نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، مدیریت صنعتی، دانشگاه آزاد اسلامی، واحد تهران جنوب

2 استاد، گروه مدیریت صنعتی، ، دانشگاه علامه طباطبائی، تهران.

3 استادیار، دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران.

چکیده

یکپارچه سازی تصمیمات در زنجیره های تامین منجر به کاهش هزینه ها و زمان تحویل سفارشات به مشتریان می شود ولی عدم قطعیت در پارامترهای مختلف از جمله تقاضا، آن را دچار اختلال می کند. عدم قطعیت در تقاضا، منجر به افزایش علاقه‌مندی در استفاده از مدل‌های برنامه‌ریزی احتمالی و شبیه سازی در مدل سازی زنجیره تامین شده است. هدف کلی از این پژوهش، مدل‌سازی و توسعه مدل شبکه زنجیره‌تأمین چند سطحی، چند محصولی، چند دوره‌ای با اهداف متعارض همچون کمینه‌کردن هزینه، کمینه کردن زمان تحویل و بیشینه‌کردن قابلیت اطمینان کل سیستم است. شبکه زنجیره ‌تأمین مورد بررسی دارای 4 سطح تأمین‌کنندگان، تولید‌کنندگان، توزیع‌کنندگان و خرده‌فروشان است. در این پژوهش، فرض این بود که تقاضا تصادفی و از یک تابع توزیع احتمالی پیروی می کند. بنابراین از تکنیک شبیه سازی برای برآورد هزینه ها شامل هزینه‌های حمل و نقل و هزینه فروش از دست رفته و سایر هزینه‌ها استفاده شد. پس از تدوین مدل چندهدفه، برای مسئله مورد بررسی سناریوسازی بر اساس دیدگاه های مختلف نسبت به سطوح موجودی (حداقل موجودی، حداکثر موجودی و سطح متوسط موجودی) صورت گرفت و بر اساس هر سناریو مقادیر مرتبط با اهداف مورد بررسی برآورد شد. در انتها نیز بر اساس جواب های پارتوی بدست آمده برای هر حالت از مدل،با روش تصمیم گیری ویکور به رتبه بندی نهایی جواب های و انتخاب بهترین حالت از مدل پیشنهادی اقدام شد.نتایج حاکی از آن بود که سناریوی دوم یعنی در نظر گرفتن سطح متوسط موجودی به عنوان بهترین راهکار برای مدل تشریح شده انتخاب شد.

کلیدواژه‌ها

عنوان مقاله [English]

Development of Multi-Objective Supply Chain Model with Stochastic Demand: An Optimization Approach Based on Simulation and Scenario Development

نویسندگان [English]

  • Shima Salehi 1
  • Mohammad Taghi Taghavifard 2
  • Ghanbar Abbaspour esfeden 3
  • a alirezaee 3

1 PhD Student, Industrial Management, Islamic Azad University, South Tehran Branch

2 Professor, Department of Industrial Management, Allameh Tabataba'i University, Tehran.

3 Assistant Professor, Faculty of Management and Accounting, Islamic Azad University, South Tehran Branch, Tehran.

چکیده [English]

