Results in Control and Optimization最新文献

{"title":"Optimal control and cost-effectiveness analysis of socio-nutritional and behavioral interventions for Tuberculosis management in Ghana","authors":"Fredrick A. Wireko ,&nbsp;Mary Amoako ,&nbsp;Rebecca Awerigiya ,&nbsp;Joshua Kiddy K. Asamoah ,&nbsp;Isaac K. Adu ,&nbsp;Lord Jephthah J. Gowans ,&nbsp;Emmanuel K. Owusu ,&nbsp;Nana K. Ayisi-Boateng","doi":"10.1016/j.rico.2026.100743","DOIUrl":"10.1016/j.rico.2026.100743","url":null,"abstract":"<div><div>Tuberculosis is classified as one of the deadliest diseases across the globe, which disproportionately affects developing countries. Notwithstanding the remarkable biomedical improvements, the control of Tuberculosis has been impeded by the interplay of structural vulnerabilities, poor nutrition, and adverse behavioral conditions. In this study, we develop an SLIR model that incorporates an empirically derived index of dietary risk, food insecurity, poor oral health, and psychological distress to assess their association with the transmission, reactivation, and recovery dynamics of Tuberculosis in Ghana. Through a sensitivity analysis, the study outlines the critical role of both biological and socio-behavioral parameters on the basic reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. Using WHO-reported Tuberculosis incidence data from 2012 to 2023, the estimated <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> was obtained as 2.6556. It was revealed that the disease progression rate, transmission rate, and nutritional and behavioral parameters were key drivers of Tuberculosis dynamics. We then formulated an optimal control framework by incorporating time-dependent control strategies targeting nutritional support, oral health promotion, mental health services, and treatment adherence. Further numerical simulations show that combined socio-nutritional and behavioral interventions substantially reduce Tuberculosis prevalence and total infections, with the most effective strategies involving multi-pronged approaches sustained at high intensity in the early stages. The cost-effectiveness analysis of the model provides policy-relevant insights for designing multi-sector tuberculosis intervention programs in resource-constrained countries like Ghana.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100743"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective optimization model for project scheduling and material procurement incorporating Monte Carlo cost estimation and ESG-oriented supplier selection","authors":"Mir Mostafa Majidi ,&nbsp;Ali Heidari ,&nbsp;Sasan Mazaheri ,&nbsp;Fariborz Jolai","doi":"10.1016/j.rico.2026.100740","DOIUrl":"10.1016/j.rico.2026.100740","url":null,"abstract":"<div><div>This study presents an integrated multi-objective optimization framework combining project scheduling and material procurement in construction management. The model jointly determines activity start times, material ordering and purchasing, supplier selection, inventory levels, and transportation planning using heterogeneous vehicles over a discrete horizon. Three conflicting objectives are considered: minimizing total project cost and project make-span, and maximizing suppliers’ Environmental, Social, and Governance (ESG) performance. Cost-related parameters are expressed as triangular fuzzy numbers, enabling robust planning under uncertainty. The model is solved using the Augmented Epsilon Constraint method to obtain Pareto-optimal trade-offs among objectives. A comprehensive two-span bridge construction case study demonstrates practical insights into balancing cost, time, and sustainability. Results indicate that increasing the total cost improves both make-span and ESG outcomes but with diminishing marginal benefits. Monte Carlo simulation confirms the optimal fuzzy cost as the median of its probabilistic distribution with roughly 50% confidence. The LP-Metric approach further examines how varying objective weights and supplier capacity constraints affect selection patterns. When capacity limits are absent, suppliers with higher ESG scores and shorter lead times are favored; under capacity restrictions, selection becomes stability-driven, focusing on suppliers capable of meeting required volumes. Sensitivity analyses highlight that lead time strongly influences make-span, whereas warehouse capacity affects ordering cost only up to an economic threshold, and vehicle-type diversification enhances transport efficiency. The proposed fuzzy multi-objective framework provides a comprehensive and realistic basis for sustainable, time-efficient, and cost-aware decision-making in construction projects.