Computing near-optimal policies in generalized joint replenishment

Daniel Adelman*, Diego Klabjan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


We provide a practical methodology for solving the generalized joint replenishment (GJR) problem, based on a mathematical programming approach to approximate dynamic programming. We show how to automatically generate a value function approximation basis built upon piecewise-linear ridge functions by developing and exploiting a theoretical connection with the problem of finding optimal cyclic schedules. We provide a variant of the algorithm that is effective in practice, and we exploit the special structure of the GJR problem to provide a coherent, implementable framework.

Original languageEnglish (US)
Pages (from-to)148-164
Number of pages17
JournalINFORMS Journal on Computing
Issue number1
StatePublished - Dec 2012


  • Approximate dynamic programming
  • Generalized joint replenishment
  • Piecewise-linear ridge functions

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'Computing near-optimal policies in generalized joint replenishment'. Together they form a unique fingerprint.

Cite this