Knowledge-Driven Interval Multi-Objective Scheduling for Green Construction Under Time-Varying Carbon Emission Factors

Reducing carbon emissions during construction is essential for meeting dual carbon targets. Current green scheduling methods assume fixed emission factors, overlooking time-dependent variations driven by grid peak-valley patterns. Under interval duration uncertainty coupled with tight dynamic carbon budgets, conventional algorithms struggle with sparse feasible solutions and slow Pareto front convergence. We formulate a bi-objective interval RCPSP model with time-varying carbon emission factors that minimizes both interval makespan and total carbon emissions. A possibility degree measure converts scalar carbon budgets into linearized hard constraints. To solve this NP-hard problem, we propose the Knowledge-Driven Interval Multi-Objective Evolutionary Algorithm (KD-IMOEA), which integrates four components: Knowledge-Driven Initialization (KDI), Adaptive Time-window Carbon-aware Decoding (TCD), Carbon Budget-aware Repair Mutation (CBM), and Interval Pareto Elite Archive (IPA), forming an end-to-end carbon-aware optimization pipeline. We validate KD-IMOEA on J30 through J120 benchmark instances; results show it outperforms four established algorithms, including NSGA-II, in both convergence and distribution, with hypervolume (HV) gains up to 6.3%. A green building case study confirms that KD-IMOEA exploits spatiotemporal decoupling to identify float time and assign energy-intensive machinery to lower-carbon operating profiles. At the optimal compromise makespan of 169.5 days, this strategy cuts carbon emissions by 3.07% over traditional baselines, enabling management-driven emission savings without extending project duration.