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 ManagementPezhman Abbasi Tavallali 1 , محمدرضا فیلی زاده 2 , Atefeh Amindoust 3
1 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Industrial Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
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.
Adabavazeh, N., Nikbakht, M., & Amirteimoori, A. (2020). Envelopment analysis for global response to novel 2019 coronavirus-SARS-COV-2 (COVID-19). Journal of Industrial Engineering and Management Studies, 7(2), 1–35.
Afra, A. P., & Behnamian, J. (2021). Lagrangian heuristic algorithm for green multi-product production routing problem with reverse logistics and remanufacturing. Journal of Manufacturing Systems, 58, 33–43.
Ardakani, A. (Arsalan), & Fei, J. (2020). A systematic literature review on uncertainties in cross-docking operations. Modern Supply Chain Research and Applications, 2(1), 2–22. https://doi.org/10.1108/mscra-04-2019-0011
Avakh Darestani, S., & Pourasadollah, F. (2019). A Multi-Objective Fuzzy Approach to Closed-Loop Supply Chain Network Design with Regard to Dynamic Pricing. Journal of Optimization in Industrial Engineering, 12(1), 173–194.
Babazadeh, R. (2020). A metaheuristic algorithm for optimizing strategic and tactical decisions in a logistics network design problem. Iranian Journal of Optimization, 12(1), 103–113.
Baniamerian, A., Bashiri, M., & Tavakkoli-Moghaddam, R. (2019). Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with cross-docking. Applied Soft Computing Journal, 75, 441–460. https://doi.org/10.1016/j.asoc.2018.11.029
Choi, T.-M. (2020). Risk Analysis in Logistics Systems: A Research Agenda During and After the COVID-19 Pandemic. Elsevier.
Dagne, T. B., Jayaprkash, J., & Geremew Gebeyehu, S. (2020). Design of Supply Chain Network Model for Perishable Products with Stochastic Demand: An Optimized Model. Journal of Optimization in Industrial Engineering, 13(1), 29–37.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International Conference on Parallel Problem Solving from Nature, 849–858.
Gardas, B. B., Raut, R. D., & Narkhede, B. (2018). Reducing the exploration and production of oil: Reverse logistics in the automobile service sector. Sustainable Production and Consumption, 16, 141–153. https://doi.org/10.1016/j.spc.2018.07.005
Gelareh, S., Glover, F., Guemri, O., Hanafi, S., Nduwayo, P., & Todosijević, R. (2020). A comparative study of formulations for a cross-dock door assignment problem. Omega (United Kingdom), 91, 102015. https://doi.org/10.1016/j.omega.2018.12.004
He, P., & Li, J. (2019). The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation. Applied Soft Computing Journal, 77, 387–398. https://doi.org/10.1016/j.asoc.2019.01.040
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93–103. https://doi.org/10.1016/j.jmsy.2016.10.004
Jansen, W. (2019). Efficient Routing and Planning within the Complex Logistical Network : Based on the Integration of a New Warehouse, AGV Transports and Increased Transportation Rates. http://essay.utwente.nl/77465/%0Ahttp://purl.utwente.nl/essays/77465
Khodaparasti, S., Bruni, M. E., Beraldi, P., Maleki, H. R., & Jahedi, S. (2018). A multi-period location-allocation model for nursing home network planning under uncertainty. Operations Research for Health Care, 18, 4–15. https://doi.org/10.1016/j.orhc.2018.01.005
Kim, J. H., Marks, F., & Clemens, J. D. (2021). Looking beyond COVID-19 vaccine phase 3 trials. Nature Medicine, 27(2), 205–211.
Küçükoğlu, İ., & Öztürk, N. (2019). A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking. Journal of Intelligent Manufacturing, 30(8), 2927–2943. https://doi.org/10.1007/s10845-015-1156-z
Kuşakcı, A. O., Ayvaz, B., Cin, E., & Aydın, N. (2019). Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area. Journal of Cleaner Production, 215, 1036–1051. https://doi.org/10.1016/j.jclepro.2019.01.090
Lee, L. K., Chen, P. C. Y., Lee, K. K., & Kaur, J. (2006). Menstruation among adolescent girls in Malaysia: A cross-sectional school survey. In Singapore Medical Journal (Vol. 47, Issue 10, pp. 869–874).
