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A Data-Driven Pricing Model for Distribution Systems Considering Competition | ||
| Advances in Industrial Engineering | ||
| دوره 58، شماره 1، شهریور 2024، صفحه 179-195 اصل مقاله (457.55 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2024.370998.1885 | ||
| نویسنده | ||
| Matineh Ziari* | ||
| Assistant Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| This paper addresses the need to investigate customer behavior and incorporate competition, given the significant shifts in consumer preferences. To achieve this, mathematical modeling is used to design a distribution system that maximizes profits in a competitive market, comprising a wholesaler and multiple retailers across multiple periods and products. Customer behavior is captured through a behavior-based pricing process at the retail level, with equilibrium values determined using bi-level programming based on Stackelberg modeling, which accounts for asymmetric competition. The model is solved using two distinct approaches: structural modification and data-driven learning models. In the structural modification approach, the bi-level model is linearized and converted into a single-level equivalent. Meanwhile, in the data-driven approach, the pricing process is managed using the CLIQUE clustering method, which helps develop a rule-based pricing system grounded in data extraction. Numerical examples and sensitivity analyses are provided to illustrate the concepts, and the outcomes are compared to highlight managerial implications and avenues for future research. | ||
| کلیدواژهها | ||
| Data-Mining؛ Rule-Based Systems؛ Pricing and Revenue Management؛ Game Theory؛ Retail Systems | ||
| مراجع | ||
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Ali, S. M., M. H. Rahman, T. J. Tumpa, A. A. M. Rifat and S. K. Paul (2018). "Examining price and service competition among retailers in a supply chain under potential demand disruption." Journal of Retailing and Consumer Services 40: 40-47.
Allende, G. B. and G. Still (2013). "Solving bilevel programs with the KKT-approach." Mathematical programming 138: 309-332.
Angeli, C., S. K. Howard, J. Ma, J. Yang and P. A. Kirschner (2017). "Data mining in educational technology classroom research: Can it make a contribution?" Computers & Education 113: 226-242.
Bahari, T. F. and M. S. Elayidom (2015). "An efficient CRM-data mining framework for the prediction of customer behaviour." Procedia computer science 46: 725-731.
Bard, J. F. (1984). "An investigation of the linear three level programming problem." IEEE Transactions on Systems, Man, and Cybernetics(5): 711-717.
Bialas, W. and M. Karwan (1978). "Multilevel linear programming." State University of New York at Buffalo: 78-71.
Bose, I. and X. Chen (2010). "Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach." Electronic Commerce Research and Applications 9(3): 197-208.
Cao, M., Y. Hu and L. Yue (2023). "Research on variable weight CLIQUE clustering algorithm based on partial order set 1." Journal of Intelligent & Fuzzy Systems(Preprint): 1-13.
Chen, Y. and M. Florian (1992). "On the geometric structure of linear bilevel programs: a dual approach." Centre De Recherche Sur Les Transports Publication(867).
Choe, C., S. King and N. Matsushima (2018). "Pricing with cookies: Behavior-based price discrimination and spatial competition." Management Science 64(12): 5669-5687.
Chrobak, M., C. Dürr, A. Fabijan and B. J. Nilsson (2020). "Online clique clustering." Algorithmica 82(4): 938-965.
Cong, Z., X. Luo, J. Pei, F. Zhu and Y. Zhang (2022). "Data pricing in machine learning pipelines." Knowledge and Information Systems 64(6): 1417-1455.
Costa, E. B., B. Fonseca, M. A. Santana, F. F. de Araújo and J. Rego (2017). "Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses." Computers in human behavior 73: 247-256.
Cui, W. and L. Li (2018). "A game-theoretic approach to optimize the Time-of-Use pricing considering customer behaviors." International Journal of Production Economics 201: 75-88.
De Nijs, R. (2017). "Behavior-based price discrimination and customer information sharing." International Journal of Industrial Organization 50: 319-334.
De Nijs, R. and A. Rhodes (2013). "Behavior-based pricing with experience goods." Economics Letters 118(1): 155-158.
Ernawati, E., S. Baharin and F. Kasmin (2021). A review of data mining methods in RFM-based customer segmentation. Journal of Physics: Conference Series, IOP Publishing.
Esteves, R.-B. and S. Cerqueira (2017). "Behavior-based pricing under imperfectly informed consumers." Information Economics and Policy 40: 60-70.
Gao, Z., J. Wu and H. Sun (2005). "Solution algorithm for the bi-level discrete network design problem." Transportation Research Part B: Methodological 39(6): 479-495.
Ghomi-Avili, M., S. T. A. Niaki and R. Tavakkoli-Moghaddam (2023). "A blockchain-based system for a network design problem considering pricing decisions and sustainability." Journal of Cleaner Production 423: 138696.
Ghomi-Avili, M., R. Tavakkoli-Moghaddam, S. G. Jalali Naeini and A. Jabbarzadeh (2021). "Competitive green supply chain network design model considering inventory decisions under uncertainty: a real case of a filter company." International Journal of Production Research 59(14): 4248-4267.
Gramm, J., J. Guo, F. Hüffner and R. Niedermeier (2005). "Graph-modeled data clustering: Exact algorithms for clique generation." Theory of Computing Systems 38: 373-392.
