Presenting a Mathematical Programming Model for Routing and Scheduling of Cross-Dock and Transportation in Green Reverse Logistics Network of COVID-19 Vaccines
محورهای موضوعی : 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)
کلید واژه: routing, Scheduling, Transportation, Mathematical Modeling, Cross-Dock, Green Reverse Logistics Network, COVID-19 vaccines,
چکیده مقاله :
Cross-docking is the practice of unloading Coronavirus vaccines from inbound delivery vehicles and loading them directly onto outbound vehicles. Cross-docking can streamline supply chains and help them move Coronavirus vaccines to pharmacies faster and more efficiently by eliminating or minimizing warehouse storage costs, space requirements, and inventory handling. Regarding their short shelf-life, the movement of Coronavirus vaccine to cross-dock and their freight scheduling is of great importance. Achieving the goals of green logistics in order to reduce fuel consumption and emission of pollutants has been considered in this study. Accordingly, the present study developed a mixed-integer linear programming (MILP) model for routing and scheduling of cross-dock and transportation in green reverse logistics network of Coronavirus vaccines. The model was multi-product and multi-level and focused on minimizing the logistics network costs to increase efficiency, reduce fuel consumption and pollution, maximizing the consumption value of delivered Coronavirus vaccines and minimizing risk of injection complication due to Coronavirus vaccines corruption. Considering cost minimization (i.e., earliness and tardiness penalty costs of pharmacies order delivery, cost of fuel consumption and environmental pollution caused by outbound vehicles crossover, the inventory holding costs at the temporary storage area of the cross-dock and costs of crossover and use of outbound vehicles) as well as uncertainty in pharmacies demand for Coronavirus vaccines, the model was an NP-hard problem. In this model, the problem-solving time increased exponentially according to the problem dimensions; hence, this study proposed an efficient method using the NSGA II algorithm.
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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