Optimizing version release dates of research and development long-term processes

Ran Etgar, Roy Gelbard, Yuval Cohen

Research output: Contribution to journalArticlepeer-review


This paper develops and compares several optimization approaches for the version planning and release problem. This problem is new, challenging for scholars and practitioners, and was not fully addressed in the OR literature. Version releases are part of a wide-spread phenomenon. Mobile phones, operating systems (e.g. MS-Windows) and digital printers are well known examples. However, version release can be found in many other product development fields, such as software products and games, and hardware versions (e.g. TV, screens, communication equipment etc.). In some fields (such as the automotive field) the version release is so well-established that it became an annual routine. An optimization formulation is developed for the total-value of a version-release policy throughout the relevant time-horizon. The novel formulation elements are release-features and release-dates. The value of each release is derived from the combination of features included in the specific released version, and the version release-dates. We developed several search techniques for solving this strongly NP-hard problem. We compared the results of (1) multiple particle swarm optimization (MPSO) (2) Genetic Algorithm (GA), (3) simulated annealing (SA), (4 & 5) two forms of greedy heuristics. A comprehensive computational experiment was performed. The study shows that GA and MPSO outperform the other methods. Moreover, for medium scale problems, GA better suits highly resource-constrained cases, while MPSO performs best for large scale problems disregarding the resource scarcity. This research may be a major reference point for future research on the version release problem.

Original languageEnglish
Pages (from-to)642-653
Number of pages12
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - 1 Jun 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.


  • Genetic algorithm
  • Particle swarm
  • Release
  • Scheduling
  • Simulated annealing


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