17534385. OPTIMIZER AGNOSTIC EXPLANATION SYSTEM FOR LARGE SCALE SCHEDULES simplified abstract (International Business Machines Corporation)

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OPTIMIZER AGNOSTIC EXPLANATION SYSTEM FOR LARGE SCALE SCHEDULES

Organization Name

International Business Machines Corporation

Inventor(s)

Surya Shravan Kumar Sajja of Bangalore (IN)

Kanthi Sarpatwar of Elmsford NY (US)

Lam Minh Nguyen of Ossining NY (US)

Yuan Yuan Jia of BeiJing (CN)

Stephane Michel of Roquefort les Pins (FR)

Roman Vaculin of Larchmont NY (US)

OPTIMIZER AGNOSTIC EXPLANATION SYSTEM FOR LARGE SCALE SCHEDULES - A simplified explanation of the abstract

This abstract first appeared for US patent application 17534385 titled 'OPTIMIZER AGNOSTIC EXPLANATION SYSTEM FOR LARGE SCALE SCHEDULES

Simplified Explanation

The patent application describes a computer implemented method that uses artificial intelligence to explain large scale scheduling solutions. Here are the key points:

  • The method receives a resource constrained scheduling problem, which includes a set of tasks with various resource requirements and constraints.
  • An optimizer process is used to determine a schedule for the tasks, with the goal of minimizing the makespan (total time taken to complete all tasks).
  • A minimal set of resource links is generated based on resource dependencies between tasks.
  • These resource links are added to the original scheduling problem as precedence constraints, while removing all other resource constraints.
  • A set of critical tasks is computed using a non-resource constrained critical path.
  • Schedules are provided with an explanation of the optimized order of tasks based on the use of the non-resource constrained critical path.

Potential applications of this technology:

  • Project management: This method can be used to optimize scheduling in large-scale projects with multiple tasks and resource constraints, providing explanations for the optimized order of tasks.
  • Manufacturing: The method can help optimize production schedules in manufacturing processes, considering resource requirements and dependencies between tasks.
  • Logistics: It can be applied to optimize scheduling in transportation and supply chain management, considering various resource constraints and dependencies.

Problems solved by this technology:

  • Complex scheduling: The method addresses the challenge of scheduling tasks with multiple resource requirements and constraints, finding an optimized order while minimizing makespan.
  • Resource dependencies: It takes into account the dependencies between tasks based on resource requirements, ensuring that tasks are scheduled in the correct order.
  • Explanation of schedules: The method provides explanations for the optimized order of tasks, helping users understand the reasoning behind the scheduling decisions.

Benefits of this technology:

  • Improved efficiency: By optimizing the scheduling of tasks, the method can help minimize the makespan and improve overall efficiency in various domains.
  • Enhanced decision-making: The explanations provided for the optimized order of tasks can help users make informed decisions and understand the impact of resource constraints on scheduling.
  • Scalability: The method is designed to handle large-scale scheduling problems, making it suitable for complex projects and processes.


Original Abstract Submitted

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

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