Graphical Schedule Risk Analyzer Using Monte Carlo Simulation
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Solution Overview
Problem
Current scheduling methods, such as CPM, struggle to accurately predict project completion dates due to variability in activity durations and inability to model floating and pacing, which are common in real-life project scenarios.
Innovation Solution
A computer-implemented simulation method for analyzing schedule risk in graphical schedules, which includes providing a base schedule with graphical objects connected by logic ties, sampling activity durations from probability distributions, modifying start dates based on float or drift, and using logic ties to determine simulated occurrences, thereby modeling realistic scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CPM methods are used for schedule prediction, then the scheduling process is simple and deterministic, but the prediction accuracy deteriorates due to inability to model activity duration variability and floating/pacing behaviors
Solution Approach 1:
The patent applies dynamics by transitioning from static deterministic CPM to dynamic Monte Carlo simulation. The system models activity durations as probability distributions that vary across simulation iterations, and dynamically adjusts activity start dates based on float/drift calculations in each iteration. This allows the schedule to adapt to varying scenarios while capturing real-life floating and pacing behaviors that deterministic methods miss.
Solution Approach 2:
The patent changes the parameter representation from fixed deterministic values to probability distributions. Activity durations are defined by minimum, most likely, and maximum values forming triangular distributions. The system samples from these distributions in each iteration and modifies start dates based on float/drift parameters, fundamentally changing how schedule parameters are handled to improve prediction accuracy.
2Measurement precision
If probability distributions and Monte Carlo simulation are used to model activity durations, then prediction accuracy improves, but computational complexity and analysis time increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating float and drift values for each activity in the base schedule before the Monte Carlo simulation begins. These pre-computed values are then used directly during simulation iterations to adjust start dates, avoiding redundant calculations in each iteration and significantly reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent segments the simulation process into distinct phases: base schedule preparation with float/drift calculation, probability distribution definition, iterative sampling and simulation, and results aggregation. This segmentation allows efficient computation by preparing reusable components beforehand and processing simulation iterations in a structured manner, reducing overall computation time.
3Adaptability or versatility
If float and drift modifications are applied to activity start dates, then realistic project scenarios including floating and pacing are modeled, but the logic network calculation complexity increases
Solution Approach 1:
The patent applies copying by creating a copy of the base schedule for each Monte Carlo iteration rather than modifying the original. The copied schedule inherits the logic network structure and pre-calculated float/drift values, allowing independent modification of start dates in each iteration without affecting the base schedule or requiring complex recalculation of the entire logic network.
Solution Approach 2:
The patent performs preliminary calculation of float and drift values for all activities in the base schedule before simulation. These pre-computed values are stored and reused in each iteration to determine start date modifications, avoiding repeated complex logic network calculations and reducing processing complexity while maintaining accurate modeling of floating and pacing.
Data Source
AI summary
A computer-implemented simulation method for analyzing schedule risk in a graphical schedule that includes (a) providing a base schedule that has graphical schedule objects including activities that are connected in a logic network by logic ties, the schedule culminating in a completion milestone, (b) sampling from probability distributions representing uncertainties in the durations of the activities to simulate activity durations from start dates to finish dates, (c) modifying the start date of one of the activities as a function of float or drift of the activity, and (d) using the logic ties, the sampled activity durations, and the modified start date to determine an output representing a simulated occurrence of the base schedule culminating in the completion milestone.