ارائه مدل برنامه ریزی ریاضی برای مسیریابی و زمان‌بندی بارانداز متقاطع و حمل و نقل، در شبکه لجستیک معکوس سبز واکسن های ویروس کرونا

Subject Areas : Supply Chain Management

Pezhman  Abbasi Tavallali ¹ (Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
محمدرضا  فیلی زاده ² (Department of Industrial Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran)
Atefeh  Amindoust ³ (Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2021-01-02 Accepted : 2021-12-19 Published : 2021-06-01

Keywords: برنامه ریزی ریاضی, مسیریابی, زمان‌بندی, بارانداز متقاطع, حمل ‌و نقل, شبکه لجستیک معکوس سبز, واکسن های ویروس کرونا,

Abstract :

بارانداز متقاطع، عمل بارگیری واکسن های ویروس کرونا از وسایل‌نقلیه تحویل ‌دهنده ورودی و بارگیری مستقیم آنها بر روی وسایل‌نقلیه خروجی است. با حذف یا به حداقل رساندن هزینه‌های انبار، الزامات فضا و استفاده از موجودی، بارانداز متقاطع می‌تواند زنجیره‌های عرضه را ساده‌تر کرده و به آنها کمک کند تا واکسن های ویروس کرونا را به داروخانه ها سریعتر و کاراتر هدایت کنند. با توجه به طول عمر واکسن های ویروس کرونا هدایت آنها در بارانداز متقاطع و زمان‌بندی حمل و نقل آن‌ها، دارای اهمیت بسیار زیادی است .با توجه به این موارد، در این تحقیق، یک مدل برنامه‌ریزی خطی عدد صحیح آمیخته برای مسیریابی و زمان‌بندی، بارانداز متقاطع و حمل‌ و نقل، در شبکه لجستیک معکوس سبز واکسن های ویروس کرونا ارائه شده است. اقدام به دستیابی به اهداف لجستیک سبز، به منظور کاهش مصرف سوخت و آلایندگی های حاصل از آن، در این پژوهش مد نظر قرار گرفته است. مدل ارائه شده در این تحقیق، چند محصولی و چند سطحی می ‌باشد، که همزمان، مینیمم سازی هزینه‌های مربوط به شبکه لجستیک جهت افزایش کارایی، کاهش مصرف سوخت و آلایندگی ها و ماکزیمم سازی ارزش مصرفی واکسن های ویروس کرونا تحویل داده شده به داروخانه ها به منظور حداقل سازی خطرات و عوارض ناشی از تزریق واکسن های ویروس کرونا به دلیل روند فساد آنها، را در بر می‌گیرد. مدل مورد نظر با در نظر گرفتن کمینه‌سازی هزینه‌ها (جریمه زودکرد و جریمه دیرکرد تحویل سفارش به داروخانه ها، هزینه نگهداری موقت در بارانداز متقاطع و هزینه مصرف سوخت، آلایندگی های زیست محیطی ناشی از جابجایی وسایل نقلیه و استفاده از وسیله‌های نقلیه خروجی)، همچنین در نظر گرفتن عدم قطعیت در تقاضای واکسن های ویروس کرونا توسط داروخانه ها، از نوع مسائل NP-Hard است. در این مدل، زمان حل مسأله به صورت نمایی و با توجه به ابعاد مسأله افزایش می‌یابد. بنابراین، در این تحقیق، یک روش کارا با استفاده از الگوریتم NSGA II پیشنهاد داده شده است.

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