The development of renewable energy in rural areas presents significant potential. Integrating renewable energy sources, such as wind power and photovoltaics, into microgrids as distributed generation systems offers a viable approach for local energy utilization. In recent years, the rapid advancement of agriculture, forestry, animal husbandry, and fisheries has led to an increasing demand for electricity in these regions. However, the existing power infrastructure remains underdeveloped, resulting in a pronounced imbalance between supply and demand. This paper investigates the optimization of rural microgrid configurations by incorporating demand response strategies and the synergistic interactions among wind turbines, photovoltaic systems, batteries, and loads. A multi-objective optimization model is developed to maximize annual profits and environmental externality (namely, the proposed microgrid achieves equivalent carbon dioxide emissions reductions by replacing thermal power generation through either selling green electricity to the main grid or meeting rural load demands), which is subsequently transformed into a single-objective formulation using the Shapley value method and solved via a global harmonic search algorithm. Simulation results validate the applicability of the proposed solution method and demonstrate the economic performance, development potential, and environmental benefits of the optimized microgrid configurations.

To enhance the sustainable operation of electricity–hydrogen coupling multi-microgrids (EHCMMG), this study proposes a multi-objective dispatch optimization framework driven by electricity price prediction. Although EHCMMG plays a vital role in renewable energy integration and multi-energy synergy, three major sustainability-related research gaps remain: insufficient consideration of cross-regional, multi-market, and multi-stakeholder interests; inadequate electricity–hydrogen demand response mechanisms; and limited investigation of uncertainty modeling that balances economy and security. To address these issues, this study first designs an EHCMMG architecture that supports electric-hydrogen interactions both within and outside the cluster. An electricity price prediction-driven multi-objective dispatch optimization model oriented toward multiple stakeholders is then proposed. This model incorporates incentive-based electricity–hydrogen demand response and constraints on carbon emissions. Moreover, operational uncertainties arising from renewable energy generation are addressed through the coordinated integration of spinning reserve capacity constraint and chance-constrained programming. The results show that the cluster cost, the market integrated operator (MIO) net revenue, user energy cost, and total carbon emissions are CNY 17.502 million, CNY 12.684 million, CNY 5.556 million, and 8168.126 tons in baseline scenario, respectively. The proposed model effectively balances economic efficiency, operational reliability, and low-carbon performance, thereby enhancing the overall sustainability of the EHCMMG.

Self-heating deicing pavement has demonstrated superior electrothermal properties and sustainability, which could reduce the frequency of traffic accidents caused by snow and ice accumulation. Nonetheless, differential thermal expansion between steel reinforcements and the pavement concrete occurs during the electric heating period. In this paper, the electric-thermal-mechanical numerical coupled model of pavement with embedded steel reinforcement network is established. Twenty-seven working conditions considering variations in copper mesh embedment depth, heating layer thickness, and heating voltage, are designed and applied to the calibrated numerical model. To ensure the deicing functionality and structural strength of reinforced concrete pavement, a response surface analysis was performed based on the simulation results to establish the relationship between each factor and the corresponding response values. Subsequently, a multi-objective optimization model was developed. The results show that a reinforced concrete pavement with an embedded depth of 6 cm, a heating layer thickness of 10.54 cm, and a heating voltage of 24 V can reach 0 °C after 5.3 hours of heating and rise to 17.96 °C after 24 hours. The optimal combination of above design parameters could guarantee the self-heating pavement not affected by snow, ice. It provides important parameter references and technical support for the design of electrically heated snow-melting pavement systems.

Background: Permanent Magnet Synchronous Generators (PMSGs) face design challenges due to multi-physics coupling and conflicting optimization goals. Objective: Develop a multi-objective optimization model for PMSGs to enhance efficiency, minimize torque ripple, and ensure structural feasibility. Methods: We derived key equations for slot filling ratio, generator length, efficiency, and THD in the Optimization Model. Optimization variables were reduced via geometric relationships and empirical equations. A hybrid workflow combining 2D-FEA and evolutionary algorithms (EA) identified Pareto-optimal solutions. Results: Compared with the initial design, the optimized PMSG prototype has significantly improved in terms of maximum stator partial flux density, torque ripple coefficient, total harmonic distortion( THD ), power generation efficiency, slot fill factor, and temperature rise. The bench test verified the accuracy of the optimized design, and the voltage error was less than 3.3% compared with the simulation results. Conclusions: This study proposes a systematic methodology to balance technical performance and engineering constraints, offering a scalable solution for PMSG design in energy systems.

