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Joint pricing and inventory control with reference price effects and price thresholds: A deep reinforcement learning approach
16 references·2 citing·43 second hop
Joint pricing and inventory control with reference price effects and price thresholds: A deep reinforcement learning approach (2023)Joint pricing and inventory control with …Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems (2022)Can Deep Reinforcement Learni…Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management (2021)Reward shaping to improve the…Deep reinforcement learning for inventory control: A roadmap (2021)Deep reinforcement learning f…A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization (2021)A Deep Q-Network for the Beer…Use of Proximal Policy Optimization for the Joint Replenishment Problem (2020)Use of Proximal Policy Optimi…Dynamic pricing in the presence of reference price effect and consumer strategic behaviour (2019)Dynamic pricing in the presen…Pricing and lot-sizing decisions for perishable goods when demand depends on selling price, reference price, product freshness, and displayed stocks (2018)Pricing and lot-sizing decisi…Dynamic Stochastic Inventory Management with Reference Price Effects (2016)Dynamic Stochastic Inventory …Dynamic Pricing Strategies with Reference Effects (2007)Dynamic Pricing Strategies wi…Reference Price Research: Review and Propositions (2005)Reference Price Research: Rev…Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions (2003)Dynamic Pricing in the Presen…Consumer price sensitivity and price thresholds14 14Sangman Han is Associate Professor, School of Business Administration Sungkyunkwan University, 53 3-ga, Myongnyun-dong, Chongno-gu, Seoul (phone: 82-2-760-0456, email: smhan@yurim.skku.ac.kr). Sunil Gupta is Meyer Feldberg Professor of Business (phone: 212-854-3467, email: sg37@columbia.edu) and Donald R. Lehmann is George E. Warren Professor (phone: 212-854-3465, email: drl2@columbia.edu), both at the Graduate School of Business, Columbia University, New York, NY 10027. All correspondence should be directed to Sunil Gupta. (2001)Consumer price sensitivity an…EDLP, Hi-Lo, and Margin Arithmetic (1994)EDLP, Hi-Lo, and Margin Arith…Finding Optimal (s, S) Policies Is About As Simple As Evaluating a Single Policy (1991)Finding Optimal (s, S) Polici…Mental Accounting and Consumer Choice (1985)Mental Accounting and Consume…Social Judgment: Assimilation and Contrast Effects in Communication and Attitude Change (1981)Social Judgment: Assimilation…A systematic review of machine learning approaches in inventory control optimization (2025)A systematic review of machin…Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review (2024)Benefits, challenges, and lim…Prospect Theory: An Analysis of Decision under Risk (1979)Prospect theory: An analysis of decision under risk (1988)Segmenting cruise passengers with price sensitivity (2004)Consumer food choices: the role of price and pricing strategies (2011)Green product pricing with non-green product reference (2018)A Customer Relationship Management Roadmap: What is Known, Potential Pitfalls, and Where to Go (2005)Implementing the Marketing Concept at the Employee-Customer Interface: The Role of Customer Need Knowledge (2009)Literature review as a research methodology: An overview and guidelines (2019)Advances in Consumer Research (2025)Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons (1994)Revenue Management: Research Overview and Prospects (1999)Foundations of Inventory Management (2000)An Overview of Pricing Models for Revenue Management (2003)Perspectives in supply chain risk management (2006)Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process (2009)Principles of Operations Research. (1970)Chapter 3 Centralized planning models for multi-echelon inventory systems under uncertainty (1993)Lower Bounds for Multi-Echelon Stochastic Inventory Systems (1994)A review of the joint replenishment problem literature: 1989–2005 (2007)The Economics of Welfare (1933)Meta-Analytic Procedures for Social Research (1991)Towards value-based pricing—An integrative framework for decision making (2003)Do Promotions Benefit Manufacturers, Retailers, or Both? (2004)Effects of pricing and promotion on consumer perceptions: it depends on how you frame it (2005)Optimal Policies for a Multi-Echelon Inventory Problem (1960)Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment (1989)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)The role of artificial intelligence in supply chain management: mapping the territory (2022)Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024)Studies in Mathematical Theory of Inventory and Production (1959)A Robust Optimization Approach to Inventory Theory (2006)Practical Bayesian Optimization of Machine Learning Algorithms (2012)Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature (2023)Supply Chain Resilience: A Critical Review of Risk Mitigation, Robust Optimisation, and Technological Solutions and Future Research Directions (2025)The attribution of attitudes (1967)Attitudes, Traits, and Actions: Dispositional Prediction of Behavior in Personality and Social Psychology (1987)SURVEY RESEARCH (1999)Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities (2007)Supply chain coordination through cooperative advertising with reference price effect (2012)Dynamic pricing and learning: Historical origins, current research, and new directions (2015)Are Investors Reluctant to Realize Their Losses? (1998)A perspective on judgment and choice: Mapping bounded rationality. (2003)Instructional manipulation checks: Detecting satisficing to increase statistical power (2009)
this work references cited by second hop

References

  • 1985Mental Accounting and Consumer Choice4,875 cited
  • 1981Social Judgment: Assimilation and Contrast Effects in Communication and Attitude Change1,842 cited
  • 2003Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions1,293 cited
  • 2005Reference Price Research: Review and Propositions845 cited
  • 2007Dynamic Pricing Strategies with Reference Effects450 cited
  • 1994EDLP, Hi-Lo, and Margin Arithmetic369 cited
  • 1991Finding Optimal (s, S) Policies Is About As Simple As Evaluating a Single Policy316 cited
  • 2001Consumer price sensitivity and price thresholds14 14Sangman Han is Associate Professor, School of Business Administration Sungkyunkwan University, 53 3-ga, Myongnyun-dong, Chongno-gu, Seoul (phone: 82-2-760-0456, email: smhan@yurim.skku.ac.kr). Sunil Gupta is Meyer Feldberg Professor of Business (phone: 212-854-3467, email: sg37@columbia.edu) and Donald R. Lehmann is George E. Warren Professor (phone: 212-854-3465, email: drl2@columbia.edu), both at the Graduate School of Business, Columbia University, New York, NY 10027. All correspondence should be directed to Sunil Gupta.295 cited
  • 2021A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization242 cited
  • 2021Deep reinforcement learning for inventory control: A roadmap238 cited
  • 2022Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems206 cited
  • 2018Pricing and lot-sizing decisions for perishable goods when demand depends on selling price, reference price, product freshness, and displayed stocks

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158 cited
  • 2019Dynamic pricing in the presence of reference price effect and consumer strategic behaviour125 cited
  • 2016Dynamic Stochastic Inventory Management with Reference Price Effects117 cited
  • 2021Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management116 cited
  • 2020Use of Proximal Policy Optimization for the Joint Replenishment Problem111 cited
  • Cited by

    • 2025A systematic review of machine learning approaches in inventory control optimization15 cited
    • 2024Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review12 cited