Robust Pricing and Production with Information Partitioning and Adaptation

Georgia Perakis · Melvyn Sim · Qinshen Tang · Peng Xiong

54 citations0/0 files storedManagement Science

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Supply Chain and Inventory Management Product Development and Customization Risk and Portfolio Optimization

Abstract

We introduce a new distributionally robust optimization model to address a two-period, multiitem joint pricing and production problem, which can be implemented in a data-driven setting using historical demand and side information pertinent to the prediction of demands. Starting from an additive demand model, we introduce a new partitioned-moment-based ambiguity set to characterize its residuals, which also determines how the second-period demand would evolve from the first-period information in a data-driven setting. We investigate the joint pricing and production problem by proposing a cluster-adapted markdown policy and an affine recourse adaptation, which allow us to reformulate the problem as a mixed-integer linear optimization problem that we can solve to optimality using commercial solvers. We also extend our framework to ensemble methods using a set of ambiguity sets constructed from different clustering approaches. Both the numerical experiments and case study demonstrate the benefits of the cluster-adapted markdown policy and the partitioned moment-based ambiguity set in improving the mean profit over the empirical model—when applied to most out-of-sample tests. This paper was accepted by J. George Shanthikumar, data science. Funding: The research of Q. Tang was supported by Nanyang Technological University [Start-Up Grant 020022-00001] and partly financed by a NUS Business School FY2018 Ph.D. Exchange Fellowship. The research of M. Sim and P. Xiong was supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [GrantMOE-2019-T3-1-010]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4446 .

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 54 citations exist upstream.

References

2004The Price of Robustness 2010Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems 2015Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations 1999Pricing and the Newsvendor Problem: A Review with Extensions 1994Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons 2004Adjustable robust solutions of uncertain linear programs 2003Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions 2016Marketing Analytics for Data-Rich Environments

Cited by

2023Distributionally robust facility location with uncertain facility capacity and customer demand 2023Joint pricing and inventory management under minimax regret

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.