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Performance Optimization for a Class of Petri Nets.

Weijie Shi1, Zhou He2, Chan Gu2

  • 1School of Electro-Mechanical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.

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Summary
This summary is machine-generated.

This study optimizes flexible manufacturing systems (FMSs) using Petri nets (PNs) and a novel genetic algorithm with mixed-integer linear programming (MILP). The method efficiently maximizes system performance by optimizing resource allocation under budget constraints.

Keywords:
Petri netsevolutionary techniquesflexible manufacturing systemsresource allocation

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Computer Science

Background:

  • Petri nets (PNs) are a standard modeling tool for flexible manufacturing systems (FMSs).
  • Optimizing resource allocation (quantities and types) in FMSs is crucial for performance but is a complex, nonlinear problem.
  • Existing methods struggle to achieve globally optimal solutions for FMS performance optimization.

Purpose of the Study:

  • To develop an effective methodology for the performance optimization of FMSs modeled by Petri nets.
  • To maximize FMS performance under a specified budget by optimizing resource quantities and types.
  • To address the challenges of nonlinearity and achieve globally optimal solutions in FMS resource allocation.

Main Methods:

  • A hybrid approach combining a genetic algorithm with mixed-integer linear programming (MILP) was developed.
  • MILP is used to generate candidate resource allocation strategies, including the number of resources.
  • MILP further evaluates resource types and cycle times, guiding the genetic algorithm's next generation.

Main Results:

  • The developed methodology successfully optimizes resource allocation for FMS performance.
  • Simulation studies demonstrated the effectiveness and efficiency of the proposed approach.
  • The hybrid algorithm provides a robust solution for complex, nonlinear optimization problems in FMS.

Conclusions:

  • The integrated genetic algorithm and MILP approach offers a powerful tool for FMS performance optimization.
  • This methodology enables maximizing system performance within budget constraints through intelligent resource management.
  • The study highlights a significant advancement in solving complex optimization challenges in flexible manufacturing systems.