Heidari, S., R. Radfar, M. Alborzi, M. A. Afshar Kazemi and A. Rajabzadeh Ghatari (2022). "Clustering algorithm for electronic services customers: A case study of the banking industry." International Journal of Nonlinear Analysis and Applications 13(2): 173-184.
Ho, T., S. Nguyen, H. Nguyen, N. Nguyen, D.-S. Man and T.-G. Le (2023). "An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry." Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy 14(1): 26-53.
Jenabi, G. and S. A. Mirroshandel (2014). "Using data mining techniques for improving customer relationship management." European Online Journal of Natural and Social Sciences: Proceedings 2(3 (s)): pp. 3143-3149.
Kong, X., D. Kong, J. Yao, L. Bai and J. Xiao (2020). "Online pricing of demand response based on long short-term memory and reinforcement learning." Applied Energy 271: 114945.
Lu, R., S. H. Hong and X. Zhang (2018). "A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach." Applied Energy 220: 220-230.
Maestre, R., J. Duque, A. Rubio and J. Arévalo (2018). Reinforcement learning for fair dynamic pricing. Proceedings of SAI Intelligent Systems Conference, Springer.
Maheshwari, R., S. K. Mohanty and A. C. Mishra (2023). "DCSNE: Density-based Clustering using Graph Shared Neighbors and Entropy." Pattern Recognition 137: 109341.
McCormick, G. P. (1976). "Computability of global solutions to factorable nonconvex programs: Part I—Convex underestimating problems." Mathematical programming 10(1): 147-175.
Meng, Q., M. Li, W. Liu, Z. Li and J. Zhang (2021). "Pricing policies of dual-channel green supply chain: Considering government subsidies and consumers' dual preferences." Sustainable Production and Consumption 26: 1021-1030.
Pei, J. (2020). Data Pricing--From Economics to Data Science. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Pei, J. (2020). "A survey on data pricing: from economics to data science." IEEE Transactions on knowledge and Data Engineering 34(10): 4586-4608.
Pishvaee, M. S., J. Razmi and S. A. Torabi (2012). "Robust possibilistic programming for socially responsible supply chain network design: A new approach." Fuzzy sets and systems 206: 1-20.
Rana, R. and F. S. Oliveira (2015). "Dynamic pricing policies for interdependent perishable products or services using reinforcement learning." Expert systems with applications 42(1): 426-436.
Sabbaghnia, A., J. Heydari and J. Razmi (2023). "Participative pricing and donation programs in a socially concerned supply chain." Corporate Social Responsibility and Environmental Management 30(1): 146-164.
Sheikh Sajadieh, M. and M. Ziari (2021). "A behavior-based pricing model in retail systems using game theory approach." Journal of Industrial and Systems Engineering 13(4): 142-155.
Shen, Q. and J. Miguel Villas-Boas (2018). "Behavior-based advertising." Management Science 64(5): 2047-2064.
Sotomayor, G., H. Hampel and R. F. Vázquez (2018). "Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm." Water research 130: 353-362.
Su, X. (2007). "Intertemporal pricing with strategic customer behavior." Management Science 53(5): 726-741.
Vamvakas, P., E. E. Tsiropoulou and S. Papavassiliou (2018). "Dynamic provider selection & power resource management in competitive wireless communication markets." Mobile Networks and Applications 23(1): 86-99.
Vengerov, D. (2008). "A gradient-based reinforcement learning approach to dynamic pricing in partially-observable environments." Future Generation Computer Systems 24(7): 687-693.
Videla Cavieres, I. F. (2014). "Characterization and completation of the customer data from a retail company using graph mining techniques."
Videla Cavieres, I. F. (2015). "Improvement of recommendation system for a wholesale store chain using advanced data mining techniques."
Wang, R., W. Ji, M. Liu, X. Wang, J. Weng, S. Deng, S. Gao and C.-a. Yuan (2018). "Review on mining data from multiple data sources." Pattern Recognition Letters 109: 120-128.
Xu, J., N. Hong, Z. Xu, Z. Zhao, C. Wu, K. Kuang, J. Wang, M. Zhu, J. Zhou and K. Ren (2023). "Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing." Engineering.
Yaghini, M. and M. Vard (2012). "Automatic Clustering of Mixed Data Using Genetic Algorithm." International Journal of Industrial Engineering 23(2): 187-197.
Yang, S. (2019). "Price-responsive early charging control based on data mining for electric vehicle online scheduling." Electric Power Systems Research 167: 113-121.
Yu, Y., J. Lu, D. Shen and B. Chen (2021). "Research on real estate pricing methods based on data mining and machine learning." Neural Computing and Applications 33: 3925-3937.
Zhang, G., G. Li and J. Shang (2023). "Optimizing mixed bundle pricing strategy: Advance selling and consumer regret." Omega 115: 102782.
Ziari, M., M. Ghomi‐Avili, M. S. Pishvaee and H. Jahani (2022). "A review on competitive pricing in supply chain management problems: models, classification, and applications." International Transactions in Operational Research 29(4): 2082-2115.
Ziari, M. and M. S Sajadieh (2023). "BEHAVIOR-BASED PRICING CONSIDERING COMPETITION IN RETAIL SYSTEMS." Sharif Journal of Industrial Engineering & Management 38(2): 59-66.
Ziari, M. and M. S. Sajadieh (2021). "A behavior-based pricing model in retail systems considering vertical and horizontal competition." Computers & Industrial Engineering 152: 107054. | ||
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