سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات163
تعداد شماره‌ها6,767
تعداد مقالات72,886
تعداد مشاهده مقاله132,097,534
تعداد دریافت فایل اصل مقاله103,651,906
Kazemian, Mina, Afsharkazemi, Mohammad Ali, Fathi Hafashjani, Kiamars, Motadel, Mohammadreza. (1402). Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms. , 15(3), 478-507. doi: 10.22059/imj.2023.356697.1008039
Mina Kazemian; Mohammad Ali Afsharkazemi; Kiamars Fathi Hafashjani; Mohammadreza Motadel. "Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms". , 15, 3, 1402, 478-507. doi: 10.22059/imj.2023.356697.1008039
Kazemian, Mina, Afsharkazemi, Mohammad Ali, Fathi Hafashjani, Kiamars, Motadel, Mohammadreza. (1402). 'Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms', , 15(3), pp. 478-507. doi: 10.22059/imj.2023.356697.1008039
Kazemian, Mina, Afsharkazemi, Mohammad Ali, Fathi Hafashjani, Kiamars, Motadel, Mohammadreza. Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms. , 1402; 15(3): 478-507. doi: 10.22059/imj.2023.356697.1008039

Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms

Industrial Management Journal
مقاله 5، دوره 15، شماره 3، 2023، صفحه 478-507    اصل مقاله (1.46 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/imj.2023.356697.1008039
نویسندگان
Mina Kazemian1؛ Mohammad Ali Afsharkazemi* 2؛ Kiamars Fathi Hafashjani3؛ Mohammadreza Motadel4
1Ph.D. Candidate, Department of Industrial Management, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2, Associate Prof., Department of Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3Assistant Prof., Department of Industrial Management, Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
4Assistant Prof., Department of Industrial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده
Objective
Supply chain management is a modern organizational management mode that organizes and plans information, capital flow, and business partnerships in the supply chain and requires complete business and market information (Quinn et al., 2012). However, the cost of acquiring supply chain companies and product information by traditional methods is very high. Information technology provides the power for companies to implement supply chain management and share the supply chain easily, and all companies in the supply chain can create value through information management (Shawaki et al., 2023). The utilization of intelligent approaches to predict prices and demand quantities enhances supplier delivery performance. It also refines demand forecasting accuracy, improves factory planning precision, forecasts demand for new products, and minimizes supplier risks, transportation costs, inventory, operational expenses, and time (Tirklai et al., 2021). In supply chain management, accurate forecasting of demand reflects the price. It is a critical issue that can reduce inventory costs and achieve the desired service level (Zouqaq et al., 2020). Intelligent supply chain pricing approaches can help supply chain companies to adapt the quality of their product offerings in supply chain management according to the knowledge gained (Kotsiopoulos et al., 2021). Identifying and modeling steel market fluctuations is very important in the steel industry and supply chain management. Considering the vertical chain in this industry and the interaction between the players of this industry, game theory has been used to model the optimal price. Neural network models were employed to replicate the game, as interaction and repeated gameplay are required for achieving balance among players. Taking into account Iran's unique circumstances, notably its confrontations with substantial sanctions in the metal industry, the sanctions variable was integrated as an adjusting factor in the pricing model for this sector.
 
Methods
This is a practical study. The research time frame for predicting steel prices and calculating the sanctions index spans from 2011 to 2020, with quarterly data. The MATLAB software was used.
 
Results
Three Bayesian neural networks, support vectors, and Grassberg's anti-diffusion were used to predict the price of steel. The results showed that the Grossberg anti-diffusion model is more accurate in predicting steel prices. Next, the predicted price entered the game theory process and the Nash equilibrium point of the model was determined. According to the country's specific conditions, the sanctions variable was introduced in the game theory model. The results showed that the inclusion of sanctions in the model led to price increases and production reductions within the steel industry. The present study delved into price fluctuations resulting from shifts in supply and demand, particularly in the context of sanctions. The findings reveal that a reduction in supply coupled with escalated sanctions led to substantial price hikes, surpassing the impact of supply changes. Consequently, steel exhibits a heightened susceptibility to input constraints, where any disruption in its supply chain triggers significant price spikes, thus unsettling the market. This amplifies the sensitivity of supply chain management for steel. Consequently, a systemic and dynamic approach is essential for market regulation policies, raw material supply, transportation strategies, and warehousing considerations. It should be noted that the use of intelligent approaches and machine learning can play a significant role in coordinating such issues.
 
