In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations.

Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME).

Production scheduling and machine tool intermittent state control separately influence a workshop’s machining and intermittent energy consumption. Effective scheduling decisions and intermittent state control are crucial for optimizing the overall energy consumption in the workshop. However, the scheduling scheme determines the machine tool intermittent durations, which imposes strong constraints on the decision-making process for intermittent state control. This makes it difficult for intermittent state control to be used in providing feedback and optimizing scheduling decisions, significantly limiting the overall energy-saving potential of the workshop. To this end, a workshop energy-saving scheduling method is proposed integrating machine tool intermittent state control. Firstly, the variation characteristics of workshop machining energy consumption, machine tool intermittent durations, and intermittent energy consumption are analyzed, and an energy-saving optimization strategy is designed. Secondly, by incorporating variables such as intermittent durations, intermittent energy consumption, and variable operation start time, a multi-objective integrated optimization model is established. Thirdly, the energy-saving optimization strategy is integrated into chromosome encoding, and multiple crossover and mutation genetic operator strategies, along with a low-level selection strategy, are introduced to improve the NSGA-II algorithm. Finally, the effectiveness of the proposed method is verified through a machining case. Results show that the generated Gantt chart reflects both production scheduling and intermittent state control decision outcomes, resulting in a 1.51% reduction in makespan, and 3.90% reduction in total energy consumption.

Carbon efficiency, measuring effective output per unit of carbon emissions, is vital for managing low-carbon workshops and advancing sustainable manufacturing. However, production processes often face concurrent discrete events (e.g., equipment failures, parameter adjustments) and numerous emission factors with complex relationships, making it hard to identify dominant factors and event impact degrees, thus lacking direction for operation and maintenance decisions. This paper proposes a deductive monitoring model to analyze carbon efficiency changes under event concurrency. First, for traceability, a multi-resolution enhanced carbon efficiency information transfer network is proposed. It classifies emission factors into time-driven and event-driven accounting, and under the Parallel Discrete Event System Specification framework, adopts a multi-resolution approach with high- and low-resolution models for hierarchical aggregation from equipment to workshop, establishing a traceability path to specific factors. Second, for unclear impact degrees, a dynamic monitoring model for concurrent events is designed. A state-driven dynamic carbon efficiency accounting method automatically settles upon equipment state switching, and a rule-driven priority deduction strategy enables independent accounting of each event’s impact degrees in a determined order. A case study on a machine tool spindle production workshop validates the proposed model. Under baseline conditions, the relative accounting errors for 8 h cumulative carbon emissions and effective output are approximately 1.05% and 0.89%, respectively. In concurrent event scenarios, the model achieves deterministic trajectory reproducibility across 30 independent deduction runs and enables independent impact-degree decomposition, whereas traditional discrete event simulation exhibits trajectory ambiguity. Furthermore, testing under 42 multi-parameter perturbation combinations demonstrates traceability path integrity and accurate root-cause localization, delivering a transparent and reliable quantitative basis for low-carbon maintenance decisions.