بهینه‌سازی چندهدفه در طراحی شبکه زنجیره‌تأمین خرما

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی صنایع، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار، گروه مهندسی صنایع، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران.

3 استاد گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشکدگان‌ فنی، دانشگاه تهران، تهران، ایران

4 استاد دانشکده مهندسی و علوم، دانشگاه صنعتی مونتری، مکزیک

چکیده

در سالیان اخیر، زنجیره‌های تأمین مواد غذایی و کشاورزی توجه محققان زیادی را به‌خود جلب کرده‌اند. زیرا این محصولات غالباً ارزش زیادی برای سهامداران خود فراهم می‌کنند. ازاین‌رو رویکرد جدیدی برای طراحی یک شبکه زنجیره‌تأمین حلقه بسته برای محصولات خرما در این مقاله توسعه یافته است. محصولات خرما و محصولات جانبی نیز در این شبکه برای استفاده در بازارهای هدف خود درنظر گرفته شده‌اند. دراین راستا، یک مدل ریاضی جدید برای بهینه‌سازی کل هزینه‌ها شامل هزینه‌های ثابت، هزینه‌های پردازش، هزینه‌های عملیاتی و هزینه‌های حمل‌ونقل در هردو جریان روبه جلو و معکوس فرموله شده است. نوآوری اصلی این مقاله درنظر گرفتن یک مدل شبکۀ زنجیره‌تأمین نوآورانه براساس ویژگی‌های منحصربه‌فرد محصول خرما بوده است. در این شبکه حلقه بسته، پایداری محصول خرما در شبکۀ زنجیره‌تأمین برای اولین‌بار مورد بررسی قرار گرفته است. همچنین دراین مدل برای اولین‌بار جمع‌آوری پسماند محصولات به مراکز بازیافت معرفی گردیده و برای حل مدل پیشنهادی مسأله، مجموعه‌ای از الگوریتم‌های فراابتکاری و ترکیبی به‌همراه استفاده از سالور سیپلکس استفاده شده‌ است. در ادامه نیز برای اعتبارسنجی مدل پیشنهادی و عملکرد این الگوریتم‌ها، چندین اندازۀ مسأله تولید و برازش انجام گردید. برای دست‌یابی به نتایج بهینه، پارامترهای الگوریتم‌های پیشنهادی براساس روش تاگوچی تنظیم شده و در پایان تحلیل‌ حساسیت انجام شده که نتایج آن، کاهش معنی‌داری در هزینه‌های کلی صنعت خرما با درنظر گرفتن محصولات جانبی و جمع‌آوری زباله در جریان معکوس را نشان می‌دهند.

کلیدواژه‌ها

عنوان مقاله [English]

Multi-objective Optimization in the Date Supply Chain Network Design

نویسندگان [English]

  • Abdolreza Hamdiasl 1
  • Hossein Amoozadkhalili 2
  • Reza Tavakkoli-Moghaddam 3
  • Mostafa Haji aghaie 4
1 PhD student in Industrial Engineering, Tehran Branch, Islamic Azad University, Tehran, Iran
2 Assistant Professor, Industrial Engineering Department, Sari Branch, Islamic Azad University, Sari, Iran
3 Professor, Faculty of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Ir
4 Professor at the Faculty of Engineering and Science, Universidad Tecnológico de Monterrey, Mexico
چکیده [English]

Recently, food and agricultural supply chains have attracted researchers’ attention as they provide more values for their stockholders. Hence a new approach to design a closed-loop supply chain network for date products is firstly developed in this work, which is one of the most well-known, rich, and desirable fruits. Date products and by-products are also considered in this network for the use in their target markets. In this regard, a new mathematical model is formulated to optimize the total costs including fixed, processing, operating, and transportation costs in both forward and reverse flows. The main contribution of this paper is to consider an innovative supply chain network model based on the unique characteristics of the date product. In this closed-loop network, the sustainability of the date product in the supply chain network is investigated for the first time. Also, product waste collection to the recycling centers is introduced in this model. To address the developed model, a set of meta-heuristic algorithms and a hybrid one along with the CPLEX solver of GAMS are utilized. Moreover, to validate the proposed model and the performance of these algorithms, several problem sizes are generated and solved. To achieve the best results, the parameters of the proposed algorithms are tuned based on the Taguchi method. Last but not least, sensitivity analyses are conducted and the results show a meaningful decrease in the overall costs of the date industry considering by-products and waste collection in the reverse flow.