Aviation maintenance scheduling presents complex challenges due to dynamic task arrivals, stochastic service times, and the need to balance competing objectives. To address this, we introduce a novel framework that integrates these factors into a real-time, multi-objective optimization model. Our approach combines mathematical modeling with advanced meta-heuristic algorithms, supported by new theoretical performance guarantees. We evaluate nine algorithms across 810 experimental configurations, demonstrating that our proposed methods achieve statistically significant improvements over baseline scheduling approaches. Among single-objective metrics, Adaptive Tabu Search (ATS) achieves the lowest cost at $13,072 ± $4544, while multi-objective methods provide diverse Pareto fronts with a mean hypervolume of 0.0268 (normalized scale [0,1]), dominating significantly more of the objective space than comparative methods. The framework demonstrates the potential for significant operational cost reductions and provides a robust theoretical foundation for developing next-generation maintenance scheduling systems.

Under the backdrop of the “dual carbon” strategy, leveraging renewable energy to promote low-carbon renovations of existing buildings has become an important path for the construction industry to achieve sustainable development. Currently, to achieve efficient utilization of renewable energy in buildings, key issues such as energy type matching, optimization of energy storage system configuration, and multi-objective collaborative decision-making need to be addressed. This paper explores the adaptation mechanisms between building characteristics, such as layout, climate impact, and energy distribution, and different energy systems, highlighting the core role of optimizing energy storage technology in achieving flexible energy use and dynamic regulation. Combined with artificial intelligence algorithms and multi-objective optimization models, it supports the real-time trade-off and optimization of the system’s operational efficiency, economic performance, and environmental benefits. This review aims to provide theoretical and practical references for enhancing the overall energy efficiency of buildings and promoting the scientific planning and refined operation of renewable energy in sustainable building practices.

ABSTRACT Considering time-varying factors like weather and road conditions is critical for open-pit mine route planning, yet it has rarely been addressed in existing studies. This gap leaves transportation solutions unable to guide actual ore production, so data-driven truck scheduling optimisation for open-pit mines is developed in this study. First, a multi-objective truck routing optimisation model with time windows is presented according to a real application scenario. Subsequently, a data-driven optimisation method is introduced to construct a dynamic random forest (RF) surrogate model using truck trajectory data to assess the quality of the new solutions. Moreover, a novel multi-objective evolutionary algorithm is proposed to obtain a set of Pareto-optimal alternatives. It is composed of an improved multi-objective ant colony optimiser and a fitness mechanism, which can strike a balance between diversity and convergence of the solutions. The proposed algorithm is simulated for routing optimisation of an open-pit mine in China. Under the first decision objective, Optimal Truck Route Scheme (OTRS)’s LowerBound outperforms Multi-Objective Ant Colony Optimisation (MOACO) by 15%–40% in most instances and averages better than Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2); under the second, its upper-lower bound range is approximately 3.7 times that of MOACO and about 4.27% larger than that of NSGA-II, while SPEA2 has outliers in many cases. The experimental results confirm that this approach has robust performance, with faster hauling efficiency and lower cost.

Thickeners are devices that utilise the theory of gravitational settling to achieve solid–liquid separation. As the primary load-bearing structure, the thickener pool requires structural verification and optimisation. However, the high operational difficulty of CAD and CAE software and the difficulty of sharing models across these platforms pose substantial challenges. To address this issue, this study proposes a general cross-platform design and simulation strategy that integrates a knowledge-base-driven framework with universal methodologies such as feature-ID stability, sketch modelling, and model-driven parametric design, enabling consistent CAD–CAE interoperability across platforms. Based on this strategy and secondary development techniques, a parametric design and simulation system for thickener pools was developed, reducing the design and preprocessing time from 10 h to 0.5 h. Finally, taking a 20-meter-deep conical thickener pool as an example, the system was used to efficiently complete the design and simulation tasks, and experimental data were employed to establish a multi-objective optimisation model that enables structural optimisation of the steel framework. This study establishes a reusable, knowledge-base-driven framework for cross-platform CAD–CAE parametric design of engineering equipment, and provides a practical modelling and optimisation paradigm for load-bearing structures, laying the foundation for future extensions toward Pareto-based multi-objective optimisation and cross-domain applications.

