Results in Control and Optimization最新文献_第2页

Strategic intervention policies of human-to-human viral encephalitis: a mathematical control approach

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-14 DOI: 10.1016/j.rico.2026.100658

M.S. Rahman , Rehena Nasrin , M.H.A. Biswas

Encephalitis is an acute inflammatory disease of the brain and continues to pose a significant public health challenge, particularly in the case of viral infections capable of sustained human-to-human transmission and progression to severe clinical outcomes. Effective disease management requires intervention strategies that can reduce transmission while avoiding excessive strain on limited healthcare resources. In this study, we develop and analyze an SEITR-type compartmental model that incorporates multiple intervention measures, including prevention, early treatment, intermittent therapy, and suppressive treatment. To better capture disease severity and healthcare demand, additional compartments representing intensive care unit (ICU) admission and ventilator support are included. Numerical simulations are carried out to investigate the combined impact of these interventions on disease dynamics and associated costs. The results indicate that coordinated implementation of control measures can substantially reduce the epidemic burden, lowering the peak number of infections by approximately 85 % and cumulative cases by about 95 % compared with an uncontrolled scenario, while remaining economically feasible within the model assumptions. These findings highlight the potential benefits of integrated intervention strategies for mitigating transmission and managing healthcare capacity during encephalitis outbreaks. The proposed framework provides a quantitative basis for comparative assessment of control strategies and may serve as a decision-support tool for exploring intervention trade-offs in the context of viral encephalitis.

脑炎是一种急性脑部炎症性疾病，继续对公共卫生构成重大挑战，特别是在病毒感染能够持续人际传播并发展为严重临床结果的情况下。有效的疾病管理需要能够减少传播的干预策略，同时避免对有限的卫生保健资源造成过度压力。在本研究中，我们开发并分析了seitr型室室模型，该模型包含多种干预措施，包括预防、早期治疗、间歇治疗和抑制性治疗。为了更好地捕捉疾病严重程度和医疗保健需求，还包括了代表重症监护病房（ICU）入院和呼吸机支持的额外隔间。进行数值模拟，以调查这些干预措施对疾病动态和相关成本的综合影响。结果表明，与不受控制的情况相比，协调实施控制措施可以大大减轻流行病负担，将峰值感染人数减少约85%，累计病例减少约95%，同时在模型假设范围内保持经济可行性。这些发现强调了综合干预策略在脑炎暴发期间减轻传播和管理卫生保健能力方面的潜在益处。提出的框架为控制策略的比较评估提供了定量基础，并可作为探索病毒性脑炎背景下干预权衡的决策支持工具。

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引用次数: 0

Optimal transport and incentive design in multi-agent economic control 多主体经济控制下的最优运输与激励设计

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-12 DOI: 10.1016/j.rico.2026.100656

Ramen Ghosh

This paper develops a principled framework for incentive design in multi-agent economic systems using tools from optimal transport (OT) theory and decentralized control. We consider a class of stochastic multi-agent environments in which each agent selects actions to minimize individual cost functions that depend on both private preferences and aggregate outcomes. To promote socially desirable allocations, we introduce an OT-based mechanism design approach, where incentives are computed as gradients of a Lagrangian dual formulation over probability measures. Our main results establish: (i) a KKT-type characterization of incentive compatibility in Wasserstein space, (ii) monotonicity and fairness of equilibrium allocations under convex coupling, (iii) structural convexity of cost functionals over coupled agent dynamics, (iv) convergence of iterative market updates to optimal allocations, and (v) efficiency guarantees under decentralized feedback. We demonstrate that fairness and incentive alignment emerge naturally as solutions to constrained OT problems, allowing for scalable, interpretable, and robust economic control policies. This formulation provides a unifying perspective on decentralized optimization, mechanism design, and ergodic fairness in economic networks, and opens new directions for data-driven social planning under uncertainty.