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100740"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An artificial neural network approach for a single parameter boundary value problem with singular perturbation","authors":"Ganesh Kumar Vadla ,&nbsp;Jithender Reddy Gurejala ,&nbsp;Pothanna Nalimela ,&nbsp;Raju Adigoppula ,&nbsp;Swarnakar Dornala","doi":"10.1016/j.rico.2026.100735","DOIUrl":"10.1016/j.rico.2026.100735","url":null,"abstract":"<div><div>This article explores the artificial neural networks(ANN) approach in solving a one parameter singular perturbation boundary value problem. Solution behaviour of a singular perturbation convection-diffusion boundary value problem through a small perturbation restriction is analysed using neural networks. Feed-forward neural networks(FFNN) computations are utilised to determine and investigate the behaviour of proposed solution. A class of neural network is employed for the test functions and the adams approximation optimization system is used to estimate the adjustable factors. Three computational models are conducted to exhibit the efficacy of the technique, and its maximum magnitude errors are matched with those of other approaches in the works in order to validate the approach. Visualized the result profiles for both artificial neural network and analytical methods. The high R-Squared values were attained for prediction. The results imply that artificial neural networks offer an adaptable and efficient strategy for certain classes of singular perturbation boundary value problems, supporting further study and practical use.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100735"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal hierarchical online energy management of hydrogen PEM fuel cell hybrid electric vehicles with solar energy assisted charging","authors":"Kamel Djermouni ,&nbsp;Ali Berboucha ,&nbsp;Salah Tamalouzt ,&nbsp;Karim Fathi Sayeh ,&nbsp;Youcef Belkhier ,&nbsp;Mohamed Benbouzid","doi":"10.1016/j.rico.2026.100753","DOIUrl":"10.1016/j.rico.2026.100753","url":null,"abstract":"<div><div>Global carbon dioxide (CO₂) emissions are significantly driven by the transport sector, underscoring the critical need for clean, alternative energy solutions. However, the decarbonization of Emergency Medical Services (EMS) faces unique challenges, such as highly stochastic power demands and aggressive driving profiles that lead to rapid fuel cell (FC) degradation and inefficient energy distribution under extreme operational stress. While recent techniques like standard Fuzzy Logic or Rule-Based strategies offer basic functionality, they often fail to ensure long-term robustness and optimal multi-source synchronization. To address these gaps, this paper introduces an online optimization-based energy management strategy for a four-source hybrid energy system (FC, lithium-ion batteries, super-capacitors, and photovoltaics). The work details a comprehensive sizing methodology and a novel two-stage control architecture, integrating real-time fuzzy logic controllers with deterministic optimization methods. This system enhances energy flow supervision across diverse driving conditions, including extreme environmental stressors such as high wind speeds and severe road gradients. Simulations conducted on a hybrid ambulance model (RENAULT MASTER) confirm the proposed strategy's effectiveness. Results demonstrate substantial reductions in hydrogen consumption (∼10%), improved regenerative recovery, and increased operational stability. This holistic approach not only ensures stable operation under severe emergency missions but also contributes to an improvement in the powertrain lifespan by mitigating electrochemical stress on the fuel cell and storage devices.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100753"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised learning of iterative solvers for constrained optimization","authors":"Lukas Lüken,&nbsp;Sergio Lucia","doi":"10.1016/j.rico.2026.100751","DOIUrl":"10.1016/j.rico.2026.100751","url":null,"abstract":"<div><div>The real-time solution of parametric optimization problems is critical for applications that demand high accuracy under tight real-time constraints, such as model predictive control. To this end, this work presents a learning-based iterative solver for constrained optimization, comprising a neural network predictor that generates initial primal–dual solution estimates, followed by a learned iterative solver that refines these estimates to reach high accuracy.