Path planning for large-scale agricultural fields faces challenges such as irregular field shapes, uncertain boundaries, and the need to balance path efficiency, energy consumption, and coverage quality. To address these problems, this research introduces a strategy-aware hierarchical hybrid optimization framework (HANS) for autonomous agricultural operations. This framework introduces a global principal axis extraction method based on Principal Component Analysis (PCA), utilizing the statistical distribution of field boundaries to guide path direction, thereby improving robustness against boundary noise and irregular geometries. The framework integrates Adaptive Large Neighborhood Search (ALNS) for global exploration and Tabu Search (TS) for local optimization, forming a tightly coordinated hybrid structure. The framework further employs a Pareto-set-based decision support selection strategy to solve a multi-objective optimization model encompassing machine kinematics, turning patterns, and energy-aware cost evaluation. This strategy provides three methods: weighted preference-based compromise solution selection, crowding distance-based diversified solution selection, and single-objective extreme value-based dedicated optimization solution selection. To balance the impact of path length, energy consumption, and coverage rate, we assigned equal or nearly equal weights to them (i.e., (0.33, 0.33, 0.34)). Furthermore, the framework incorporates operators and feedback learning mechanisms specific to agricultural coverage path problems to enable adaptive operator selection and reduce reliance on manual parameter tuning. Simulation results under three representative field scenarios show that compared to fixed-direction planning, HANS improves the average coverage rate by 0.51 percentage points and reduces fuel consumption by 4.34%. Compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), and Simulated Annealing (SA), the proposed method shortens the working path length by 0.37-0.83%, improves coverage rate by 0.34-1.11%, and reduces energy consumption by 0.61-1.03%, while maintaining competitive computational costs. These results demonstrate the effectiveness and practicality of HANS in large-scale autonomous farming operations.

To address the limitations of traditional algorithms in human resource scheduling optimization under multiple constraints—such as slow convergence and low constraint satisfaction rates—this study proposes a hybrid intelligent algorithm (ADE-ACO) integrating adaptive differential evolution (ADE) and ant colony optimization (ACO). First, a multi-objective optimization model for human resource scheduling is constructed. Then, an improved adaptive differential evolution algorithm is designed, which dynamically adjusts the scaling factor and crossover probability to effectively mitigate the issues of local optima stagnation and premature convergence in conventional methods. Furthermore, by incorporating an adaptive pheromone update mechanism and a multi-attribute dynamic candidate list strategy, the algorithm’s global search capability is significantly enhanced. Experimental validation on the Project Scheduling Problem Library (PSPLIB) benchmark dataset demonstrates that compared to traditional baseline algorithms including standard differential evolution (SDE) and ACO, the proposed ADE-ACO algorithm achieves a 32% significant reduction in makespan ( p < 0.01), improves resource utilization to 92.3%, while maintaining over 95% constraint satisfaction rate, conclusively proving its superiority in both convergence performance and scheduling quality.

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles.

ABSTRACT Warehousing faces increasing pressure to balance cost efficiency, sustainability, and workforce well-being. Existing studies often address these dimensions separately, leaving a gap in integrated decision-making. This study develops and validates a novel engineering sustainability framework that combines the Fuzzy Analytic Hierarchy Process (FAHP) with a multi-objective optimisation model to guide operator allocation under uncertainty. By prioritising Key Performance Indicators (KPIs) across economic, environmental, and social pillars, the framework translates sustainability trade-offs into actionable allocation strategies. A case study in a Tunisian hardware warehouse demonstrates that the approach reduces costs and energy consumption while improving operator safety and job satisfaction. Beyond warehousing, the framework illustrates how engineering methods can operationalise the triple bottom line to design resilient, cost-effective, and socially responsible industrial systems. The findings highlight both theoretical contributions by bridging FAHP and optimisation within sustainable engineering and practical implications for managers seeking to align efficiency with sustainability goals.

Abstract Stainless steel tube bending is a critical manufacturing process where the choice of processing parameters strongly influences the final product quality. Existing approaches often address this as a single-objective problem, focusing on individual metrics while neglecting the interdependencies among them. To overcome this limitation, this study adopts a multi-objective optimization approach. Three key quality indicators were identified in this optimization problem: outer wall thinning, inner wall thickening, and cross-sectional ovalization. A multi-objective optimization model was constructed using NSGA-II to account for these factors simultaneously. This approach not only captures the trade-offs between different objectives, but also provides more solutions for parameter selection. Our approach performs better than existing single-objective optimization approaches, providing a tool to achieve higher quality standards in tube bending operations.