本文利用最优运输理论和分散控制理论，建立了多智能体经济系统激励设计的原则框架。我们考虑了一类随机多智能体环境，其中每个智能体选择行动以最小化依赖于私人偏好和总结果的个体成本函数。为了促进社会理想的分配，我们引入了一种基于ot的机制设计方法，其中激励被计算为拉格朗日对偶公式在概率度量上的梯度。我们的主要研究结果建立了：(i) Wasserstein空间中激励相容性的kkt型表征，（ii）凸耦合下均衡分配的单调性和公平性，（iii）耦合agent动力学上成本函数的结构凸性，（iv）迭代市场更新对最优分配的收敛性，以及(v)分散反馈下的效率保证。我们证明，公平和激励一致性作为受限OT问题的解决方案自然出现，允许可扩展、可解释和稳健的经济控制政策。这一表述为经济网络中的分散优化、机制设计和遍历公平提供了统一的视角，为不确定条件下数据驱动的社会规划开辟了新的方向。

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引用次数: 0

Optimizing epidemic control: Nash game approach to stochastic modeling with Brownian motion 优化流行病控制：布朗运动随机建模的纳什博弈方法

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-07 DOI: 10.1016/j.rico.2026.100657

Md. Abdullah Bin Masud , Sharmina Rahman , Faijun Nesa Shimi , Mostak Ahmed , Rathindra Chandra Gope

This research expands upon stochastic modeling for COVID-19 management by incorporating Nash game theory to optimize control strategies for disease transmission. Building on our original model, which has integrated Brownian motion and nonlinear dynamics to enhance diagnosis and isolation procedures, we now apply Nash game theory to explore the interactions between multiple control variables. Using Pontryagin’s Maximum Principle for theoretical analysis and MATLAB and Python for numerical simulations, we demonstrate that Nash control offers a more practical approach than traditional game theory for balancing interventions. The model’s performance, validated with Worldometer data, achieves low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), underscoring its high predictive accuracy. Our findings suggest that Nash control provides a superior framework for real-time epidemic management by optimizing disease control policies, particularly when coordinating antiviral treatments and isolation measures. This work highlights the advantages of Nash-based strategies in developing robust and adaptive epidemic management systems.

本研究扩展了新冠肺炎管理的随机建模，并结合纳什博弈论优化疾病传播控制策略。在我们的原始模型的基础上，我们整合了布朗运动和非线性动力学来增强诊断和隔离程序，我们现在应用纳什博弈论来探索多个控制变量之间的相互作用。使用庞特里亚金的极大值原理进行理论分析，使用MATLAB和Python进行数值模拟，我们证明纳什控制提供了比传统博弈论更实用的方法来平衡干预。该模型的性能通过Worldometer数据验证，实现了较低的均方根误差（RMSE）和平均绝对百分比误差（MAPE），强调了其较高的预测精度。我们的研究结果表明，纳什控制通过优化疾病控制政策，特别是在协调抗病毒治疗和隔离措施时，为实时流行病管理提供了一个优越的框架。这项工作突出了基于纳什的策略在开发健壮和适应性强的流行病管理系统方面的优势。

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引用次数: 0

Stability and weighted l2-gain analysis of discrete-time switched T–S fuzzy systems based on admissible-edge-dependent weighted average dwell time strategy 基于允许边相关加权平均停留时间策略的离散时间切换T-S模糊系统稳定性及加权12增益分析

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-05 DOI: 10.1016/j.rico.2026.100655

Qiang Yu, Lijuan Mao

The paper introduces the admissible-edge-dependent weighted average dwell time switching strategy that not only considers the differences and compensation between subsystems, but also takes into account the switching order of subsystems. The global uniform asymptotic stability and weighted

l_{2}

-gain of a class of discrete-time switched nonlinear systems and its related switched T–S (Takagi–Sugeno) model are studied under the new strategy and the multiple discontinuous Lyapunov function approach. The obtained results present a larger feasible range of switching signals than the existing results. Finally, a numerical example is given to illustrate the validity and superiority of the results.