Conclusion
Considering that Stackelberg's approach was used in the current research, the sequence of players' entry into the game holds significance with respect to the Nash equilibrium. The development of market entry monitoring rules and regulations in this industry should be investigated because the steel industry is one of the industries that face high entry and exit costs. As a result, Policymakers and industry managers should monitor the entry and exit of players within this sector. They should endeavor to establish norms and regulations governing interactions among market participants to foster a structured and well-defined competitive environment.
کلیدواژه‌ها
Game theory؛ Optimal price؛ Neural network؛ Steel industry
مراجع
منابع

دری، محسن؛ جعفری، ‌میثم و چهارسوقی، کمال (1398). انتخاب خط‌مشی سفارش هماهنگ‌شده در زنجیره تأمین دو سطحی: رویکرد نظریه بازی. تحقیقات مدرن در تصمیم‌‌گیری، 4(3)، 47- 73.

سلطانی تهرانی، الهه و دایی کریم‌زاده، سعید (1394). پیش‏بینی قیمت فولاد با استفاده از مدل سری زمانی. کنفرانس بین‌المللی مدیریت و اقتصاد در قرن بیست‌و‌‌‌یکم، تهران.

شاکری، عباس (1387). کتاب اقتصاد خرد 2: نظریه‌ها و کاربردها، تهران، نشر نی.

شفیعی، مرتضی و فرح گل، پوریا (1398). کاربرد تئوری بازی درتحلیل زنجیرۀ تأمین با رویکرد بازار مشتری ( مورد مطالعه: سیمان فارس). مطالعات مدیریت صنعتی، 17(53)، 185-217.

متین‌فر، فرزام؛ آزادی پرند، فرشته و لونی، عبداله (1398)، مروری بر رویکردهای نظریه بازی در شبکه توزیع هوشمند با تأکید بر بازی‌های همکارانه. صنایع الکترونیک، 10 (3)، 17-29.

نعیمی صدیق، علی؛ چهارسوقی، سید کمال و شیخ محمدی، مجید (1391). طراحی مدل هماهنگی در زنجیرۀ تأمین رقابتی با استفاده از رویکرد نظریه بازی با همکاری و بدون همکاری. مدیریت صنعتی، 4(14)، 108-118.

نویدی، حمیدرضا و رحمتی، علی (1392). ارائه مدل رقابتی فروش چند جانبه در زنجیره‌های تأمین و تحلیل آن با استفاده از نظریه بازی‌ها، دهمین کنفرانس بین‌المللی هندسی صنایع، تهران. 1-7.

 

References

Abapour, S., Mohammadi-Ivatloo, B. & Hagh, M. T. (2020). Robust bidding strategy for demand response aggregators in the electricity market based on game theory. Journal of Cleaner Production, 243(7), A. 118393.

Abdellaoui, M., Li, C., Wakker, P. P. & Wu, G. (2020). A defense of prospect theory in Bernheim and Sprenger’s experiment. Working paper. Rotterdam, Netherlands.

Ageron, B., Gunasekaran, A. & Spalanzani, A. (2012). Sustainable supply management: An empirical study. International Journal of Production Economics, 140(1), 168-182.

Álvarez, X., Gómez-Rúa, M. & Vidal-Puga, J. (2019). River flooding risk prevention: A cooperative game theory approach. Journal of Environmental Management, 248. https://doi.org/10.1016/j.jenvman.2019.109284

Amer, M., Tsotskas, Ch., Hawes, M., Franco, P. & Mihaylova, L. (2017). A game theory approach for congestion control in vehicular ad hoc networks. 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1-6. 10.1109/SDF.2017.8126359

Amin, W., Huang, Q., Afzal, M., Khan, A. A., Zhang, Z., Umer, K. & Ahmed, S. A. (2020). Consumers’ preference-based optimal price determination model for P2P energy trading. Electric Power Systems Research, 187, A. 106488.

