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Multi-project scheduling optimization with artificial intelligence: a novel metaheuristic framework

delete2025-12-29
delete0
PRE
AI
V
Vu Hong Son Pham
L
Luu Ngoc Quynh Khoi *
L
Luu Xuan Loc
DOI：10.1007/s10586-025-05834-8delete
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Abstract

Abstract

En 中文
This research delivers a comprehensive analysis of Artificial Intelligence (AI) applications for solving the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP), one of the most complex optimization challenges in construction management. Using a unified framework, seven state-of-the-art multi-objective metaheuristic algorithms and the proposed Tournament Selection Multi-Objective Giant Pacific Octopus Optimizer (MOGPOO-TS)—were applied to multi construction projects to determine optimal start–finish times while balancing stringent resource, time, cost, and quality constraints. MOGPOO-TS was specifically designed to explore vast, high-dimensional solution spaces, maintain diversity along the Pareto front, and converge quickly to high-quality schedules. Across all evaluation criteria, MOGPOO consistently outperformed the other six algorithms, demonstrating superior trade-offs among time, cost and quality as well as remarkable stability under complex multi-project constraints. This highlights MOGPOO-TS’s pivotal role as an AI-enhanced benchmark for accelerating and refining multi-project construction scheduling, bridging the gap between single-project and enterprise-wide optimization and setting a new standard for practical operations research in the construction sector.
Keywords:
Artificial intelligence
Optimization
Multi-project
Giant pacific octopus optimizer

Journal

C
Cluster Computing
IF:
0
Papers:
691
Citations:
1

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No organization information available