提出了一种基于允许边的加权平均停留时间切换策略，该策略不仅考虑了子系统间的差异和补偿，而且考虑了子系统间的切换顺序。研究了一类离散时间切换非线性系统及其相关的切换T-S （Takagi-Sugeno）模型在新策略和多重不连续Lyapunov函数方法下的全局一致渐近稳定性和加权12增益。所得结果比现有结果提供了更大的开关信号可行范围。最后通过数值算例说明了所得结果的有效性和优越性。

引用次数: 0

Image classification and object detection complexity optimization: Exploring deep learning models on camera trap and surveillance clips 图像分类和目标检测复杂性优化：探索摄像机陷阱和监控片段的深度学习模型

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2026-01-03 DOI: 10.1016/j.rico.2026.100654

Hayder Yousif , Zahraa Al-Milaji

Input image size for convolutional neural networks (CNNs) has played a major role in classification accuracy and network speed. Designing a large depth, scale, and resolution CNN model cannot guarantee the best performance because of the problems of overfitting and memorization. On the other hand, object detection models have produced very low performance on event-triggered camera-trap images due to highly dynamic scenes. In this paper, we propose a framework for optimizing image classification in terms of performance and complexity by selecting the convenient deep learning model for each image. Based on the image sequence activation maps, we propose Resolution Selection Model (RSM) that generates a weight value for each image in the sequence. We utilize support vector machine (SVM) and the generated weight from RSM to select the appropriate deep learning model. We utilized EfficientNet models that have different input image resolutions to classify and detect the objects from the scaled images. Our results on camera-trap and surveillance images show the efficacy of the proposed method compared to the state-of-the-art architectures in terms of accuracy and computational complexity.

卷积神经网络（cnn）的输入图像大小对分类精度和网络速度起着重要作用。设计一个大深度、大尺度、大分辨率的CNN模型，由于存在过拟合和记忆问题，无法保证最佳的性能。另一方面，由于高度动态的场景，物体检测模型在事件触发的相机陷阱图像上产生了非常低的性能。在本文中，我们提出了一个框架，通过为每个图像选择方便的深度学习模型，从性能和复杂性方面优化图像分类。基于图像序列激活映射，我们提出了分辨率选择模型（RSM），该模型为序列中的每个图像生成一个权重值。我们利用支持向量机（SVM）和RSM生成的权值来选择合适的深度学习模型。我们利用具有不同输入图像分辨率的effentnet模型从缩放图像中对目标进行分类和检测。我们在摄像机陷阱和监控图像上的结果表明，与最先进的架构相比，所提出的方法在准确性和计算复杂性方面是有效的。

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引用次数: 0

Recursive feasibility without terminal constraints via parent–child MPC architecture

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-30 DOI: 10.1016/j.rico.2025.100653

Filip Surma, Anahita Jamshidnejad

This paper introduces a novel hierarchical model predictive control (MPC) framework, called the Parent–Child MPC architecture, designed to ensure recursive feasibility without relying on terminal constraints. The proposed architecture targets nonlinear constrained systems with Lipschitz continuous dynamics, such as quadrotors, helicopters and autonomous bicycles. For such systems, traditional MPC approaches may suffer from computational intractability or conservativeness due to needing terminal constraints. The proposed framework couples a small-horizon, high-fidelity Child MPC with one or more large-horizon, simplified Parent MPC layers. The Parent layers provide robust invariant tubes that replace terminal constraints, enabling scalable planning and stability guarantees. Two case studies, including a linear double integrator system and a nonlinear system, demonstrate the effectiveness of the architecture. Compared to standard robust tube-based MPC, the Parent–Child MPC achieves up to an eight-fold reduction in solver time and a three-fold increase in controllable prediction horizon. It also maintains performance within 3% of robust tube-based MPC. These results highlight the potential of this architecture for real-time control of complex, nonlinear systems under uncertainty.