Ansari, Z. N. & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of cleaner production, 142, 2524-2543.

Bai, C. & Sarkis, J. (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124(1), 252- 264.

Bousqaoui, H., Slimani, I. & Achchab, S. (2021). Comparative analysis of short-term demand predicting models using ARIMA and deep learning. International Journal of Electrical and Computer Engineering (IJECE), 11(4), 3319–3328.

Brandenburg, M., Govindan, K., Sarkis, J. & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of operational research, 233(2), 299-312.

Charmchi, A.S., Ifaei, P. & Yoo, C. (2021). Smart supply-side management of optimal hydro reservoirs using the water/energy nexus concept: a hydropower pinch analysis. Appl Energy 281, 116136. https://doi.org/10.1016/j.apenergy.2020.116136

Chen, J., Zhang, H. & Sun, Y. (2012). Implementing coordination contracts in a manufacturer Stackelberg dual-channel supply chain. Omega, 40(5), 571-583.

Chen, P., Li, B., Jiang, Y. & Hou, P. (2017). The Impact of Manufacturer’s Direct Sales and Cost Information Asymmetry in a Dual-Channel Supply Chain with a Risk-Averse Retailer. International Journal of Electronic Commerce, 21(1), 43-66.

Chen, X., Cai, G. & Song, J. S. (2019). The cash flow advantages of 3PLs as supply chain orchestrators. Manufacturing & Service Operations Management, 21(2), 435-451.

Chien, C.F., Lin, Y.S., Lin, S.K. (2020). Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. International Journal of Production Research, 58(9), 2784–2804.

De Boer, R. Steeman, M. & van Bergen, M. (2015). Supply chain finance, it’s practical relevance and strategic value: the supply chain finance essential knowledge series. Hogeschool Windesheim. Zwolle, Netherlands.

Dorri, M., Jafari, M. & Chaharsoghi, K. (2019). Choosing coordinated ordering policy in the two-level supply chian: A game theory approach. Modern research in decision making, 4(3), 47-73. (in Persian)

Du, W., Fan, Y. & Yan, L. (2018). Pricing Strategies for Competitive Water Supply Chains under Different Power Structures: An Application to the South-to-North Water Diversion Project in China. Sustainability, 10(8), 1-13.

Ghavamifar, A., A. Makui & A.A. Taleizadeh (2018). Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review, 115(C), 87-109.

Giannoccaro, I. & Pontrandolfoo, P. (2004). Supply Chain coordination By revenue Sharing Contracts. International Journal Of Production Economics, 89(2), 131-139.

Goldberg, P. K. (1995). Product Differentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry. Econometrica, 63(4), 891-951.

Groba, C., Sartal, A. & Bergantiño, G. (2020). Optimization of tuna fishing logistic routes through information sharing policies: A game theory-based approach. Marine Policy, 113. https://doi.org/10.1016/j.marpol.2019.103795

Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z. & Chen, X. (2020). Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Adv Mater, 32(45), 2004805.

Haghi, E., Shamsi, H., Dimitrov, S., Fowler, M. & Raahemifar, K. (2020). Assessing the potential of fuel cell-powered and battery-powered forklifts for reducing GHG emissions using clean surplus power; a game theory approach. International Journal of Hydrogen Energy, 45(59), 34532-34544.

He, C. & Zhou, H. (2019). A retailer promotion policy model in a manufacturer Stackelberg dual-channel green supply chain. 11 th CIRP Conference on Industrial, Procedia CIRP, 83, 722-727.

He, Y., Huang, H., & Li, D. (2020). Inventory and pricing decisions for a dual-channel supply chain with deteriorating products. Operational Research, 20, 1461-1503

Heydari, J., Govindan, K. & Aslani, A. (2019). Pricing and greening decisions in a three-tier dual-channel. International journal of production economics, 217, 185-196.

