Breaking Rework Chains in Low-Carbon Prefabrication: A Hybrid Evolutionary Scheduling Framework

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers in construction management and operational research will find the hybrid evolutionary algorithm and the bi-level optimization model a novel contribution to low-carbon scheduling.

🏢実務担当者:Corporate sustainability teams in construction can use the framework to evaluate trade-offs between quality inspection investment and carbon reduction benefits.

🏛政策担当者:Policymakers designing carbon pricing mechanisms can note the sensitivity analysis showing how different tax rates influence project-level decisions.

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures.

• semanticscholar https://doi.org/10.3390/buildings16050968first seen 2026-05-15 17:21:27