本文介绍了一种新的层次模型预测控制（MPC）框架，称为亲子MPC架构，旨在确保递归的可行性，而不依赖于终端约束。所提出的架构针对具有Lipschitz连续动力学的非线性约束系统，例如四旋翼机，直升机和自动自行车。对于这样的系统，由于需要终端约束，传统的MPC方法可能存在计算难解性或保守性。提出的框架将一个小视界、高保真的子MPC与一个或多个大视界、简化的父MPC层耦合在一起。父层提供健壮的不变管道，取代终端约束，支持可伸缩的规划和稳定性保证。两个实例研究，包括一个线性双积分系统和一个非线性系统，证明了该架构的有效性。与标准的基于坚固管的MPC相比，亲子MPC的求解时间减少了8倍，可控预测范围增加了3倍。它还将性能保持在坚固的管基MPC的3%以内。这些结果突出了该体系结构在不确定情况下对复杂非线性系统进行实时控制的潜力。

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引用次数: 0

A novel water bi-objective optimization in agricultural supply chains using Jackson Queue Network

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-28 DOI: 10.1016/j.rico.2025.100652

Sepideh Hemmatian Ashrafian, Vahid Baradaran, Hamid Esmaeeli

Inappropriate water use in agriculture is one of the main challenges in water resource management in any country. This situation indicates the need to improve irrigation methods and productivity. Saving water in agriculture can have high economic value and the importance of this issue is essential in maintaining food security and preventing water crises. The main goal of current study is to optimize water consumption in the agricultural sector. For this purpose, an innovative model of the agricultural supply chain (SC) under conditions of supply and demand uncertainty is designed to simultaneously balance two conflicting goals: reducing the total SC costs and reducing water consumption. By optimizing the water supply chain and reducing costs, farmers can achieve greater productivity. This means increased income and reduced economic risks in the agricultural sector. In addition, it will help identify the balance between costs and water consumption, allowing for not only cost reduction but also the conservation of water resources in the environment. A mathematical model of the agricultural supply chain is designed according to the Jackson Queuing Network. To control the non-deterministic parameters of demand and supply, the stable box method has been used. The model is analyzed using multi-objective decision making methods such as the Torabi-Hosseini (TH) method, Enhanced Epsilon Constraint (EPC) method, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the use of high-tech processing centers leads to a significant reduction in water consumption, albeit at the cost of increased total SC expenses. As uncertainty in supply and demand rises, customer demand increases while agricultural material supply declines, prompting the expansion of processing and distribution centers. Furthermore, increasing the stability factor of the model improves water efficiency and demand fulfillment but leads to higher overall costs. Balancing sustainability and cost-efficiency in agricultural supply chain requires managing uncertainty through advanced modeling and technology investment.

农业用水不当是任何国家水资源管理的主要挑战之一。这种情况表明需要改进灌溉方法和提高生产力。农业节水具有很高的经济价值，这一问题的重要性对维持粮食安全和防止水危机至关重要。本研究的主要目标是优化农业部门的用水。为此，设计了供需不确定性条件下的农业供应链创新模型，以同时平衡两个相互冲突的目标：降低供应链总成本和减少用水量。通过优化水供应链和降低成本，农民可以获得更高的生产力。这意味着农业部门的收入增加和经济风险降低。此外，它将有助于确定成本和水消耗之间的平衡，不仅可以降低成本，而且还可以保护环境中的水资源。基于杰克逊排队网络，建立了农业供应链的数学模型。为了控制需求和供给的不确定性参数，采用了稳定箱法。采用Torabi-Hosseini （TH）法、增强型Epsilon约束（EPC）法和非支配排序遗传算法II （NSGA-II）等多目标决策方法对模型进行分析。研究结果表明，高科技加工中心的使用导致了用水量的显著减少，尽管其代价是SC总费用的增加。随着供需不确定性的增加，客户需求增加，农资供应减少，促使加工配送中心的扩张。此外，增加模型的稳定系数可以提高用水效率和满足需求，但会导致更高的总成本。平衡农业供应链的可持续性和成本效益需要通过先进的建模和技术投资来管理不确定性。

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引用次数: 0

Stability and optimizing the treatment control of tuberculosis model via numerical approach 用数值方法研究结核模型的稳定性和治疗控制的优化

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-26 DOI: 10.1016/j.rico.2025.100650

Muhammad Farman , David Amilo , Manal Ghannam , Kottakkaran Sooppy Nisar , Mohamed Hafez

