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A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm.

Yongquan Xie1, Zude Zhou2, Duc Truong Pham3

  • 1School of Information Engineering, Wuhan University of Technology, Luoshi Road 122, Wuhan 430070, China ; Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China ; School of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham B152TT, UK.

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Summary
This summary is machine-generated.

This study optimizes resource service allocation in manufacturing using a tailored Bees Algorithm for multi-user resource service composition (RSC). The method enhances search speed and success rates by incorporating trusted services and Pareto optimality rules.

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Area of Science:

  • Manufacturing Systems Engineering
  • Operations Research
  • Computer Science

Background:

  • Current manufacturing models require optimal resource service allocation.
  • Multi-user resource service composition (RSC) is a complex, constrained problem.
  • Existing algorithms are not optimized for discrete, multi-objective RSC.

Purpose of the Study:

  • To model and solve the multi-user resource service composition (RSC) problem in open, service-oriented manufacturing.
  • To develop an efficient algorithm for optimal resource allocation considering Quality of Service (QoS).
  • To improve the success rate and search speed of RSC solutions.

Main Methods:

  • Resource Service Composition (RSC) modeled as a constrained, multi-objective problem.
  • Quality of Service (QoS) properties (subjective and objective) used for evaluation.
  • A modified Bees Algorithm tailored for discrete domains, incorporating Pareto optimality rules and constraint handling.
  • Introduction of a trusted service set to guide the search process.

Main Results:

  • The modified Bees Algorithm effectively finds near-optimal solutions for multi-user RSC.
  • Experiments demonstrate improved success rates, faster searching, and avoidance of suboptimal solutions.
  • The inclusion of trusted service sets enhances algorithm performance.
  • The proposed method proves effective for complex resource allocation challenges.

Conclusions:

  • The tailored Bees Algorithm provides an effective solution for multi-user resource service composition in manufacturing.
  • The approach optimizes resource allocation by considering diverse QoS factors and constraints.
  • This method offers a significant advancement in managing complex service-oriented manufacturing environments.