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Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm.

Jun Long Peng1, Xiao Liu2, Chao Peng3

  • 1Changsha University of Science & Technology, Changsha, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces a multi-skill resource-based multi-modal project scheduling problem (MRCMPSP) and a novel hybrid quantum particle swarm algorithm (HQPSO) to minimize project duration. The HQPSO demonstrates superior performance for effective real-world project scheduling.

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Area of Science:

  • Operations Research
  • Management Science
  • Computer Science

Background:

  • Traditional project scheduling models often oversimplify by considering single factors, not reflecting real-world complexities.
  • Existing algorithms may lack efficiency or applicability for multi-faceted project environments.

Purpose of the Study:

  • To propose and model the multi-skill resource-based multi-modal project scheduling problem (MRCMPSP).
  • To develop and validate a novel hybrid quantum particle swarm optimization (HQPSO) algorithm for solving MRCMPSP.
  • To enhance project scheduling algorithms and provide practical management insights.

Main Methods:

  • Development of the MRCMPSP using a resource capability matrix and constraints.
  • Selection and enhancement of the quantum particle swarm algorithm (QPSO) into a hybrid quantum algorithm (HQPSO) with JAYA optimization.
  • Design of comparative experiments (independent and practical) to evaluate algorithm performance across various scales.

Main Results:

  • The proposed HQPSO algorithm exhibits superior convergence performance and solution accuracy.
  • Experimental results validate the algorithm's effectiveness and generality for MRCMPSP.
  • The study successfully provides an effective scheduling solution applicable to real-world scenarios.

Conclusions:

  • The developed MRCMPSP offers a more realistic framework for project scheduling.
  • The HQPSO algorithm is a robust and efficient method for solving complex scheduling problems.
  • The research provides valuable managerial insights and advances project management methodologies.