With the increasing integration of renewable energy and intensifying fluctuations in industrial electricity demand, efficient load management of heavy industrial facilities has become a critical issue in the field of demand response. This study proposes a load scheduling optimization method that combines zoning and hierarchical control. For the steel industry, which is characterized by multiple types of high-energy-consuming equipment, functional zones are first defined based on production processes and load characteristics, followed by hierarchical management within each zone according to the response capability and importance of equipment. Furthermore, a multi-objective optimization model is established that considers dynamic electricity prices, scheduling pressure, equipment response costs, and compensation mechanisms. By setting reasonable scheduling constraints, time-based, hierarchical, and zoned optimization scheduling of various types of equipment is achieved. Simulation results based on a typical steel enterprise demonstrate that the proposed method can ensure stable operation of core production equipment while fully tapping the regulation potential of non-core equipment, thereby enhancing the overall flexibility and economic benefits of the load response. This study provides a theoretical foundation and a practical reference for demand response strategies and intelligent load management in energy-intensive industrial parks.

Analysis of Fenugreek gum-based microalgae harvesting technology and its mechanism of action.

Multi-objective phased optimization framework of gray-green-blue infrastructure for synergistic runoff control in data-scarce regions.

This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated framework, combining a genetic algorithm (GA) with a CatBoost-based surrogate model for multi-objective optimization of the injection molding machine parameters. The aim of the optimization is to minimize the cycle time and cycle energy while maintaining the product quality. Ten process parameters were optimized, which are machine-specific. An evolutionary optimization using the NSGA-II algorithm is used to generate the recommended parameter set. The proposed GA-surrogate hybrid approach produces the optimal set of parameters that reduced the cycle time by 4.5%, for this specific product, while maintaining product quality. Cycle energy was evaluated on an hourly basis; its variation across candidate solutions was limited, but it was retained as an optimization objective to support energy-based process optimization. A total of 95% of the generated solutions satisfied industrial quality constraints, demonstrating the robustness of the proposed optimization framework. While classical Design of Experiment (DOE) approaches require sequential physical trials, the proposed GA-surrogate framework achieves convergence in computational iterations, which significantly reduces machine usage for optimization. This approach demonstrates a practical way to automate data-driven process optimization in an injection mold machine for an industrial application, and it can be extended to other manufacturing systems that require adaptive control parameters.

A bi-level multi-objective optimization model for cross-provincial transfer fees to promote cooperation in renewable energy consumption

The search for integrated solutions in the machining industry, particularly in job shop production scheduling and maintenance planning, is crucial. This development aims to simultaneously inform production scheduling decisions and maintenance planning, thereby minimizing makespan and operational costs. The limited time for decision-making on an industrial scale presents a challenge in providing fast and optimal decisions. Therefore, optimization is carried out using an approximation approach: NSGA-II and MOALNS. A multi-objective optimization model has been successfully developed and can provide optimal results for the integrated production and maintenance scheduling. The mathematical model demonstrated robustness in various solved cases. NSGA-II reduced operational costs by up to 28% but increased makespan by 4%. In comparison, MOALNS reduced the makespan by up to 4% but only reduced operational costs by up to 16%. Finally, NSGA II consistently provides better performance results than MOALNS. With significant reductions in operational costs, the industry can save significantly on total operational costs, such as machine maintenance, labor costs, and maintaining output quality, from existing operations and can continue to serve customers more sustainably. NSGA-II is superior in covering the objective function space, achieving solution quality close to the Pareto front, and maintaining a consistent distribution of solutions.

Abstract In high-precision equipment such as launch vehicles and aero-engines, the stress-free assembly of rigid tubes is a critical factor in ensuring operational reliability; however, the prevalence of manufacturing and positioning deviations has rendered this a persistent and unresolved challenge within the industry. While existing research primarily focuses on the derivation of theoretical pipeline parameters, it largely overlooks the critical influence of end face machining parameters on the assembly workflow. This oversight necessitates iterative trimming of tube ends during actual production, severely constraining assembly efficiency. Consequently, this significant research gap at the practical implementation level has yet to receive sufficient attention. To address this issue, this paper proposes a novel flexible assembly method governed by multi-objective constraints. The method begins by employing laser scanning to capture the assembly environment and determine installation boundaries, enabling the adaptive modeling of tube geometry. On-site machining is then performed to mitigate dimensional uncertainties caused by environmental variation. A multi-objective optimization model is developed to determine cutting parameters, incorporating three critical constraints: tube length, horseshoe port misalignment, and weld surface perpendicularity. These are formulated within a nonlinear constrained optimization framework to achieve one-time end-face machining. The proposed method is validated through physical model experiments and field trials on rocket tubes. The physical model experimental results demonstrate an average assembly gap within 0.2 mm and an average misshapen edge within 0.035 mm. Furthermore, the rocket field trial confirms that the optimized parameters achieve successful one-time assembly, strictly satisfying stress-free requirements and significantly enhancing overall efficiency.