Existing resource leveling (RL) approaches fall short of analyzing the trade-off relation between the total float consumption and project completion probability in a real-life project RL problem. This article presents a stochastic resource leveling optimization (SOLO) method that minimizes the total float consumption along with maximizing the project completion probability. It initializes the earliest start times of noncritical activities, measures the level of resource fluctuations of each candidate solution, computes the probability of completing a project in a target deadline by executing simulation-based scheduling, and identifies optimal solution(s) (i.e., optimal start times of noncritical activities) by implementing genetic algorithm, thereby identifying an optimal resource-leveled baseline. The study is of value to practitioners because SOLO considers both the amount of total float consumption and project completion probability. This study facilitates experimentation with different computation time-saving options given various constraints (i.e., the residual of project completion probabilities, threshold of release and rehire, ratio of criticality index, and number of critical activities). Test cases verify the validity of the computational method.

中文翻译：

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权衡浮动消耗和项目完成概率的随机资源均衡优化方法

现有的资源平衡 (RL) 方法无法分析实际项目 RL 问题中总浮游量消耗与项目完成概率之间的权衡关系。本文提出了一种随机资源平衡优化 (SOLO) 方法，该方法可以最大限度地减少总浮游量消耗，同时最大限度地提高项目完成概率。它初始化非关键活动的最早开始时间，测量每个候选解决方案的资源波动水平，通过执行基于模拟的调度计算在目标期限内完成项目的概率，并确定最佳解决方案（即最优非关键活动的开始时间）通过实施遗传算法，从而确定最佳资源水平基线。该研究对从业者很有价值，因为 SOLO 考虑了总浮动消耗量和项目完成概率。考虑到各种限制（即项目完成概率的残差、发布和重新雇用的阈值、关键性指数的比率以及关键活动的数量），这项研究有助于使用不同的计算时间节省选项进行实验。测试用例验证了计算方法的有效性。