کلیدواژه‌ها [English]

  • Network Design
  • Agricultural Supply Chain
  • Reverse Logistics
  • Meta-Heuristic Algorithm

مراجع

  • Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R. (2018). “The social engineering optimizer (SEO)”, Engineering Applications of Artificial Intelligence, 72: 267-293.
  • Weber, H., Wiek, A., & Lang, D. J. (2020). Sustainability entrepreneurship to address large distances in international food supply. Business Strategy & Development, 3(3): 318-331.
  • Hajiaghaei-Keshteli, M., Cheraghalipour, A. (2017). “An integrated approach for collection center selection in reverse logistics”, International Journal of Engineering, 30: 1005-1016.
  • Liao, Y., Kaviyani-Charati, M., Hajiaghaei-Keshteli, M., Diabat, A. (2020). “Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues”, Journal of Manufacturing Systems, 55: 199-220.
  • Cheraghalipour, A., Paydar, M.M., Hajiaghaei-Keshteli, M. (2019). “Designing and solving a bi-level model for rice supply chain using the evolutionary algorithms”, Computers and Electronics in Agriculture, 162: 651-668.
  • Motevalli-Taher, F., Paydar, M. M., & Emami, S. (2020). Wheat sustainable supply chain network design with forecasted demand by simulation. Computers and Electronics in Agriculture, 178, 105763.
  • FAO, fao.org/dateproduct (2021).
  • FAO, fao.org/dateproduct (2020).
  • Moniri, A., Yousefi Yegane, B. (2018). “Simultaneous Pricing, Routing, and Inventory Control for Perishable Goods in a Two-echelon Supply Chain”, International Journal of Engineering, 31: 1074-1081.
  • Stoecker, A., Seidmann, A., Lloyd, G. (1985). “A linear dynamic programming approach to irrigation system management with depleting groundwater”, Management Science, 31: 422-434.
  • Miller, W., Leung, L., Azhar, T., Sargent, S. (1997.( “Fuzzy production planning model for fresh tomato packing”, International Journal of Production Economics, 53: 227-238.
  • Ekman, S. (2000). “I—Information Technology: Tillage System Selection: a Mathematical Programming Model incorporating Weather Variability”, Journal of Agricultural Engineering Research, 77: 267-276.
  • Caixeta-Filho, J.V. (2006). “Orange harvesting scheduling management: a case study”, Journal of the Operational Research Society, 57: 637-642.
  • Apaiah, R. K., Hendrix, E. M. (2005). “Design of a supply chain network for pea-based novel protein foods”, Journal of Food Engineering. 55: 199-220.
  • Ferrer, J.C., Mac Cawley, A., Maturana, S., Toloza, S., Vera, J. (2008). “An optimization approach for scheduling wine grape harvest operations”, International Journal of Production Economics, 112: 985-999.
  • Arnaout, J.P.M., Maatouk, M. (2010). “Optimization of quality and operational costs through improved scheduling of harvest operations”, International Transactions in operational research, 17: 595-605.
  • Ahumada, O., Villalobos, J. R. (2011). “Operational model for planning the harvest and distribution of perishable agricultural products”, International Journal of Production Economics, 133: 677-687.
  • Tan, B., Çömden, N. (2012). “Agricultural planning of annual plants under demand, maturation, harvest, and yield risk”, European Journal of Operational Research, 220: 539-549.
  • Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M. (2013). “A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach”, Computers and Electronics in Agriculture, 93: 37-45.
  • Agustina, D., Lee, C., Piplani, R. (2014). “Vehicle scheduling and routing at a cross docking center for food supply chains”, International Journal of Production Economics, 152: 29-41.
  • González-Araya, M. C., Soto-Silva, W. E., Espejo, L.G.A. (2015). “Harvest planning in apple orchards using an optimization model”, In Handbook of operations research in agriculture and the agri-food industry. 133:79-105
  • Cheraghalipour, A., Paydar, M. M., Hajiaghaei-Keshteli, M. (2018). “A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms”, Applied Soft Computing, 33: 59-63.
  • Amiri, S. A. H. S., Zahedi, A., Kazemi, M., Soroor, J., Hajiaghaei-Keshteli, M. (2021). “Determination of the optimal sales level of perishable goods in a two-echelon supply chain network”, Computers & Industrial Engineering, 139: 106-156.
  • Jabarzadeh, Y., Yamchi, H.R., Kumar, V., Ghaffarinasab, N. (2020). “A multi-objective mixed-integer linear model for sustainable fruit closed-loop supply chain network”, Management of Environmental Quality: International Journal. 93: 37-45.
  • Zhao, R., Liu, Y., Zhang, Z., Guo, S., Tseng, M. L., & Wu, K. J. (2018). Enhancing eco-efficiency of agro-products’ closed-loop supply chain under the belt and road initiatives: A system dynamics approach. Sustainability, 10(3), 668.Paksoy, T., Pehlivan, N. Y., Özceylan, E. (2012). “Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer”, Applied Mathematical Modelling, 36: 2762-2776.
  • Rocco, C. D., Morabito, R. (2016). “Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model”, Computers and Electronics in Agriculture, 127: 763-774.
  • Soto-Silva, W. E., González-Araya, M. C., Oliva-Fernández, M. A., Plà-Aragonés, L. M. (2017). “Optimizing fresh food logistics for processing: Application for a large Chilean apple supply chain”, Computers and Electronics in Agriculture. 53: 227-238.