According to World Health Organization data, tuberculosis (TB) affects nearly one-third of the world’s population and causes several million deaths and new cases each year. Recent advances in fractal–fractional differential operators have proven effective in simulating complex real-world problems. In this study, we present a TB model with an emphasis on hospital treatment and public health education, using a fractal–fractional operator under the Mittag-Leffler function. The study focuses on biological feasibility elements such as unique solutions, existence, positivity, and feasible domains. The Lipschitz and growth conditions are used to demonstrate the existence and uniqueness of solutions to the proposed TB system. A next-generation matrix technique is used to calculate the effective reproductive number of tuberculosis to determine its spread. Suitable Lyapunov functionals are developed to demonstrate the global stability of both TB-free and endemic equilibria. Each model parameter’s impact on the effective reproductive number is assessed using a normalized sensitivity index calculation. A numerical iterative method with Newton polynomial interpolation is utilized to demonstrate the usefulness of the proposed model, and numerical simulations show that it is more efficient at various fractional orders. We looked at numerical data from a variety of factors and fractional order values, concentrating on their impact on disease eradication. The simulation results are compared between the Newton polynomial interpolation approach and the fractional Adams–Bashforth–Moulton predictor–corrector method for the model compartments. The fractal–fractional approach essentially combines the complex real-world dynamics of infectious diseases with theoretical mathematics. This approach offers deep insights that help improve public health decision-making and guide successful control measures.

根据世界卫生组织的数据，结核病（TB）影响着世界近三分之一的人口，每年造成数百万人死亡和新病例。分形-分数阶微分算子的最新进展已被证明在模拟复杂的现实问题方面是有效的。在这项研究中，我们提出了一个结核病模型，重点是医院治疗和公共卫生教育，在Mittag-Leffler函数下使用分形-分数算子。研究的重点是生物可行性要素，如唯一解、存在性、正性和可行域。利用Lipschitz条件和生长条件证明了所提出的TB系统解的存在性和唯一性。采用新一代基质技术计算结核的有效繁殖数，以确定其传播。开发了合适的Lyapunov泛函来证明无结核病和地方性平衡的全局稳定性。每个模型参数对有效繁殖数的影响采用归一化敏感性指数计算进行评估。利用牛顿多项式插值的数值迭代方法验证了该模型的有效性，数值仿真结果表明，该模型在不同分数阶上都具有较高的效率。我们查看了来自各种因素和分数阶值的数值数据，重点关注它们对疾病根除的影响。比较了牛顿多项式插值法和分数阶Adams-Bashforth-Moulton预测校正法对模型室的模拟结果。分形-分数方法本质上是将复杂的真实世界传染病动力学与理论数学相结合。这种方法提供了深刻的见解，有助于改进公共卫生决策并指导成功的控制措施。

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引用次数: 0

Optimal fractional order PI controller design for time-delayed processes 时滞过程的最优分数阶PI控制器设计

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-22 DOI: 10.1016/j.rico.2025.100651

Erdal Cokmez, Ibrahim Kaya

The usage of fractional calculus enables additional flexibility and precision regarding the control parameters. This study introduces a fully analytical design for a fractional-order Proportional-Integral (FOPI) controller, eliminating the need for predefined parameters or iterative optimization. The Åström recursive algorithm, previously applied to integer-order controllers, is adapted for the first time to optimize FOPI controllers based on integral of squared time error (ISTE), integral of squared time-squared error (IST²E), and integral of squared time-cubed error (IST³E) performance criteria. This study differs from others since instead of specific types, it combines three types of first-order plus time-delay processes: stable (SFOPTD), integrating (IFOPTD), and unstable (UFOPTD) processes. Analytical formulas have been derived for optimal parameter selection, while separate formulas provided for gain margin (GM), phase margin (PM), and maximum sensitivity (Ms) enable the pre-determination of system robustness. The controller's performance is validated using simulations of the step response, disturbance rejection, control effort, and perturbation response. In addition, real-time experiments on an inverted pendulum illustrate its utility in dynamic processes. This provides a comprehensive framework aimed to further the development of fractional-order control by providing a systematic solution to a large scope of industrial applications.