Hill, R. & Myatt, A. (2007). Overemphasis on Perfectly Competitive Markets in Microeconomics Principles Textbooks. Journal of Economic Education, 38 (1).

Hiller, T. (2019). Structure of teams—A cooperative game theory approach. Managerial and Decision Economics, 40(5), 520-525.

Ho, J. (2013). A Research Note: An exploration of the intellectual learning process of thinking by managers in the digital social media ecosystem. European Academic Research, 1(5), 636-649.

Horsky, D. & Nelson, P. (1992). New Brand Positioning and Pricing in An Oligopolistic Market. Marketing Science, 11(2), 133-153.

Hosseini, S. & Vakili, R. (2019). Game theory approach for detecting vulnerable data centers in a cloud computing network. International Journal of Communication Systems, 32(8).

Hosseinnia Shavaki, F. & Ebrahimi Ghahnavieh, A. (2023). Application of deap learning in supply chain management : a systematic literature review and a framework for future research. A review of artificial intelligence, 56,4447-4489.

Hua, G., Wang, S. & Cheng, T. E. (2010). Price and lead time decisions in dual-channel supply chains. European Journal of Operational Research, 205(1), 113-126.

Huang, W. & Swaminathan, J. M. (2009). Introduction of a second channel: Implications for pricing and profits. European Journal of Operational Research, 194(1), 258-279.

Jafari, H., Hejazi, S. R. & Rasti-Barzoki, M. (2016). Pricing Decisions in Dual-Channel supply chain including monopolistic manufacture and duoplistic retailers: A game-theoretic approach. Journal of industrial competition trade, 16, 323-343.

Kai, J. (2016). Research on Cooperative Advertising Decisions in Dual-Channel Supply Chain Under Asymmetric Demand Information When Online Channel Implements Discount Promotion. Management Science and Engineering, 10(4), 13-19.

Ke, H., Huang, H. & Gao, X. (2018). Pricing decision problem in the dual-channel supply chain based on experts' belief degrees. Soft Computing, 22, 5683-5698.

Khan, P.W., Byun, Y.C. & Park, N. (2020). IoT-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning. Sensors, 20(10), 2990.

Kilimci, ZH., Akyuz, A.O., Uysal, M., Akyokus, S., Uysal, M.O., Bulbul, B.A., Ekmis, M.A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for the supply chain. Complexity. https://doi.org/10.1155/2019/9067367

Koç, E. & Türkoğlu, M. (2021). Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: COVID-19 pandemic in Turkey. Signal Image Video Process, 16(3), 613-621.  doi: 10.1007/s11760-020-01847-5.

Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D. & Tzovaras, D. (2021). Machine learning and deep learning in smart manufacturing: the smart grid paradigm. Computer Science Review, 40, 100341.

Lee, P. A. & Wen, X.G. (2008). Spin-triplet p-wave pairing in a three-orbital model for iron pnictide superconductors. Physical Review, 78(14), 144517.

Li, G., Li, L., Sethi, S. P., & Guan, X. (2019). Return strategy and pricing in a dual-channel supply chain. International Journal of Production Economics, 215, 153-164.

Liu, L., Parlar, M., Zhu, S. X. (2007). Pricing and lead time decisions in decentralized supply chains. Management Science, 53(5), 713-725.

Liu, Y., Feng, L. & Jin, B. (2020). Future-aware trend alignment for sales predictions.  Information, 11(12), 558.

Liu, Y., Li, J., Ren, W.& Forrest, J.Y-L. (2020). Differentiated product pricing with consumer network acceptance in a dual-channel supply chain. Electronic Commerce, 39, 100915.

Lotfi, E. & Navidi, H. (2012). A decision support system for OPEC oil production level based on game theory and ANN. Advances in Computational Mathematics and its Applications (ACMA), 2(1), 253-258.

Luo, L., Kannan, P. K. & Ratchford, B. T. (2007). New Product Development Under Channel Acceptance. Marketing Science, 26(2), 149–163.

 Ma, J., Zhang, D., Dong, J., & Tu, Y. (2020). A supply chain network economic model with time-based competition. European Journal of Operational Research, 280(3), 889- 908.

