Beneficial Modeling Techniques Not Usually Available in Project Management Software

Conventional project management scheduling software tools often fall short of capturing the complexities found in real-world projects, particularly in high-stakes industries like aerospace, defense, and manufacturing. An IEEE Aerospace 2025 paper by Robert Richards, a research scientist at Stottler Henke, identifies several advanced modeling capabilities that bridge the gap between idealized project plans and real-world execution. These capabilities, many implemented in the Aurora intelligent scheduling software used by NASA, allow project managers to create more realistic models that adapt to changing conditions and produce more accurate schedules.

Enhanced Temporal Dependency Constraints

Standard project management tools typically offer basic temporal constraints like finish-to-start relationships with simple lead and lag options. The paper introduces more sophisticated constraint options, found in project management scheduling software tools like Aurora:

• Absolute Constraints: Unlike standard constraints that establish minimum time relationships (e.g., finish ≤ start), absolute constraints (e.g., finish = start) enforce exact timing relationships. This is valuable for modeling processes that must start immediately after another finishes, such as paint drying immediately after application.
• Maximum Offset Constraints: These establish countdown or expiration periods after one task finishes, requiring another task to start within a specified timeframe. For instance, a maximum offset constraint might require Task B to start within 25 hours of Task A’s completion, modeling time-sensitive dependencies.
• Constraint Documentation: Enhanced constraint definitions include justification fields and notes that explain the reasoning behind constraints, providing valuable context for team members and future decision-making.
• Constraint Review Tracking: The system tracks constraint review history and allows marking constraints as reviewed, improving quality control in complex projects.

Preference Constraints

Perhaps the most significant advancement discussed is the concept of preference constraints, which introduce flexibility into project models that traditional tools lack:

• Resource Preferences: Activities may prefer specific resources (e.g., a particular shop or machine) but can use alternatives when necessary. This allows schedulers to maximize resource utilization while respecting preferences when possible.
• Process Flow Preferences: In manufacturing environments, components may need to flow through multiple processing steps with preferences about delay times between steps. The system can balance overall throughput with keeping components moving through the process at a preferred pace.
• Dynamic Priority Adjustments: When assembly work begins before all components are manufactured, the system can automatically increase the priority of remaining components and adjust their scheduling constraints.

These preference constraints allow project models to adapt to real-world conditions more fluidly, rather than treating all constraints as absolute requirements. This significantly reduces the brittleness of project plans during execution.

Improved Lead and Lag Modeling

Traditional lead and lag definitions are often tied to fixed time periods based on original task durations. When task durations change during execution, these leads and lags don’t automatically adjust, causing scheduling inaccuracies.

The paper proposes a more flexible approach by:

• Using Task Subdivision: Instead of defining a percentage-based lag (e.g., start successor after 20% completion of predecessor), divide the task into subtasks representing meaningful progress points.
• Employing Absolute Finish-to-Start Constraints: Connect these subtasks with absolute finish-to-start constraints, ensuring the relationship remains valid even if task durations change.

This method provides greater clarity and adaptability when task durations change, as the progress milestones move appropriately with the task’s evolving timeline.

Modeling Physical Space and Resource Capabilities

Space constraints and detailed resource capabilities are often overlooked in traditional project management tools:

• Space Creation and Elimination: Projects often create or eliminate usable space as they progress. For example, installing panels may block access to a space zone, making it unavailable for subsequent tasks.
• Capacity Change Constraints: These allow modeling how tasks affect resource availability, such as adding a space zone that can subsequently be used for work.
• Detailed Resource Attributes: Resources can be modeled with specializations and certifications beyond simple occupation classifications, allowing more precise assignment.

Hazard and Exclusivity Constraints

For safety-critical industries, modeling incompatible or hazardous activities is essential:

• Hazard Constraints: These ensure potentially dangerous activities never coincide with incompatible tasks during execution.
• Exclusivities/Non-concurrent Constraints: These specify that certain tasks cannot happen simultaneously, preventing conflicts or safety issues.
• Concurrent Constraints: Conversely, these specify tasks that must happen simultaneously, ensuring coordinated execution.

Variable Task Attributes and Alternative Resources

Real-world task execution often varies based on resource assignment and conditions:

• Variable Duration Tasks: Tasks may be completed more quickly with more resources or more experienced personnel. The system can model these relationships and optimize assignments.
• Alternative Resource Combinations: Different combinations of resources might complete the same task, potentially with different durations or costs.
• Ergonomic Constraints: Human physical limitations can be modeled, considering factors like how long workers can perform physically demanding tasks.

Monte Carlo Simulation with Advanced Constraints

Risk analysis through Monte Carlo simulation is common in project management, but most tools can’t incorporate the advanced constraints discussed:

• Comprehensive Constraint Support: Aurora’s Monte Carlo simulation respects all the advanced modeling capabilities, providing more accurate risk analysis.
• Resource-Limited Simulation: Both infinite resource and limited resource Monte Carlo analyses are supported, solving the complex resource-loaded scheduling problem while accounting for advanced constraints.
• Linear Scaling: Solution time grows linearly with project size, making analysis of large projects feasible.

This capability allows project managers to build more realistic models that adapt correctly to real-world changes and then simulate these changes accurately, even for complex resource-loaded schedules.

Enhanced Visualization Capabilities

As project modeling complexity increases, visualization tools must evolve to help comprehend the models:

• Constraint Display Options: Various constraint types can be visually distinguished and selectively shown or hidden.
• Multi-scale Navigation: Large projects can be navigated through overview mini-maps that show the entire model while allowing detailed inspection of specific areas.
• Single-Element Views: These focus on all relationships connected to a specific task, making it easier to understand task dependencies and resource relationships.
• Upstream/Downstream Analysis: These visualizations show tasks dependent on a selected task or tasks the selected task depends on, helping understand critical paths and dependencies.
• Point-to-Point Analysis: This finds and displays the path through the network connecting two milestones or tasks, aiding analysis of specific segments of complex projects.

Shift-Specific Features

Several capabilities address specific needs in manufacturing and shift-based work:

• Shift Control Properties: These control how tasks interact with shift breaks, specifying whether tasks can span shifts or days and whether they require completion a certain time before shift end.
• Multi-shift tasks and resources: Provide options that state whether the same resources are required to work on a multi-shift or multi-day task.

Conclusion

These advanced modeling capabilities represent significant advancements in project management technology. By allowing more realistic modeling of project constraints and dynamics, they enable project managers to create plans that better adapt to real-world conditions during execution. Rather than constantly adjusting schedules due to model limitations, managers can focus on higher-value project management tasks.

The implementation of these capabilities in software like Aurora has demonstrated their value in complex projects at organizations including NASA, Boeing, General Dynamics, and Los Alamos National Laboratory. While there is always a balance to be struck between model detail and human judgment, these enhanced modeling capabilities give project managers the tools to create plans with the appropriate level of detail for their specific projects, ultimately leading to more accurate schedules and more successful projects.