  • Ghezavati, V., Hooshyar, S., Tavakkoli-Moghaddam, R. (2017). “A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato”, Central European Journal of Operations Research, 25: 29-54.
  • Ma, X., Wang, S., Islam, S. , Liu, X. (2019). “Coordinating a three-echelon fresh agricultural products supply chain considering freshness-keeping effort with asymmetric information”, Applied Mathematical Modelling, 67: 337-356.
  • Roghanian, E., Cheraghalipour, A. (2019). “Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions”, Journal of Cleaner Production, 239: 81-118.
  • Jifroudi, S., Teimoury, E., Barzinpour, F. (2020). “Designing and planning a rice supply chain: a case study for Iran farmlands”, Decision Science Letters, 9: 163-180.
  • Yan, B., Chen, X., Cai, C., Guan, S. (2020). “Supply chain coordination of fresh agricultural products based on consumer behavior”, Computers & Operations Research, 123: 105-138.
  • Chavez, M. M. M., Sarache, W., Costa, Y., Soto, J. (2020). “Multiobjective stochastic scheduling of upstream operations in a sustainable sugarcane supply chain”, Journal of Cleaner Production, 276: 123-305.
  • Salehi-Amiri, A., Zahedi, A., Gholian-Jouybari, F., Calvo, E. Z. R., & Hajiaghaei-Keshteli, M. (2022). Designing a closed-loop supply chain network considering social factors; a case study on avocado industry. Applied Mathematical Modelling, 101: 600-631.
  • Rufí-Salís, M., Petit-Boix, , Villalba, G., Sanjuan-Delmás, D., Parada, F., Ercilla-Montserrat, M., Gabarrell, X. (2020). “Recirculating water and nutrients in urban agriculture: An opportunity towards environmental sustainability and water use efficiency”, Journal of Cleaner Production, 123: 121-145.
  • Ricardi, P. (2020). Trade and Consumer Goods. In An Archaeology of Nineteenth-Century Consumer Behavior in Melbourne, Australia, and Buenos Aires, Argentina (pp. 165-183). Springer, Cham.
  • Moretti, B., Bertora, C., Grignani, C., Lerda, C., Celi, L., Sacco, D. (2020). “Conversion from mineral fertilisation to MSW compost use: nitrogen fertiliser value in continuous maize and test on crop rotation”, Science of The Total Environment, 705: 135-308.
  • He-Lambert, L., Shylo, O., English, B. C., Eash, N. S., Zahn, J. A., Lambert, D. M. (2019). “Supply chain and logistic optimization of industrial Spent Microbial Biomass distribution as a soil amendment for field crop production. Resources”, Conservation and Recycling, 146: 218-231.
  • Kim, T., Glock, C. H., Kwon, Y. (2014). “A closed-loop supply chain for deteriorating products under stochastic container return times”, Omega, 43: 30-40.
  • Hajiaghaei-Keshteli, M., Aminnayeri, M. (2013). “Keshtel Algorithm (KA); a new optimization algorithm inspired by Keshtels’ feeding”, Paper presented at the Proceeding in IEEE Conference on Industrial Engineering and Management Systems, 123: 121-145.
  • Hajiaghaei-Keshteli, M., Aminnayeri, M. (2014). “Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm”, Applied Soft Computing, 25: 184-203.
  • Kennedy, J., Eberhart, R. (1995). “Particle swarm optimization”, Paper presented at the Proceedings of ICNN'95-International Conference on Neural Networks. 55: 199-220.
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2): 182-197.
  • Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes (No. 658.562 T3).
  • Anggraeni, E. W., Handayati, Y., & Novani, S. (2022). Improving Local Food Systems through the Coordination of Agriculture Supply Chain Actors. Sustainability, 14(6):
  • Baghizadeh, K., Cheikhrouhou, N., Govindan, K., & Ziyarati, M. (2022). Sustainable agriculture supply chain network design considering water‐energy‐food nexus using queuing system: A hybrid robust possibilistic programming. Natural Resource Modeling, 35(1):
  • Latino, M. E., Menegoli, M., Lazoi, M., & Corallo, A. (2022). Voluntary traceability in food supply chain: a framework leading its implementation in Agriculture 4.0. Technological Forecasting and Social Change, 178:
  • Karras, A., Karras, C., Drakopoulos, G., Tsolis, D., Mylonas, P., & Sioutas, S. (2022). SAF: A Peer to Peer IoT LoRa System for Smart Supply Chain in Agriculture. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 41-50). Springer, Cham.
  • Yadav, V. S., Singh, A. R., Gunasekaran, A., Raut, R. D., & Narkhede, B. E. (2022). A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustainable Production and Consumption, 29: 685-704.
  • قندی بیدگلی، س.، امینی م. (2021). "ارائه مدل زمان‌بندی چندعاملی درمحیط جریان کارگاهی بافرض زوال‌پذیری کارها، زمان‌های آماده‌سازی وابسته به توالی و زمان آزادسازی کارها بااستفاده از الگوریتم ازدحام ذرات چندهدفه." نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید 9(18): 59-79.

ثقه ئی, ا. و همکاران. (2021). "ارائه رویکرد برنامه‌ریزی دوسطحی چندپیرو درحالت عدم همکاری برای موقعیت‌یابی از پیش‌انبارهای اضطراری بحران." نشریه پژوهش های مهندسی صنایع در سیستم‌های تولید 9(18): 81-95

نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 9، شماره 19
اسفند 1400
صفحه 137-153

فایل ها

هم رسانی

ارجاع به این مقاله

آمار