The integration of supply chain decisions aims to reduce costs and delivery time for customers. However, uncertainty in supply chain parameters, particularly demand, can disrupt this integration. The increased interest in probabilistic planning and simulation models in supply chain modeling is a response to this demand uncertainty. Therefore, the main objective of this study was to develop a multi-level, multi-product, multi-period supply chain network model that considers conflicting objectives such as cost minimization, delivery time minimization, and system-wide reliability maximization. The supply chain network under investigation consisted of four levels or subsystems: suppliers, manufacturers, distributors, and retailers. In this study, it was assumed that demand follows a random probabilistic distribution function. Consequently, simulation techniques were employed to estimate costs, including shipping costs, lost sales costs, and other expenses. After developing the multi-objective model, various scenarios were created based on different perspectives of inventory levels, namely minimum inventory, maximum inventory, and average inventory level. For each scenario, objective-related values were estimated. Ultimately, based on the Pareto optimal solutions obtained for each case of the model, the Vickor decision-making method was used to rank the answers and select the best solution from the proposed model. The results indicated that the second scenario, considering the average inventory level, was identified as the optimal solution for the described model.
Introduction
Today, supply chain management (SCM) encompasses the entire production planning process for the supply chain, from raw material suppliers to the final customer. This has become a focal point for numerous researchers. In most supply chain designs, the objective has been to transfer products from one layer to another in order to meet strategic, tactical, and operational demands while minimizing complications arising from interrelationships and uncertainties across the chain. These challenges have posed significant decision-making hurdles in the supply chain domain. Supply chains can be regarded as complex systems wherein various factors interact with each other, resulting in emergent properties. Designing a versatile supply chain to address conflicting and diverse objectives requires considering them simultaneously and striking a balance among different criteria. The dynamic and intricate nature of the supply chain introduces a high level of uncertainty, thereby impacting the decision-making process in supply chain planning and influencing overall network performance. Based on the aforementioned issues, the focus of investigation includes the following: The examined supply chain network comprises four levels or subsystems, namely suppliers, manufacturers, distributors, and retailers. Raw materials are sourced from suppliers and sent to production factories, where each product is manufactured using a specific combination of raw materials. The products are then transported from manufacturers to distribution centers, and subsequently forwarded to retailers. The market is divided into different regions, and customer demands are fulfilled through visits to the retailers. Demand is assumed to be random and follows a probability distribution pattern. Consequently, simulation techniques are employed to estimate costs, including transportation costs, lost sales costs, and other expenses. Scenarios are created based on different perspectives at each level, focusing on inventory levels (minimum, maximum, and average). For each scenario, the values associated with the investigated objectives are estimated.
Materials and methods
In this research, data collection involved the examination of relevant literature, including articles published in international journals, books, and treatises. Documentary studies were conducted to gather information. To analyze the collected data, simulation and multi-objective programming concepts and methods were employed. Minitab and ED software were utilized for statistical analysis and simulation purposes.
Conclusions
Considering that the model can be solved under different conditions, including the current situation and various scenarios, the answers obtained for each state are Pareto optimal. This means that it is not possible to determine a single best answer for each state of the model. Therefore, before comparing the scenarios with each other, the Pareto optimal answers for each scenario should be ranked to identify the best options. In this research, a model for designing the supply chain network was presented, taking into account demand randomness. To better understand the proposed model and demonstrate its practicality, numerical examples were examined and evaluated using different scenarios and the Lingo software. It is important to note that the developed model in this study is independent of the number of facilities at each level of the supply chain and the parameter values. Therefore, the general form of this model can be applied to any production environment that aligns with the patterns presented in this research. The proposed model initially employed the design of experiments to estimate the mathematical relationship related to the cost objective function. After developing the multi-objective model, the Lingo software was used to solve the sample problem and evaluate the results under different scenarios. Finally, based on the Victor decision-making method, the Pareto optimal solutions for each state of the model were used to rank the answers and determine the best mode for the proposed models. Based on the obtained results, the third option or the second scenario is suggested as the preferred choice for the described model, considering the index values associated with each option

کلیدواژه‌ها [English]