Liao, T. Y. (2018). Reverse logistics network design for product recovery and remanufacturing. Applied Mathematical Modelling, 60, 145–163. https://doi.org/10.1016/j.apm.2018.03.003
LIU, H., & And, C. L. (2019). Optimization for Multi-objective Location-routing Problem of Cross-docking with Fuzzy Time Windows. Journal of University of Electronic Science. http://en.cnki.com.cn/Article_en/CJFDTotal-DKJB201905011.htm
Mancini, S. (2017). The Hybrid Vehicle Routing Problem. Transportation Research Part C: Emerging Technologies, 78, 1–12. https://doi.org/10.1016/j.trc.2017.02.004
Mousavi, S. M., & Vahdani, B. (2017). A robust approach to multiple vehicle location-routing problems with time windows for optimization of cross-docking under uncertainty. Journal of Intelligent and Fuzzy Systems, 32(1), 49–62. https://doi.org/10.3233/JIFS-151050
Nalepa, J., & Blocho, M. (2017). Adaptive guided ejection search for pickup and delivery with time windows. Journal of Intelligent and Fuzzy Systems, 32(2), 1547–1559. https://doi.org/10.3233/JIFS-169149
Nasiri, M. M., Rahbari, A., Werner, F., & Karimi, R. (2018). Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem. International Journal of Production Research, 56(19), 6527–6552. https://doi.org/10.1080/00207543.2018.1471241
Nikolopoulou, A. I., Repoussis, P. P., Tarantilis, C. D., & Zachariadis, E. E. (2019). Adaptive memory programming for the many-to-many vehicle routing problem with cross-docking. Operational Research, 19(1), 1–38. https://doi.org/10.1007/s12351-016-0278-1
Rahbari, A., Nasiri, M. M., Werner, F., Musavi, M. M., & Jolai, F. (2019). The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling, 70, 605–625. https://doi.org/10.1016/j.apm.2019.01.047
Rahimi, M., & Ghezavati, V. (2018). Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste. Journal of Cleaner Production, 172, 1567–1581. https://doi.org/10.1016/j.jclepro.2017.10.240
Rahmandoust, A., & Soltani, R. (2019). Designing a location-routing model for cross docking in green supply chain. Uncertain Supply Chain Management, 7(1), 1–16. https://doi.org/10.5267/j.uscm.2018.7.001
Roshani Delivand, M., & Shabgoo Monsef, S. M. (2020). The Impact of Green Supply Chain Management on Export Performance of Exporters of Guilan Province Due to the Mediating Role of Environmental Performance. Iranian Journal of Optimization, 12(1), 73–82.
Sharafi, A., & Bashiri, M. (2016). Green vehicle routing problem with safety and social concerns. Journal of Optimization in Industrial Engineering, 10(21), 93–100.
Shirouyezad, H., Khodadadi-Karimvand, M., & Jozdani, J. (2020). An Analysis of the COVID-19 Contagion Growth in European Countries. Iranian Journal of Optimization, 12(1), 10–18.
Shuang, Y., Diabat, A., & Liao, Y. (2019). A stochastic reverse logistics production routing model with emissions control policy selection. International Journal of Production Economics, 213, 201–216. https://doi.org/10.1016/j.ijpe.2019.03.006
Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2020). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 1–16.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.
Taleghani, A., & Taleghani, M. (2018). The Impact of Supply Chain Management on Industrial Efficiency and Technical Performance (Case Study: Engineering New Enterprises of Guilan Province, Northern of Iran). Iranian Journal of Optimization, 10(1), 31–39.
Tirkolaee, E. B., Hadian, S., Weber, G., & Mahdavi, I. (2020). A robust green traffic‐based routing problem for perishable products distribution. Computational Intelligence, 36(1), 80–101.
Trochu, J., Chaabane, A., & Ouhimmou, M. (2018). Reverse logistics network redesign under uncertainty for wood waste in the CRD industry. Resources, Conservation and Recycling, 128, 32–47. https://doi.org/10.1016/j.resconrec.2017.09.011
Vaez-Ghasemi, M., Moghaddas, Z., & Saen, R. F. (2021). Cost efficiency evaluation in sustainable supply chains with marginal surcharge values for harmful environmental factors: a case study in a food industry. Operational Research, 1–16.
Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282–305. https://doi.org/10.1016/j.jclepro.2019.03.279
Yazdanpanah, A. H., Akbari, A. A., & Mozafari, M. (2019). A Game Theoretical Approach to Optimize Policies of Government Under the Cartel of Two Green and Non-Green Supply Chains. Journal of Optimization in Industrial Engineering, 12(2), 189–197.
Yu, H., & Solvang, W. D. (2018). Incorporating flexible capacity in the planning of a multi-product multi-echelon sustainable reverse logistics network under uncertainty. Journal of Cleaner Production, 198, 285–303. https://doi.org/10.1016/j.jclepro.2018.07.019
Zhang, Y., Alshraideh, H., & Diabat, A. (2018). A stochastic reverse logistics production routing model with environmental considerations. Annals of Operations Research, 271(2), 1023–1044. https://doi.org/10.1007/s10479-018-3045-2
Zulvia, F. E., Kuo, R. J., & Nugroho, D. Y. (2020). A many-objective gradient evolution algorithm for solving a green vehicle routing problem with time windows and time dependency for perishable products. Journal of Cleaner Production, 242, 118428.
