Data-Driven Project Planning: An Integrated Network Learning, Process Mining, and Constraint Relaxation Approach in Favor of Scheduling Recurring Projects

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Our focus is on projects, i.e., business processes, which are emerging as the economic drivers of our times. Differently from day-to-day operational processes that do not require detailed planning, a project requires planning and resource-constrained scheduling for coordinating resources across sub- or related projects and organizations. A planner in charge of project planning has to select a set of activities to perform, determine their precedence constraints, and schedule them according to temporal project constraints. We suggest a data-driven project planning approach for classes of projects such as infrastructure building and information systems development projects. In such projects, a significant portion of activities recurs within other organizational projects, which may be similar, while each project is unique in its realization. The first steps of the suggested approach include learning a project network from historical records of similar projects. The discovered network relaxes temporal constraints embedded in individual projects, thus, uncovering where planning and scheduling flexibility can be exploited for greater benefit. Then, the network, which contains multiple project plan variations, is enriched by identifying decision rules and frequent paths in favor of selecting a specific variation as the chosen project plan. The planner can rely on the suggested approach for: 1) Decoding a project variation such that it forms a new project plan, and 2) applying resource-constrained project scheduling procedures to determine the project’s schedule and resource allocation. Using two real-world project datasets, we show that the suggested approach may provide the planner with significant flexibility (up to a 26% reduction of the critical path of a real project) to adjust the project plan and schedule. We believe that the proposed approach can play an important part in supporting decision making toward automated data-driven project planning.

Original languageEnglish
Pages (from-to)7719-7729
Number of pages11
JournalIEEE Transactions on Engineering Management
StatePublished - 2024

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© 2024 IEEE.


  • Constraint relaxation
  • data-driven planning
  • machine learning
  • process mining
  • project planning


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