Matinfar, F., Azadi Parand, F. & Looney, A. (2020). A review of game theory approaches in the intelligent distribution network with emphasis on collaborative gmaes. Electronics Industries, 10(3), 17-29. (in Persian)

Matsui, K. (2020). Optimal bargaining timing of a wholesale price for a manufacturer with a retailer in a dual-channel supply chain. European Journal of Operational Research, 287, 225-236.

Mocanu, E., Nguyen, P.H., Gibescu, M., Kling, W.L. (2016). Deep learning for estimating building energy consumption. Sustain Energy Grids Netw, 6, 91–99.

Modak, N. M. & Kelle, P. (2019). Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. European Journal of Operational Research, 272(1), 147-161.

Naeimi Siddiq, A., Chaharsooqi, K. & Sheikh Mohammadi, M. (2012). Designing a coordination model in a competitive supply chain using the game theory approach with and without cooperation. Quarterly journal of industrial management, 4(14), 108-118.
(in Persian)

Navidi, H. & Rahmati, A. (2012). Presentation of the competitive model of multilateral sales in supply chains and its analysis using game theory, 10th International Industrial Engineering Conference, Tehran. 1-7. (in Persian)

Nazari, L., Seifbarghy, M. & Setak, M. (2018). Modeling and analyzing pricing and inventory problem in a closed-loop supply chain with return policy and multiple manufacturers and multiple sales channels using game theory. Scientia Iranica, 25(5), 2759-2774.

Nikolopoulos, K., Punia, S., Schafers, A., Tsinopoulos, C. & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99–115.

Piccialli, F., Giampaolo, F., Prezioso, E., Camacho, D. & Acampora, G. (2021). Artificial intelligence and healthcare: forecasting of medical bookings through multi-source time-series fusion. Information Fusion, 74, 1–16.

Punia, S., Singh, S.P. & Madaan, J.K. (2020). A cross-temporal hierarchical framework and deep learning for supply chain forecasting. Computers & Industrial Engineering, 149:106796.

Qin, Y. L. & Xin, X. (2012). Research on the price prediction in the supply chain based on data mining technology. International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 460-463. doi: 10.1109/MSNA.2012.6324621.

Rahmani, D., Qaisari, M. & Hosseinnezhad, J. (2020). Joint decision on product greenness strategies and pricing in a dual-channel supply chain : A robust possibilistic approach. Journal of Cleaner Production, 256, 120437. https://doi.org/10.1016/j.jclepro.2020.120437

Ranjan, A. & Jha, J. (2019). Pricing and coordination strategies of a dual-channel supply chain considering green quality and sales effort. Journal of Cleaner Production, 218, 409-424.

Raza, S.A. & Madhumohan Govindaluri, S. (2019). Pricing strategies in a dual-channel green supply chain with cannibalization and risk aversion. Operations Research Perspectives, 6, 100118.

Rezvani, M. S., Amoozad Mahdiraji, H., Abbasian, E. & Mehregan, M. (2023). Evaluation of Cooperation Strategy in Financial Services Supply Chain Based on Prospect Theory and Game Theory. Iranian Journal of Accounting, Auditing, and Finance, 7(1), 93-108.

Rhim, H. & Cooper, L.G. (2005). Assessing Potential Threats to Incumbent Brands: New Product under Price Competition in A Multisegmented Market. International Journal of Research in Marketing, 22(2), 159-182.

Rimiene, K. (2011). Supply Chain Agility concept evolution (1990-2010). Journal of Economics and Management, 890-905.

Rzeczycki, A. (2022). Supply chain decision making with the use of game theory. Procedia Computer Science, 207, 3988-3997.

Setak, M., Kafshian Ahar, H. & Alaei, S. (2017). Coordination of Information Sharing and Cooperative Advertising in a Decentralized Supply Chain with Competing Retailers Considering Free Riding Behavior. Journal of Industrial and Systems Engineering, 10(2), 151-168.

