ملاحظات اجتماعی در طراحی زنجیره تامین پایدار حلقه بسته: رویکرد الگوریتم تکاملی چندین هدفه برای کربن‌زدایی عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران

2 دانشیار، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران

3 محقق پسادکتری، دانشکده کارآفرینی و توسعه پایدار، دانشگاه هالمستاد، هالمستاد، سوئد

چکیده
معاهده پاریس به طور گسترده به دنبال جلوگیری از افزایش گرمایش و انتشار گازهای گلخانه‌ای در سطح اکوسیستم است. این مساله، به ‌همراه قوانین محیط زیستی دولت‌ها و افزایش توجه مشتریان به توسعه پایدار، شرکت‌ها را وادار به بازنگری در ساختارهای خود کرده است. لذا این مطالعه با هدف طراحی یک زنجیره تامین پایدار حلقه بسته چند سطحی برای حرکت به سمت کربن‌زدایی عمیق انجام شده است. یک مدل برنامه‌ریزی خطی عدد صحیح مختلط غیرقطعی توسعه داده شده است تا هزینه، مصرف انرژی و انتشار کربن را به حداقل رسانیده و در عین حال فرصت‌های شغلی را حداکثر کند. آموزش کارکنان شبکه برای بهبود شرایط اجتماعی آن در توسعه مدل این مطالعه گنجانده شده، که پیش از این به ندرت مورد توجه قرار گرفته است. رویکرد برنامه‌ریزی مقید به شانس برای مقابله با عدم قطعیت در نظر گرفته شده است و الگوریتم‌های ژنتیک مرتب‌سازی غیر غالب دوم (NSGA-II) و سوم (NSGA-III) برای حل مساله به کار برده شده‌اند. نتایج، تأثیر قابل توجه استفاده از انرژی پاک و کامیون‌های برقی بر کاهش انتشار کربن در تسهیلات عملیاتی و بخش حمل و نقل را نشان می‌دهد. یافته‌ها همچنین نشان می‌دهند که افزایش در مقدار سطح اطمینان برآورده شدن محدودیت‌های غیر‌قطعی منجر به افزایش در توابع هدف هزینه و محیط زیست می‌شود. تحلیل حساسیت بروی نتایج، تأثیر قابل توجه محصولات برگشتی در کاهش هزینه‌های شبکه، انتشار کربن و حفظ منابع و مواد اولیه را نشان می‌دهد. مجموعه راه‌حل‌های پارتو انعطاف‌پذیری بیشتری را برای تصمیم‌گیرندگان در اتخاذ تصمیمات استراتژیک و عملیاتی فراهم می‌کند

کلیدواژه‌ها


عنوان مقاله English

Social Considerations in Designing Closed-Loop Sustainable Supply Chain: A Many-Objective Evolutionary Algorithm Approach for Deep Decarbonization

نویسندگان English

Pooria Hashemzahi 1
Mohammad Mohammadi 2
Mohammad Saeid Atabaki 3
1 PhD student, Department of Industrial Engineering, Faculty of Technology and Engineering, Khwarazmi University, Tehran, Iran
2 Associate Professor, Department of Industrial Engineering, Faculty of Technology and Engineering, Khwarazmi University, Tehran, Iran
3 Postdoctoral Researcher, Faculty of Entrepreneurship and Sustainable Development, Halmstad University, Halmstad, Sweden
چکیده English

The Paris Agreement broadly seeks to prevent increased warming and emissions of greenhouse gases at the ecosystem level. This issue has forced companies to review their structures due to the strict laws of governments and the desire for sustainability from customers. Therefore, this study has been carried out with the aim of designing a sustainable multi-echelon closed loop supply chain to move towards deep decarbonization. An uncertain mixed integer linear programming model is developed to minimize cost, energy consumption and carbon emissions while maximizing job opportunities. Network staff training to improve its social conditions is included in the development of this study's model, which has rarely been considered before. A chance-constrained programming approach is considered to deal with uncertainty, and second-order (NSGA-II) and third-order (NSGA-III) non-dominated sorting genetic algorithms are used to solve the problem. The results show the significant impact of using clean energy and electric trucks on reducing carbon emissions in operational facilities and the transportation sector. The findings also show that an increase in the value of the confidence level in meeting uncertain constraints leads to an increase in the objective functions of cost and environment. The sensitivity analysis of the results shows the significant impact of the returned products in reducing network costs, carbon emissions and conservation of resources and raw materials. The Pareto solution set provides greater flexibility to decision makers in making strategic and operational decisions.

کلیدواژه‌ها English

closed-loop sustainable supply chain
social responsibility
deep decarbonization
NSGA-II and NSGA-III
chance-bound planning
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