Simulation-based approach for Multiproject Scheduling based on composite priority rules

ArXiv ID: 2406.02102 “View on arXiv”

Authors: Unknown

Abstract

This paper presents a simulation approach to enhance the performance of heuristics for multi-project scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to, therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multi-project scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management.

Keywords: Multi-Project Scheduling, Resource-Constrained Scheduling, Simulation, Heuristics, Portfolio Management, Project Portfolio Management

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders
  • Why: The paper employs advanced scheduling heuristics and NP-hard complexity theory but focuses on a practical, simulation-based implementation with strong empirical validation on a standard benchmark library (MPSPLib).
  flowchart TD
    Start["Research Goal: Enhance multi-project scheduling with composite priority rules"] --> Input1["Input: MPSPLib dataset<br>projects, resources, constraints"]
    Input1 --> Method["Method: Simulation-based heuristic<br>Sequential application of composite priority rules"]
    Method --> Process["Computational Process:<br>1. Generate multiple feasible schedules<br>2. Evaluate resource-constrained solutions"]
    Process --> Analyze["Analysis: Compare simulated schedules<br>against known optimal/heuristic benchmarks"]
    Analyze --> Outcome["Key Findings:<br>• Composite rules outperform single rules<br>• Higher probability of shorter makespan<br>• Practical for time-constrained portfolio management"]