Shafiee, M. & Farahgol P. (2019). Application of theory in supply chain analysis with customer market approach (case study: Fars cement). Industrial management bstudies,17(53), 185-217. (in Persian)

Shankar, S., Ilavarasan, P.V., Punia, S. & Singh, S.P. (2020). Forecasting container throughput with long short-term memory networks. Industrial Management & Data Systems, 120(3), 425–441.

Shi, S., Sun, J., & Cheng, T. (2020). Wholesale or drop-shipping: Contract choices of the online retailer and the manufacturer in a dual-channel supply chain. International Journal of Production Economics, 107618.

Simon Biaou, B. O., Oluwatope, A. O., Odukoya, H. O., Babalola, A., Ojo, O. E. & Sossou, E. H. (2020). Ayo game approach to mitigate free riding in peer-to-peer networks. Journal of King Saud University - Computer and Information Sciences, 34(6), 2451-2460.

Soleimani, F. (2016). Optimal pricing decisions in a fuzzy dual-channel supply chain. Soft computing, 20(1), 689-696.

Soltani Tehrani, E. & Daei Karimzadeh, S. (2015). Steel price forecast using models. International conference on management and economics in 21century, Tehran.
(in Persian)

Strvulaki, E. & Dvis, M. (2010). Aligning product with supply chain processes and strategy. The International journal of logistic management, 21(1), 127-151.

Taleizadeh, A., Akhavan Niaki, S. & Wee, H. (2013). Joint single vendor single Buyer supply chain problem with stochastic demand and fuzzy lead-time. Knowledge-based systems, 48, 1-9.

Tang, Z. & Ge, Y. (2021). CNN model optimization and intelligent balance model for material demand forecast. International Journal of System Assurance Engineering and Management, 13, 978- 986.

Tirkolaee, E.B., Sadeghi, S., Mooseloo, F.M., Vandchali, H.R. & Aeini, S. (2021). Application of machine learning in supply chain management: a comprehensive overview of the main areas. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/1476043

Toufighi, S. P., Mehregan, M. & Jafarnejad, A. (2020). Optimization of Iran’s Production in Forouzan Common Oil Filed based on Game Theory. Mathematics Interdisciplinary Research, 5, 173-192.

Wang, J., Jian, H. & Yu, M. (2020). Pricing decisions in a dual-channel green supply chain with product customization. Journal of Cleaner Production, 247, 119101.

Weng, T., Liu, W. & Xiao, J. (2019a). Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems,120(2), 265–279.

Wu, B., Wang, L., Wang, S. & Zeng, Y.R. (2021). Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic. Energy, 226:120403.

Wu, J., Zhang, J., Yi, W., Cai, H., Li, Y. & Su, Z. (2021). A game-theoretic analysis of incentive effects for China's agribiomass power generation supply chain. Energies, 14(3), 546.https://doi.org/10.3390/en14030546

Xu, H., Liu, Z.Z. & Zhang, S.H. (2012). A strategic analysis of dual-channel supply chain design with price and delivery lead time considerations. International of Production Economics, 139(2), 654-663.

Xu, Q., Liu, Z. & He, J. (2015). Optimum Retail Pricing Based on Price Comparison in Dual-Channel Supply Chain. (eds) LISS 2013. Springer, Berlin, Heidelberg.

Zhang, C., Liu, Y. & Han, G. (2021). Two-stage pricing strategies of a dual-channel supply chain considering public green preference. Computers & Industrial Engineering, 151, 106988.

Zhang, Y. & Hezarkhani, B. (2021). Competition in dual-channel supply chains: The manufacturers’ channel selection. European Journal of Operational Research, 91(1), 244-262.

Zhou, J., Zhao, R. & Wang, W. (2019). Pricing decision of a manufacturer in a dual-channel supply chain with asymmetric information. European Journal of Operational Research, 278(3), 809-820.

Zougagh, N., Charkaoui, A. & Echchatbi, A. (2020). Prediction models of demand in supply chain. Procedia Computer Science, 177, 462-467.

آمار
تعداد مشاهده مقاله: 786
تعداد دریافت فایل اصل مقاله: 536





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب