Shortening the project schedule: solving multimode chance-constrained critical chain buffer management using reinforcement learning

Claudio Szwarcfiter, Yale T. Herer, Avraham Shtub

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers.

Original languageEnglish
Pages (from-to)565-592
Number of pages28
JournalAnnals of Operations Research
Volume337
Issue number2
DOIs
StatePublished - 1 Jun 2024
Externally publishedYes

Keywords

  • Chance constraints
  • Critical chain buffer management
  • Multimode project management
  • Project scheduling
  • Reinforcement learning

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research

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