرا شد. یافته‌ها بیانگر آنست که مدل ارائه‌شده توانایی بالایی در مواجهه با عدم‌قطعیت داشته و تصمیمات بهینه‌تری در راستای پایداری اقتصادی, اجتماعی و زیست‌محیطی فراهم می‌کند. همچنین نتایج محاسباتی نشان‌دهنده‌ی کارایی و دقت بالاتر الگوریتم HBA در حل مسائل سخت (NP-Hard) با فضای جستجوی بزرگ و پیچیده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Multi-objective honey-badger metaheuristic and genetic algorithms for solving a robust mathematical model of a sustainable supply chain

نویسندگان English

Massoumeh Nazari 1
Mahmoud Dehghan Nayeri 2
1 Phd. Candidate, Department of Industrial Management, faculty of Management, Islamic Azad University, South Tehran branch, Iran.
2 Assocciate professor, Department of Industrial Management, faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
چکیده English

With increasing market uncertainty and the growing complexity of competitive environments, supply chains have become more vulnerable to disruptions. Even minor fluctuations in demand or reductions in capacity can generate widespread negative impacts on overall supply chain performance. As a result, developing mathematical models that capture this complexity has become more challenging and requires appropriate solution methodologies. This study proposes an effective solution approach for the robust, scenario-based multi-objective mathematical model developed by Nazari et al. (2024), which seeks to reduce total system costs and environmental emissions. A key innovation of the model is the simultaneous consideration of demand uncertainty—driven by inflation rates—and capacity reductions caused by disruptions. Because exact solution methods become impractical for large-scale, multi-objective problems, two powerful metaheuristic algorithms were employed: a multi-objective extension of the emerging Honey Badger Algorithm (HBA) and the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II). To evaluate the performance of the proposed model and algorithms, a numerical case study was designed and executed. The results indicate that the proposed model is highly effective in handling uncertainty and supports more optimal decision-making aligned with economic, social, and environmental sustainability. Furthermore, the computational results demonstrate that the HBA algorithm achieves higher efficiency and accuracy in solving hard (NP-hard) problems with large, complex search spaces.

کلیدواژه‌ها English

Sustainable Supply Chain
Multi-Objective Optimization
Uncertainty
Metaheuristic Algorithm
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