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Simulation-based framework for stochastic multi-mode resource-constrained project scheduling.

Ali Rahimifard1, Isa Nakhai-Kamalabadi2, Kaveh Khalili-Damghani1

  • 1Department of Industrial Engineering, ST.C., Islamic Azad University, Tehran, Iran.

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Summary
This summary is machine-generated.

This study presents a new simulation framework for uncertain project scheduling. It combines Discrete Event Simulation (DES) and Multi-Agent Systems (MAS) to improve planning in complex, resource-constrained environments.

Keywords:
Discrete-event simulation (DES)Multi-agent systems (MAS)Multi-mode resource-constrained project schedulingSimulation modeling, AnyLogicStochastic project schedulingTaguchi design of experiments

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Area of Science:

  • Operations Research
  • Project Management
  • Simulation Modeling

Background:

  • Project scheduling is complex due to uncertain activity durations and resource limitations.
  • Existing models often struggle to capture real-world dynamic uncertainties and interactions.
  • The stochastic multi-mode resource-constrained project scheduling problem (SN-MMRCPSP) presents significant planning challenges.

Purpose of the Study:

  • To introduce a novel simulation-based framework for addressing the SN-MMRCPSP.
  • To enhance project planning effectiveness in uncertain and dynamic environments.
  • To improve decision-making for complex projects with resource constraints.

Main Methods:

  • A Hybrid Discrete Event Simulation (DES) and Multi-Agent Systems (MAS) architecture was developed.
  • Taguchi Design of Experiments (DOE) was employed to optimize execution modes.
  • The framework integrates simulation for uncertainty modeling and MAS for complex interactions.

Main Results:

  • The proposed Hybrid DES-MAS model effectively captures project uncertainties and interactions.
  • Taguchi DOE identified optimal execution modes, enhancing model robustness.
  • Case studies and benchmark comparisons validated the framework's practicality and effectiveness.

Conclusions:

  • The developed simulation framework offers a robust solution for SN-MMRCPSP.
  • This approach significantly improves project planning and decision-making under uncertainty.
  • The integration of DES and MAS provides a powerful tool for managing complex projects.