</div><div>We introduce a novel loss function based on Karush–Kuhn–Tucker (KKT) optimality conditions, enabling fully self-supervised training without pre-solved optimizer solutions. Theoretical guarantees ensure that the training loss function attains minima exclusively at KKT points. A convexification procedure enables application to nonconvex problems while preserving these guarantees.</div><div>Experiments on two nonconvex case studies demonstrate speedups of up to one order of magnitude compared to state-of-the-art solvers such as IPOPT, while achieving orders of magnitude higher accuracy than competing learning-based approaches.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100751"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-switch three-phase inverter fed IM drives under predictive control strategies: A comparative study","authors":"Wiem Zouari,&nbsp;Bassem El Badsi,&nbsp;Ahmed Masmoudi","doi":"10.1016/j.rico.2026.100739","DOIUrl":"10.1016/j.rico.2026.100739","url":null,"abstract":"<div><div>In recent years, the progress of power electronics converters and microprocessors has enabled the implementation of advanced control strategies in AC machine drives. This article deals with a comparative study of the performance of the four-switch three-phase inverter (B4) fed induction motor (IM) drive under predictive torque and flux control strategies (PTC and PFC). The B4 inverter is the reduced structure of the fault-tolerant six-switch inverter (B6) following a fault affecting one leg. First, the IM drive performance under different values of the flux weighting factor of the PTC strategy is investigated. Then, the reliability of the drive following the reconfiguration from B6 to post-fault-tolerant B4 inverter is analyzed, without structural modification of the control laws. The comparative study focuses on the investigation of the steady-state behavior of the drive under two PTC strategies that differ by their weighting factors and the PFC, with emphasis on three criteria namely: (i) the average switching frequency, (ii) the average total harmonic distortion, and (iii) the average switching loss factor. Moreover, the robustness of the drive against resistances variation and against load torque perturbation is tested by simulations. An experimental validation of the steady-state behavior is also carried out using the dSPACE DS1104 platform.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100739"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A delayed fractional-order model of Ebola virus disease with human behavioral dynamics","authors":"Eva Lusekelo ,&nbsp;Mlyashimbi Helikumi ,&nbsp;Salamida Daudi ,&nbsp;Steady Mushayabasa","doi":"10.1016/j.rico.2026.100738","DOIUrl":"10.1016/j.rico.2026.100738","url":null,"abstract":"<div><div>This study presents a fractional-order mathematical model to examine the transmission dynamics of Ebola Virus Disease, explicitly incorporating incubation delays, human behavioral responses, and socio-cultural funeral practices. Caputo fractional derivatives are employed to represent memory effects, capturing the complex temporal evolution of outbreaks. Conditions for the local stability of disease-free and endemic equilibria are derived. Numerical bifurcation analysis indicates that increasing the incubation delay <span><math><mi>τ</mi></math></span> beyond a critical threshold <span><math><mrow><msup><mrow><mi>τ</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>≈</mo><mn>2</mn><mo>.</mo><mn>5</mn></mrow></math></span> days induces a Hopf bifurcation, resulting in sustained oscillations in infection levels. Additionally, increasing <span><math><mi>τ</mi></math></span> from 1 to 3 days delays the epidemic peak by approximately 10 days and elevates peak infections from 1200 to over 10,000 cases. The model further demonstrates that higher proportions (<span><math><mrow><msub><mrow><mi>p</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>4</mn></mrow></math></span>) of unmonitored deaths due to socio-cultural burial practices increase the outbreak size by 35%, highlighting the importance of cultural interventions. The fractional order <span><math><mi>q</mi></math></span> influences oscillatory behaviors; as <span><math><mi>q</mi></math></span> approaches 1, peak infection levels rise by 20%. These findings emphasize the significance of integrating cultural, behavioral, and delay effects into Ebola control strategies.