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<p>In recent years, the resource-constrained project scheduling problem (RCPSP) has been recognized as one of the most well-known problems in the project management context. Project managers always try to assign the resources to the project activities optimally in a short period. However, this is a challenging and complex problem that is categorized as an NP-hard problem. This paper proposes a novel gravitational search algorithm (GSA) to solve RCPSP for organizations and project managers in two steps. In the first step, a mathematical model for the RCPSP is developed, including the problem's purposes, constraints and limitations, and several factor levels. In the second step, the GSA algorithm is deployed to solve the problem efficiently. The suggested GSA's effectiveness is demonstrated by simulation results based on benchmarks and compared with various existing algorithms. The findings reveal that the proposed technique improved existing algorithms and decreased the gap.</p>

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A gravitational meta-heuristic algorithm for solving resource-constrained project scheduling problems

  • 期刊论文

  • 发表日期：

    2025年5月17日

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  • Mahdi Yousefi Nejad Attari,
  • Ali Ala,
  • Vladimir Simic,
  • Dragan Pamucar,
  • Nezir Aydin

摘要

In recent years, the resource-constrained project scheduling problem (RCPSP) has been recognized as one of the most well-known problems in the project management context. Project managers always try to assign the resources to the project activities optimally in a short period. However, this is a challenging and complex problem that is categorized as an NP-hard problem. This paper proposes a novel gravitational search algorithm (GSA) to solve RCPSP for organizations and project managers in two steps. In the first step, a mathematical model for the RCPSP is developed, including the problem's purposes, constraints and limitations, and several factor levels. In the second step, the GSA algorithm is deployed to solve the problem efficiently. The suggested GSA's effectiveness is demonstrated by simulation results based on benchmarks and compared with various existing algorithms. The findings reveal that the proposed technique improved existing algorithms and decreased the gap.