  • supply chain
  • multi-objective model
  • stochastic demand
  • simulation-based optimization
  • scenario
اختیاری، مصطفی. (1389). مدیریت زنجیره تأمین سه سطحی تحت عدم قطعیت با استفاده از برنامه‌ریزی فازی چند هدفه. مطالعات مدیریت صنعتی، سال هشتم، شماره 18، پاییز 1389، صفحات 123 تا 160.
اشتدلر، هارتموت و کیلگر، کریستوف (2005) مدیریت زنجیره تأمین» ترجمه نسرین عسگری و رضا فراهانی، انتشارات دانشگاه صنعتی امیرکبیر، چاپ اول 1385
افشاری نیا، زهرا؛ توکلی مقدم، رضا و قلی پور کنعانی، یوسف (1392). استفاده از روش تجزیه بندرز به حل مسئله طراحی شبکه زنجیره تأمین چندمحصولی دوسطحی با تقاضای تصادفی. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. سال اول، شماره دوم، پاییز و زمستان 1392، صص 155-165.
امیرخان، محمد؛ نورنگ، احم و توکلی مقدم، رضا (1394). یک رویکرد برنامه‌ریزی تعاملی فازی برای طراحی شبکه زنجیره تأمین چندسطحی، چندکالایی و چند دوره‌ای تحت شرایط عدم قطعیت با در نظر گرفتن هزینه و زمان. مدیریت تولید و عملیات، دوره ششم، شماره (1)، پیاپی (10)، بهار و تابستان 1394.
بهنامیان، جواد و بشر، محمدمهدی. (1396) مدل‌سازی چندمرحله‌ای مسئله زنجیره تأمین سه سطحی غیرهمکارانه با در نظر گرفتن تخفیف در شرایط عدم قطعیت. پژوهش‌های نوین در تصمیم‌گیری. دوره 2، شماره 3، پاییز 1396.
روی بیلینتون، رونالد آلن (1390). ارزیابی قابلیت اطمینان سیستم‌های مهندسی؛ مفاهیم و روش‌ها؛ مترجم محسن رضائیان. تهران: دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران)، مرکز نشر.
صادقیان، رامین و طالبی لنگرودی، گلناز (1396). ارائه یک مدل موجودی در زنجیرة تأمین سه‌سطحی با در نظر گرفتن تقاضای احتمالی. مهندسی صنایع و مدیریت، دوره 33.1، شماره 1.2، بهار و تابستان 1396، صفحه 101-112.
مظاهری، علی؛ کرباسیان، مهدی؛ سجادی، سیدمجتبی، شیرویه زاد، هادی و عابدی، سعید (1393). ارائه مدلی جهت بهینه‌سازی زنجیره تأمین یکپارچه با استفاده از روش برنامه‌ریزی تصادفی چندهدفه. مهندسی صنایع و مدیریت تولید، شماره 2، جلد 25، شهریورماه 1393. صص 186-204.
Aguirre, Adri´an M., Liu, Songsong, Papageorgiou,Lazaros G., (2018).Optimisation Approaches for Supply Chain Planning and Scheduling under Demand Uncertainty.Chemical Engineering Research and Design, 138: 341-357
Aliev, R. A., Fazlollahi, B., Guirimov, B. G. Aliev, R. R. (2007). "Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management". Informaton Sciences, 177, 4241-4255.
Aqlan, F., & Lam, S. S. (2016). Supply chain optimization under risk and uncertainty: A case study for high-end server man-ufacturing. Computers & Industrial Engineering, 93, 78-87.
Beamon, B. M. (1998). Supply chain design and analysis: Models and Methods. International Journal of Production Economics, 55(3), 281-294.
Bilge, B. (2010).Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Systems with Applications, 37(6):4488-4495.
Billala M. M., & Hossaina M. M. (2020). Multi-objective Optimization for Multi-product Multi-period Four Echelon Supply Chain Problems under Uncertainty. Journal of Optimization in Industrial Engineering, 13(1), 1-17.
Burkovskis, R. (2008). Efficiency of freight forwarder’s participation in the process of transportation, Transport, 23(3): 208–213.
Chen,S.P., Chang,P.C. (2006). "A mathematical programming approach to supply chain models with fuzzy parameters". Engineering Optimization, 38, 647-669.
Coskun, S., Ozgur, L., Polat, O. and Gungor, A., (2015). A model proposal for green supply chain network design based on consumer segmentation. Journal of Cleaner Production.38(2), 136-146.
Ehm J, Scholz-Reiter B, Makuschewitz T, Frazzon E M. (2015). Graph-Based Integrated Production and Intermodal Transport Scheduling with Capacity Restrictions. CIRP Journal of Manufacturing Science and Technology, 23-30.
