How Aurora Works

Introduction

Most other scheduling systems use simple rules to select and schedule tasks and assign resources to carry them out. These rules usually consider only limited information about the required tasks, resources, and constraints, so the generated schedules are far from optimal.

Many systems rely on mathematical optimization to search systematically for the best scheduling solution. However, as the number of scheduled tasks and constraints grows–particularly in complex project scheduling–the computer time needed to solve the problem increases exponentially, making this approach impractical for managing large, complex operations.

Aurora solves complex project scheduling problems effectively by encoding and applying sophisticated scheduling knowledge and decision-making rules, along with complex constraints and resource requirements.

Aurora encodes attributes of data objects representing individual tasks, groups of tasks, resources, resource sets, and constraints.

Aurora’s built-in and user-supplied decision rules produce better schedules by considering the values of these attributes at key scheduling decision points such as:

• Determining which task to schedule next,
• Assigning the best time and resources to each task to optimize the overall schedule, and
• Handling situations in which some resources are unavailable when the task is scheduled.

Aurora’s knowledge-rich approach enables it to  combine human expertise with intelligent algorithms to generate superior schedules. Aurora technology addresses aerospace operations planning challenges by:

• Generating near-optimal schedules for highly customized and resource-constrained aerospace schedules.
• Generating and revising schedules rapidly, so organizations can quickly adapt to changing conditions.
• Providing complex constraint support beyond what is found in other scheduling tools.
• Explaining why Aurora selected the scheduled  time and resources for each task.
• Exploiting domain-specific scheduling knowledge and requirements, entered quickly and easily via graphical user interface.
• Detecting and highlighting scheduling problems such as unresolved conflicts.

Aurora accelerates airplane assembly operations, enabling Bombardier Learjet to adjust smoothly to changes in production rate and component delivery dates.

Mitsubishi Heavy Industries selected Aurora to accelerate its production of composite wings for the Boeing 787 Dreamliner.

Complex Constraint Modeling

Conventional scheduling systems often support only basic constraints such as finish-to-start, start-to-start, finish-to-finish, and start-to-finish. Without a complete and accurate model of the constraints that schedules must satisfy, simpler systems cannot even determine whether a candidate schedule is valid.

By contrast, Aurora enables specification and enforcement of complex constraints, so it can schedule projects that other tools cannot even model. Examples of these constraints include:

• Resource requirements for each task (e.g., labor, equipment, space, materials),
• Exclusivities (incompatible tasks that cannot run concurrently),
• Preferred resources,
• Ergonomic constraints,
• Shift-based constraints, and
• Same-resource constraints for related tasks.

Advanced Algorithms

Aurora uses advanced techniques such as bottleneck avoidance, which pre-analyzes the resources required by all the tasks, prior to scheduling them, to ensure that the most constrained resources are assigned to the tasks that have the greatest need for them, resulting in better schedules for complex project scheduling challenges.