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100738"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction notice to “Measurement-based Characterization of an RF Transmitter to Offset the Effects of Nonlinearities” [RICO (2025) 100521]","authors":"Fadia Noori Hummadi,&nbsp;Ekhlas Kadhum Hamza,&nbsp;Ali M.J. Zalzala,&nbsp;Ahmad H. Sabry","doi":"10.1016/j.rico.2026.100700","DOIUrl":"10.1016/j.rico.2026.100700","url":null,"abstract":"","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100700"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling norovirus transmission dynamics with environmental reservoirs, reinfection, and optimal control strategies","authors":"Gideon K. Gogovi ,&nbsp;Oyu-Erdene Ankhbayar ,&nbsp;Ling Qin","doi":"10.1016/j.rico.2026.100746","DOIUrl":"10.1016/j.rico.2026.100746","url":null,"abstract":"<div><div>Norovirus remains a leading cause of acute gastroenteritis worldwide, imposing substantial public health and economic burdens. We developed a compartmental model incorporating environmental viral reservoirs, waning immunity, and reinfection dynamics to characterize transmission and evaluate intervention strategies. Mathematical analysis established global stability of the disease-free and endemic equilibria via Lyapunov functions, and sensitivity analysis identified the environmental transmission rate, viral shedding rate, and recruitment rate as dominant drivers of the control reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> (unit sensitivity indices, PRCC <span><math><mrow><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>79</mn></mrow></math></span>). An optimal control problem incorporating three time-dependent interventions (symptomatic case isolation <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>, environmental sanitation <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, and hand hygiene <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span>) was formulated and solved via Pontryagin’s Maximum Principle and forward–backward sweep algorithms. Maximum-intensity interventions during the first week achieved optimal suppression, reducing susceptible depletion 15-fold and cumulative symptomatic burden by 85%. Cost-effectiveness analysis identified the triple-intervention strategy as optimal, achieving a 90.2% cost reduction from the uncontrolled baseline (ACER <span><math><mrow><mo>=</mo><mn>0</mn><mo>.</mo><mn>17</mn></mrow></math></span>, ICER <span><math><mrow><mo>=</mo><mo>−</mo><mn>9</mn><mo>.</mo><mn>90</mn></mrow></math></span>) and averting 90.82% of infections. Hand hygiene alone was the dominant single intervention (90.56% infection reduction, ACER <span><math><mrow><mo>=</mo><mn>0</mn><mo>.</mo><mn>21</mn></mrow></math></span>), far outperforming the dominated strategies of isolation alone (4.30%, ACER <span><math><mrow><mo>=</mo><mn>31</mn><mo>.</mo><mn>09</mn></mrow></math></span>) and disinfection alone (0.22%, ACER <span><math><mrow><mo>=</mo><mn>642</mn><mo>.</mo><mn>26</mn></mrow></math></span>). These results provide quantitative evidence for prioritizing hand hygiene and environmental decontamination as the cornerstones of norovirus outbreak control in institutional settings.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100746"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chance constrained nonlinear model predictive control for multiple systems with shared resources","authors":"Ineza Remy Mugenga ,&nbsp;Abebe Geletu ,&nbsp;Silas Mirau ,&nbsp;Pu Li","doi":"10.1016/j.rico.2026.100747","DOIUrl":"10.1016/j.rico.2026.100747","url":null,"abstract":"<div><div>This paper proposes chance constrained nonlinear model predictive control (CCNMPC) for multiple interconnected systems sharing limited resources under uncertainty. Each subsystem with uncertainties has its local objectives and constraints and is coupled to the other subsystems through the shared resource. The shared resource limitation is satisfied by a global chance constraint which is decomposed into individual local chance constraints via the Bonferroni inequality, enabling a distributed solution approach. The chance constrained problem is then transformed into a sequence of smooth deterministic problems through the inner–outer approximation method, in which the smoothing parameter is shown to act as an implicit constraint-tightening mechanism that recovers the classical Gaussian tube-MPC back-off in the zero-smoothing limit. Within this framework, we prove probabilistic one-step recursive feasibility under unbounded disturbances. The resulting distributed nonconvex problem is solved using an adapted Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN) algorithm. The framework is validated on a shared battery energy storage system serving multiple microgrids with non-Gaussian demand forecast errors over a 24-h horizon: the empirical violation rates remain below the prescribed 5% threshold and the terminal invariance condition is satisfied with substantial margin throughout.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100747"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}