Felfel, H., Ayadi, O., & Masmoudi, F. (2016). Multi-objective stochastic multi-site supply chain planning under demand un-certainty considering downside risk. Computers & Industrial Engineering, 102, 268-279.
Gibson, B.J., Mentzer, J.T. and Cook, R.L. (2005). Supply chain management: the pursuit of a consensus definition. Journal of Business Logistics, Vol. 26 No. 2, pp. 17-25
Hugos M., (2006),"essential of supply chain management", second edition, published by John Wiley & Sons Inc.
Liang, T. F. Chen, H. W. (2008). Application f fuzzy sets to manufacturing/distribution planning decision with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36, 3367-3377.
Liang, T. F. Chen, H. W. (2013). Application f fuzzy sets to manufacturing/distribution planning decision with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36, 3367-3377.
Lukinskiy, V.S., Lukinskiy, V.V., Churilov, R. (2007). Problem of the Supply Chain Reliability Evaluation. Transport and Telecommunication. 15(2):120-129.
Lukinskiy, V.S., Lukinskiy, V.V., Churilov, R. (2014). "Problem of the Supply Chain Reliability Evaluation". Transport and Telecommunication. Vol.15. no.2:120-129.
Miao, X., Yu, B., Xi, B. (2009). "The Uncertainty Evaluation Method Of Supply Chain Reliability". Transport. 24(4):296-300.
Mirotin, L.B., Sergeev, V.I. (2002). Principles of Logistics. M.: INFRA-M.
Mohammadi Bidhandi, H., & Yusuff R.M. (2011). Integrated Supply Chain Planning under Uncertainty using an Improved Stochastic Approach. Applied Mathematical Modelling, 35(6):2618–2630
Ozkan, O. & Kilic, S. (2019). A Monte Carlo Simulation for Reliability Estimation of Logistics and Supply Chain Networks. IFAC Papers On Line, 52-13,2080–2085.
Pasandideh, S, H, R., Akhavan Niaki, S, T.,Asadi, K. (2015) "Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability". Expert Systems with Applications, 42:2615-2623.
peidro, D., Mulla, J., Poler, R., Verdegay, J, L. (2009). Fuzzy optimization for supply chain planning under supply, demand, and process uncertainties. fuzzy sets and systems, 160, 2640-2657.
Quigley, J.; Walls, L. 2007. Trading reliability targets within a supply chain using Shapley’s value, Reliability Engineering & System Safety 92(10): 1448–1457.
Salema, M., Barbosa-Povoa, A.P., Noavais, A. (2009). "A strategic and tactical model for closed-loop supply chains. OR Spectrum, 31, 573-599.
Svoronos, A., Zipkin. P. (1995). "Evaluation of One-for-One Replenishment Policies for Multiechlon Inventory Systems. Management Science, 37(1), 68-83.
Terzi, S., &  Cavalieri, S. (2004). Simulation in the Supply Chain Context: A Survey. Computers in Industry, 53(1):3-16.
Thomas, A., & Charpentier, P. (2005). Reducing simulation models for scheduling manufacturing facilities”, European Journal of Operation Research, 161, pp. 111-125.
Van Nieuwenhuyse, I., &  Vandaele, N.  (2006) The impact of delivery lot splitting on delivery reliability in a two-stage supply chain. International Journal of Production Economics, 104(2):694-708
Wolfgang, K., Thorsten, B. (2006). "Managing Risks in Supply Chains. How to Build Reliable Collaboration in Logistics". Berlin: Erich Schmidt Verlag.
Zaitzev, E.I., Bochazev, A.A. (2010). Optimizing supply-planning in multi-level network structures in the light of reliability. Logistics and Supply Chain Management, 2 (37).
Zaitzev, E.I., Uvarov, S.A. (2012). Using indicators of Perfect Order Fulfillment in the distribution logistics. Logistics and Supply Chain Management, 4(51).
