Scalable relaxation techniques to solve stochastic dynamic multi-product pricing problems with substitution effects

  • In many businesses, firms are selling different types of products, which share mutual substitution effects in demand. To compute effective pricing strategies is challenging as the sales probabilities of each of a firm's products can also be affected by the prices of potential substitutes. In this paper, we analyze stochastic dynamic multi-product pricing models for the sale of perishable goods. To circumvent the limitations of time-consuming optimal solutions for highly complex models, we propose different relaxation techniques, which allow to reduce the size of critical model components, such as the state space, the action space, or the set of potential sales events. Our heuristics are able to decrease the size of those components by forming corresponding clusters and using subsets of representative elements. Using numerical examples, we verify that our heuristics make it possible to dramatically reduce the computation time while still obtaining close-to-optimal expected profits. Further, we show that our heuristics are (i) flexible,In many businesses, firms are selling different types of products, which share mutual substitution effects in demand. To compute effective pricing strategies is challenging as the sales probabilities of each of a firm's products can also be affected by the prices of potential substitutes. In this paper, we analyze stochastic dynamic multi-product pricing models for the sale of perishable goods. To circumvent the limitations of time-consuming optimal solutions for highly complex models, we propose different relaxation techniques, which allow to reduce the size of critical model components, such as the state space, the action space, or the set of potential sales events. Our heuristics are able to decrease the size of those components by forming corresponding clusters and using subsets of representative elements. Using numerical examples, we verify that our heuristics make it possible to dramatically reduce the computation time while still obtaining close-to-optimal expected profits. Further, we show that our heuristics are (i) flexible, (ii) scalable, and (iii) can be arbitrarily combined in a mutually supportive way.show moreshow less

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Metadaten
Author details:Rainer SchlosserORCiDGND
DOI:https://doi.org/10.1057/s41272-020-00249-z
ISSN:1476-6930
ISSN:1477-657X
Title of parent work (English):Journal of revenue and pricing management
Publisher:Palgrave Macmillan
Place of publishing:Basingstoke
Publication type:Article
Language:English
Date of first publication:2020/06/10
Publication year:2020
Release date:2022/11/23
Tag:data-driven demand; dynamic; heuristics; multi-product pricing; programming; substitution effects
Volume:20
Issue:1
Number of pages:12
First page:54
Last Page:65
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Peer review:Referiert
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