Simple heuristics for the joint inventory and pricing models with fixed replenishment costs

M. Edib Gurkan, Huseyin Tunc and S. Armagan Tarim

Journal of the Operational Research Society, 2025, vol. 76, issue 3, 567-580

Abstract: This study considers the joint inventory and pricing problem of a firm selling a single item over a multi-period planning horizon. A fixed replenishment cost is incurred whenever the replenishment decision is made. Period demands are considered to be non-stationary, stochastic, and price-dependent. The literature shows that the optimal policy in such a system is of an (s, S, p)-type. The implementation of this type of policy might be challenging in practice due to the prevailing computational difficulty in determining the optimal policy parameters. This study, therefore, aims to develop computationally efficient heuristic approaches for the joint inventory and pricing problem. The proposed heuristics provide different levels of flexibility in making inventory and pricing decisions. Furthermore, they are essentially built on the concept of the replenishment cycle and do not require solving a stochastic dynamic program. Our numerical study demonstrates the profit efficiency of the proposed heuristic approaches against the optimal policy. The results also indicate that firms lacking the technical ability to employ a dynamic pricing strategy might still gain significant profit improvement by only using a dynamic inventory control policy.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2376061 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:76:y:2025:i:3:p:567-580

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2376061

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().