Managing announced task completion times is a fundamental control problem in project management. While extensive research exists on estimating task durations and task scheduling, the problem of when and how to update completion times communicated to stakeholders remains understudied. Organizations must balance announcement accuracy against the costs of frequent timeline updates, which can erode stakeholder trust and trigger costly replanning. Despite the prevalence of this problem, current approaches rely on static predictions or ad-hoc policies that fail to account for the sequential nature of announcement management. In this paper, we formulate the task announcement problem as a Partially Observable Markov Decision Process (POMDP) where the control policy must decide when to update announced completion times based on noisy observations of true task completion. Since most state variables (current time and previous announcements) are fully observable, we leverage the Mixed Observability MDP (MOMDP) framework to enable more efficient policy optimization. Our reward structure captures the dual costs of announcement errors and update frequency, enabling synthesis of optimal announcement control policies. Using off-the-shelf solvers, we generate policies that act as feedback controllers, adaptively managing announcements based on belief state evolution. Simulation results demonstrate significant improvements in both accuracy and announcement stability compared to baseline strategies, achieving up to 75\% reduction in unnecessary updates while maintaining or improving prediction accuracy.

翻译：在项目管理中，管理已公布的任务完成时间是一个基础性的控制问题。尽管在任务工期估算和任务调度方面已有大量研究，但何时以及如何向利益相关者更新已传达的完成时间这一问题仍未得到充分探讨。组织必须在公告准确性与频繁更新时间线所带来的成本之间取得平衡，后者可能损害利益相关者的信任并引发代价高昂的重新规划。尽管该问题普遍存在，但现有方法依赖于静态预测或临时性策略，未能考虑公告管理的序列特性。本文将任务公告问题建模为一个部分可观测马尔可夫决策过程（POMDP），其中控制策略必须基于对真实任务完成情况的噪声观测，决定何时更新已公布的完成时间。由于大多数状态变量（当前时间和先前公告）是完全可观测的，我们利用混合可观测性MDP（MOMDP）框架以实现更高效的政策优化。我们的奖励结构同时捕捉了公告错误和更新频率的双重成本，从而能够合成最优的公告控制策略。使用现成的求解器，我们生成了作为反馈控制器的策略，能够基于信念状态的演化自适应地管理公告。仿真结果表明，与基线策略相比，该方法在准确性和公告稳定性方面均有显著提升，在保持或提高预测精度的同时，实现了高达75%的不必要更新减少。