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. A project network is first learned from historical records. 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, from which one has to be selected, is enriched by identifying decision rules and frequent paths. The planner can rely on the project network 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 towards automated data-driven project planning.

翻译：本文聚焦于项目（即业务流程），这类流程正成为当今时代的经济驱动力。与无需详细规划的日常运营流程不同，项目需要规划及资源受限调度，以协调子项目、关联项目及组织间的资源。负责项目规划的规划者必须选择待执行的活动集合，确定其优先约束关系，并根据项目时间约束进行调度。针对基础设施建设与信息系统开发等类别的项目，我们提出一种数据驱动的项目规划方法。首先，从历史记录中学习项目网络结构。该网络解构了单一项目中隐含的时间约束，从而揭示可被利用以获取更大效益的规划与调度柔性空间。随后，通过识别决策规则与频繁路径，对包含多种项目计划变体（需从中择一）的网络进行增强。规划者可依托该网络实现：1）解码项目变体以形成新的项目计划，2）应用资源受限项目调度程序确定项目进度与资源分配。基于两个真实项目数据集，本方法可为规划者提供显著灵活性（真实项目关键路径缩短幅度最高达26%），以调整项目计划与进度。我们相信，该研究将在支持自动化数据驱动项目规划的决策过程中发挥重要作用。