طراحی زنجیره تامین حلقه بسته سبز با در نظر گرفتن پیش بینی تقاضا برای محصول پرینتر سه بعدی
محورهای موضوعی : مدیریت صنعتی
فریده اعتمادی فرد
1
,
امیررضا احمدی کشاورز
2
,
فرشته خلج
3
1 - دانشجوی کارشناسی ارشد مهندسی صنایع ، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار گروه مهندسی صنایع، واحد پرند، دانشگاه آزاد اسلامی، تهران، ایران
3 - استادیار گروه مهندسی صنایع، واحد رباط کریم، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: زنجیره تامین سبز, حلقه بسته, پیش بینی تقاضا, پرینتر سه بعدی,
چکیده مقاله :
چکیده:
این پژوهش به طراحی و بهینهسازی زنجیره تأمین سبز حلقه بسته (CLSC) با در نظر گرفتن پیشبینی تقاضا برای محصولات پرینتر سه بعدی میپردازد. معماری شبکه طراحی شده شامل هشت سطح مختلف است: مراکز پرینتر سه بعدی (DPC)، مراکز درمان (TC)، مراکز بازیافت (RC)، مشتریان رشته (FC)، مراکز پردازش (PC)، مراکز جمعآوری (CC)، بازارها و کاربران نهایی. این شبکه با هدف استفاده از بطریهای PET بازیافتی برای تولید فیلامنتهای پرینتر سه بعدی، کاهش هزینهها و آلودگیهای زیستمحیطی طراحی شده است. برای بهینهسازی این شبکه، یک مدل ریاضی به فرم برنامهریزی خطی عدد صحیح مختلط (MILP) توسعه داده شده که هدف آن کمینهسازی مجموع هزینههای ثابت، حملونقل و انتشار کربن است. مدل پیشنهادی با رویکرد چند سناریویی، عدم قطعیت در پیشبینی تقاضا را در نظر میگیرد و برای هر سناریو احتمال وقوع مشخصی تعریف شده است. محدودیتهای مدل شامل محدودیتهای ظرفیت، تعادل جریان، تخصیص و ارتباط بین گرههای شبکه میباشد. برای حل این مدل بهینهسازی پیچیده، از الگوریتمهای فراابتکاری الگوریتم ژنتیک (GA) و شبیهسازی تبرید (SA) استفاده شده است. کدگذاری کروموزومها شامل بخشهای باینری برای مکانیابی و تخصیص مراکز و بخش پیوسته برای جریان مواد است. نتایج نشان میدهد که این رویکرد میتواند به طور مؤثری هزینههای کل زنجیره تأمین را کاهش دهد و همزمان اهداف زیستمحیطی را با استفاده از بازیافت بطریهای PET برآورده سازد. این پژوهش با رویکردی کاربردی-توسعهای، راهکاری مؤثر برای مدیریت پایدار زنجیره تأمین در صنعت پرینت سه بعدی ارائه میدهد که میتواند به کاهش هزینههای تولید فیلامنت و بهبود عملکرد زیستمحیطی این صنعت کمک شایانی نماید.
Abstract
This research focuses on designing and optimizing a Green Closed-Loop Supply Chain (CLSC) for 3D printer products, incorporating demand forecasting. The proposed network architecture comprises eight levels: 3D Printer Centers (DPC), Treatment Centers (TC), Recycling Centers (RC), Filament Customers (FC), Processing Centers (PC), Collection Centers (CC), markets, and end users. The goal is to utilize recycled PET bottles for filament production, thereby reducing costs and minimizing environmental pollution. To optimize this network, a mathematical model based on Mixed Integer Linear Programming (MILP) has been developed. The model aims to minimize total costs, including fixed expenses, transportation, and carbon emissions. It employs a multi-scenario approach to address demand uncertainty, assigning specific probabilities to each scenario. The model constraints cover capacity limitations, flow balance, allocation, and network node connections. Given the complexity of the problem, metaheuristic algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) were used to find solutions. The chromosome encoding combines binary segments for facility location and allocation decisions with continuous segments for material flows. Results demonstrate that this approach effectively reduces overall supply chain costs while achieving environmental objectives through PET bottle recycling. This applied and developmental research offers a practical solution for sustainable supply chain management within the 3D printing industry, significantly lowering filament production costs and enhancing the industry's environmental performance.
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