Zhang, M., Chen, J., & Chang Sh-H. (2020). An adaptive simulation analysis of reliability model for the system of supply chain based on partial differential equations. Alexandria Engineering Journal. Article in press.
Pathak, C., Mukherjee, S., and Kumar Ghosh, S. (2020). A Three Echelon Supply Chain Model with Stochastic Demand Dependent on Price, Quality and Energy Reduction. Journal of Industrial and Management Optimization.1-17.
Chen, S., Wang, W., & Zio, E. (2021). A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains. Energies, 14,2684.
Arman Sajedinejad, Erfan Hassannayebi, Mohammad Saviz Asadi Lari. (2020). Simulation based optimization of multi-product supply chain under a JIT system. Journal of Industrial Engineering and Management Studies. 7(1), pp. 87-106.
Nayeri S., Torabi S. A., Tavakoli, M., & Sazvar Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 311,127691.
Ibrahim Alharkan, Mustafa Saleh, Mageed Ghaleb, Abdulsalam Farhan and Ahmed Badwelan (2020). Simulation-Based Optimization of a Two-Echelon Continuous Review Inventory Model with Lot Size-Dependent Lead Time. Processes, 8, 1014.
Govindan, K., Mina, H., Esmaeili, A., Gholami-Zanjani, S.M., 2020. An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. J. Clean. Prod. 242, 118317.
Jouzdani, J., Govindan, K., (2020). On the sustainable perishable food supply chain network design: a dairy products case to achieve sustainable development goals. J. Clean. Prod. 278, 123060.
Hosseini-Motlagh, S.-M., Samani, M.R.G., Saadi, F.A., 2020. A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Comput. Electron. Agric. 168, 105095.
Vafaei, A., Yaghoubi, S., Tajik, J., Barzinpour, F., 2020. Designing a sustainable multi-channel supply chain distribution network: a case study. J. Clean. Prod. 251, 119628
Ekhtiari, M. (2010). Three-echelon Supply Chain Management under Uncertainty Using Multi-Objective Fuzzy Programming. Industrial Management Studies, 8(18), 123-160. [In Persian]
Schedler, Hartmut and Kielger, Christoph (2005). Supply Chain Management. translated by Nasrin Asgari and Reza Farahani, Amirkabir University of Technology Publications, first edition. [In Persian]
Tavakkoli-Moghaddam, R., Afsharinia, Z., Gholipour-Kanani, Y. (2013). Use of a Benders decomposition method for solving a two-echelon multi-commodity supply chain network design problem with stochastic demands. Journal of Industrial Engineering Research in Production Systems, 1(2), 155-165. [In Persian]
Amirkhan, M., Norang, A., Tavakkoli-Moghaddam, R. (2015). An Interactive Fuzzy Programming Approach for Designing a Multi-Echelon, Multi-Product, Multi-Period Supply Chain Network Under Uncertainty Considering Cost and Time. Journal of Production and Operations Management, 6(1), 127-148. [In Persian]
Behnamian, J., Bashar, M. (2017). Multi-stage modeling for non-cooperative multi-echelon supply chain management problem with discount under uncertainty. Modern Research in Decision Making, 2(3), 49-75. [In Persian]
Roy Billinton, Ronald Allen (1390). Reliability assessment of engineering systems; Concepts and methods. Translated by Mohsen Rezaian. Tehran: Amirkabir University of Technology (Tehran Polytechnic), Publishing Center. [In Persian]
S‌a‌d‌e‌g‌h‌i‌a‌n, R., T‌a‌l‌e‌b‌i L‌a‌n‌g‌a‌r‌o‌u‌d‌i, G. (2017). A‌n inventory model for a three-stage supply chain under stochastic demand. Sharif Journal of Industrial Engineering & Management, 33.1(1.2), 101-112. [In Persian]
Azimi P., Esmati A., & Farajpournazari, M. (2013). Optimization through simulation with comprehensive ED simulation software training. Islamic Azad University of Qazvin. [In Persian]