Benefits of Additional Project Modeling Capabilities: Details for Alternative Resource Combinations & Preference Constraints

Effectively managing complex projects requires sophisticated modeling and scheduling capabilities that go far beyond basic task assignment. This section introduces a range of advanced constraint-handling features that have proven valuable across multiple project and production domains. These capabilities support nuanced real-world scenarios—such as physical space limitations, specialized human resources, concurrent or exclusive task requirements, ergonomic considerations, and variable job durations. 

Beyond conventional tools, this approach also addresses dynamic constraints like shift-dependent workflows and resource availability changes. A notable example is in high-volume mortgage auditing, where tasks must align with strict training requirements, timing, and auditor preferences. By integrating both hard and soft constraints into the scheduling engine, systems like Aurora can automate thousands of audits weekly, rapidly adapting to shifting conditions while ensuring accuracy and efficiency. The following content explores these useful real-world project modeling capabilities in greater detail, highlighting how flexible modeling empowers more intelligent, adaptable project management.

This section is derived from a paper titled “Need for and Benefits of Additional Real-World Project Modeling Capabilities” written by Robert Richards, a research scientist at Stottler Henke Associates. These useful real-world project modeling capabilities have been incorporated into the project management and intelligent scheduling tool, Aurora, and they can be previewed in our ever-evolving Aurora-Viewer, a free Primavera P6 XER file viewer for Windows.

• Ability to handle physical space constraints, including considering the creation and elimination of the space during the project. 
• Ability to model human resources with details beyond just an occupation, such as occupation plus a set of specializations and/or certifications. 
• Concurrent Constraints – Specify that two jobs need to happen at the same time in the schedule. 
• Exclusivities / non-concurrent constraints – Specify that a job cannot happen at the same time as another job or class of jobs. 
• Preferred Resources – Specify a preference order when defining a set of resources that are mostly interchangeable. 
• Alternative Resource Combinations – Specify different combinations of requirements that could be used to complete a task, including variants with different durations. 
• Ergonomic Constraints – Consider human physical limitations. For example, workers may have limitations on how long they can work on their knees, both in one sitting and throughout the whole shift. 
• Variable Duration Jobs – Specify that a job could use more people and get done more quickly, or fewer people and get done more slowly. 
• Special Manufacturing/Shift Control Properties – This is a set of properties that allows the user to control how jobs interact with shift breaks (can it go between shifts, can it go from one day to another, does it have to complete a certain length of time before a shift ends, etc.) 
• Capacity Change Constraints – This allows the user to specify a relationship between a task and a resource. Some tasks may make a resource available (e.g., adding a space zone that can subsequently be used for work), others may make a resource unavailable (e.g., installing panels that block access to a space zone). 
• Jig Support – This is specialized support that ensures that a jig is assigned to a series of work and will be retained throughout the work statement

Applying Advanced Modeling to Real-World Scheduling: A Case Study on Mortgage Auditing

The following case demonstrates how these useful real-world project modeling principles are applied to streamline and optimize audit scheduling at scale.

Mortgage auditing is routinely performed on lenders to guarantee that mortgage approvals are appropriate and unbiased. A large mortgage auditing company may perform thousands of audits for dozens of clients in a given week. Each audit goes through multiple synchronized steps, and all steps must be completed by a hard deadline. There are a number of constraints on how those audits should be allocated to auditors to create a schedule: 

• Training: There are a wide variety of mortgage types, and audits must be assigned to personnel with the correct training
• Consistency: Assignment of a consistent, minimal subset of auditors is advantageous 
• Thoroughness: At least two auditors are required. 

Some of these constraints are soft (e.g., using consistent auditors for a client, or preferring a small number of auditors), while others are hard (e.g., training requirements or deadline satisfaction), the soft constraints are an example of preference constraints. Providing the capability to model preference constraints greatly complicates the scheduling process, which shows why most project management tools do not provide the option. 

To demonstrate one technique to handle these preferences efficiently is to model a queue for each auditor, with logic to determine on which day a given audit will be completed. By populating this queue in due-date order, starting with the most preferred formulation but shifting work based on a variety of heuristics, the scheduler is able to quickly find a solution that maximizes the soft/preference constraints, (while still satisfying the hard constraints). In this case, the customized Aurora software solution allows automated scheduling of thousands of audits, a process that used to require a human scheduler to devote a person-day to each week. Because it is automated, Aurora can also update the schedule as frequently as needed to support rapid adaptation to changing circumstances. The figure below showcases one of the custom interfaces developed to show the human auditor schedules; the team assignment display that dynamically shows the auditors who are considered acceptable for the client.

Team Assignment Display