分数阶微积分的使用使控制参数具有额外的灵活性和精度。本研究介绍了分数阶比例积分（FOPI）控制器的完全解析设计，消除了预定义参数或迭代优化的需要。先前应用于整阶控制器的Åström递归算法首次适用于基于时间误差平方积分（ISTE）、时间误差平方积分（IST2E）和时间立方误差平方积分（IST3E）性能标准的FOPI控制器优化。本研究与其他研究的不同之处在于，它结合了三种一阶加时滞过程：稳定（SFOPTD）、整合（IFOPTD）和不稳定（UFOPTD）过程，而不是特定的类型。推导了最佳参数选择的解析公式，同时提供了增益裕度（GM），相位裕度（PM）和最大灵敏度（Ms）的单独公式，可以预先确定系统的鲁棒性。通过阶跃响应、扰动抑制、控制努力和扰动响应的仿真验证了控制器的性能。另外，在倒立摆上进行了实时实验，说明了该方法在动态过程中的应用。这提供了一个全面的框架，旨在通过为大范围的工业应用提供系统的解决方案，进一步发展分数阶控制。

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引用次数: 0

An innovative two-stage method for balancing time, cost, quality, and additional objectives in multi-mode resource-constrained project scheduling: A case study of a food product packaging production line project 在多模式资源约束项目调度中平衡时间、成本、质量和附加目标的创新两阶段方法：以食品包装生产线项目为例研究

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-19 DOI: 10.1016/j.rico.2025.100649

Hossein Edrisi, Meghdad Jahromi, Narges Norouzi

This paper presents a novel two-stage approach for optimizing the scheduling of multi-objective construction projects, validated through a real-world case study of a food product packaging production line. Beyond the conventional objectives of time, cost, and quality, five additional factors—risk, scope creep, environmental impacts, stakeholder satisfaction, and safety—are quantitatively incorporated based on expert judgment and normalized performance ratings. In the first stage, a TOPSIS-based similarity index is calculated for each execution mode of the project activities, aggregating the additional objectives into a single comparable criterion derived from the weighted expert assessments. In the second stage, a four-objective optimization model is formulated to balance project makespan, cost, quality, and the similarity index. Integrating the similarity index ensures simultaneous consideration of both traditional and supplementary objectives. The multi-mode resource-constrained project scheduling problem (MRCPSP) is solved on small instances using the epsilon-constraint method, and for medium- and large-scale problems, two metaheuristics—NSGA-II and MOPSO—are employed. Computational results and sensitivity analyses conducted on the case study demonstrate the model’s capability to produce a diverse set of high-quality Pareto-optimal solutions within reasonable computational timeframes. The proposed framework’s flexibility makes it suitable for projects with varying objective priorities, offering a comprehensive and practical decision-support tool for project managers aiming for more balanced and precise scheduling decisions.

本文提出了一种新的两阶段方法来优化多目标建设项目的调度，并通过食品包装生产线的实际案例研究进行了验证。除了时间、成本和质量的传统目标之外，还有五个额外的因素——风险、范围蔓延、环境影响、利益相关者满意度和安全性——基于专家判断和标准化的绩效评级，被定量地纳入其中。在第一阶段，为项目活动的每一种执行模式计算基于topsis的相似性指数，将额外的目标汇总到从加权专家评估得出的单一可比标准中。在第二阶段，建立了一个四目标优化模型，以平衡项目完工时间、成本、质量和相似度指数。整合相似度指数可确保同时考虑传统目标和补充目标。采用epsilon约束方法求解小实例上的多模式资源约束项目调度问题，采用nsga - ii和mopso两种元启发式方法求解中大规模问题。对案例研究进行的计算结果和敏感性分析表明，该模型能够在合理的计算时间框架内产生各种高质量的帕累托最优解。所建议的框架的灵活性使其适合具有不同目标优先级的项目，为项目经理提供全面和实用的决策支持工具，以实现更平衡和精确的调度决策。

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引用次数: 0