Pub Date : 2025-10-22DOI: 10.1016/j.rico.2025.100625
Salwa Yahia , Saida Bedoui , Kamel Abderrahim
This paper introduces a new approach to designing fault-tolerant control systems for stochastic switched system. The main objective is to address actuator faults and unanticipated shifts in the system’s operational mode. The proposed method uses a K-Nearest Neighbors (KNN) algorithm to quickly determine the system’s current mode. It also develops an augmented observer that can simultaneously estimate the system’s current state and any actuator faults. The novel proposed fault-tolerant control (FTC) strategy is designed to be robust and reliable. Using Linear Matrix Inequalities (LMIs) and Lyapunov stability analysis alongside an objective, it guarantees strong performance. The effectiveness of this integrated approach are thoroughly substantiated through extensive numerical simulations, with a detailed case study demonstrating its practical application in two distinct scenarios: a two-pendulum system and a vehicle rollover prevention system.
{"title":"Lyapunov-based fault-tolerant control of stochastic switched systems via K-nearest neighbors switching detection and joint fault estimation","authors":"Salwa Yahia , Saida Bedoui , Kamel Abderrahim","doi":"10.1016/j.rico.2025.100625","DOIUrl":"10.1016/j.rico.2025.100625","url":null,"abstract":"<div><div>This paper introduces a new approach to designing fault-tolerant <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> control systems for stochastic switched system. The main objective is to address actuator faults and unanticipated shifts in the system’s operational mode. The proposed method uses a K-Nearest Neighbors (KNN) algorithm to quickly determine the system’s current mode. It also develops an augmented observer that can simultaneously estimate the system’s current state and any actuator faults. The novel proposed fault-tolerant control (FTC) strategy is designed to be robust and reliable. Using Linear Matrix Inequalities (LMIs) and Lyapunov stability analysis alongside an <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> objective, it guarantees strong performance. The effectiveness of this integrated approach are thoroughly substantiated through extensive numerical simulations, with a detailed case study demonstrating its practical application in two distinct scenarios: a two-pendulum system and a vehicle rollover prevention system.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100625"},"PeriodicalIF":3.2,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417188","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}
Pub Date : 2025-10-20DOI: 10.1016/j.rico.2025.100620
Chun-Wei Lan , Chien-Ming Lee , Kai-Yang Tung , Rongshun Chen
Each server in data centers is equipped with multiple fans and heat-generating components. Factors such as fans speed, layout of components, and loading scenarios, will influence the airflow and thermal flow fields within a server. Hence, a server thermal control is classified as a Multi-Input Multi-Output (MIMO) nonlinear control system, which cannot be analyzed and controlled using simple linear system method. By using the weighting mechanism of different weighting values for each cooling fan to asynchronously modulate multiple fans, and combining the Evolutionary Strategy (ES) algorithm with designing fitness functions, this study realizes the multi-fan thermal control system for a server. The developed system can attain approximately optimal weightings within a limited number of searches, based on different loading scenarios. By modulating fans asynchronously with these weightings, the system can achieve approximately optimal energy-saving while still meet the thermal specifications in a server; that is the allowed highest temperature of CPU and PCIe. Compared to modulating fans synchronously, the multi-fan control system modulating fans asynchronously saves an average of 43.1% of the total fans power, and only need to search 14.2% of all weighting options under the eight designed loading scenarios for experiments. Furthermore, the probability of each approximate optimal weighting corresponding to global optimal solution is 47.5%. Experimental results demonstrate that the pro- posed asynchronously modulating multi-fan control system can simultaneously satisfy the thermal specifications and achieves approximately optimal energy-saving for a server. As a result, the developed system is feasible with excellent performance for significant energy saving, while it is no need to construct a mathematical thermal models or to analyze numerous datasets. In the future, by adjusting the code parameters of system, it may be to be applied to various types of servers.
{"title":"Energy-saving optimization of server multi-fan control system based on weighting mechanism and evolution strategy","authors":"Chun-Wei Lan , Chien-Ming Lee , Kai-Yang Tung , Rongshun Chen","doi":"10.1016/j.rico.2025.100620","DOIUrl":"10.1016/j.rico.2025.100620","url":null,"abstract":"<div><div>Each server in data centers is equipped with multiple fans and heat-generating components. Factors such as fans speed, layout of components, and loading scenarios, will influence the airflow and thermal flow fields within a server. Hence, a server thermal control is classified as a Multi-Input Multi-Output (MIMO) nonlinear control system, which cannot be analyzed and controlled using simple linear system method. By using the weighting mechanism of different weighting values for each cooling fan to asynchronously modulate multiple fans, and combining the Evolutionary Strategy (ES) algorithm with designing fitness functions, this study realizes the multi-fan thermal control system for a server. The developed system can attain approximately optimal weightings within a limited number of searches, based on different loading scenarios. By modulating fans asynchronously with these weightings, the system can achieve approximately optimal energy-saving while still meet the thermal specifications in a server; that is the allowed highest temperature of CPU and PCIe. Compared to modulating fans synchronously, the multi-fan control system modulating fans asynchronously saves an average of 43.1% of the total fans power, and only need to search 14.2% of all weighting options under the eight designed loading scenarios for experiments. Furthermore, the probability of each approximate optimal weighting corresponding to global optimal solution is 47.5%. Experimental results demonstrate that the pro- posed asynchronously modulating multi-fan control system can simultaneously satisfy the thermal specifications and achieves approximately optimal energy-saving for a server. As a result, the developed system is feasible with excellent performance for significant energy saving, while it is no need to construct a mathematical thermal models or to analyze numerous datasets. In the future, by adjusting the code parameters of system, it may be to be applied to various types of servers.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100620"},"PeriodicalIF":3.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362461","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}
Pub Date : 2025-10-16DOI: 10.1016/j.rico.2025.100621
Sanju Sardar, Satyajit Mukherjee, Priti Kumar Roy
Methanol poisoning is a major and highly concerning public health issue worldwide, particularly in developing and underdeveloped countries. Factors, such as inadequate regulations, lack of awareness about its dangers, and deeply rooted cultural traditions play a major role in the widespread consumption of adulterated alcohol. To address such challenges, we propose a four-dimensional mathematical model to analyze the impact of awareness interventions on reducing methanol toxicity and illicit alcohol consumption. Some basic properties of the model, such as non-negativity, boundedness, existence of equilibria, and their stability, have been analyzed. An optimal control system is developed by incorporating two control measures—awareness campaigns and anti-drinking medication to minimize illegal alcohol consumption and related management costs. We establish the existence of an optimal control pair and characterize it through Pontryagin’s minimum principle. To ensure robustness and understand parameter influence, Latin Hypercube Sampling (LHS) and Sobol sensitivity analysis are used for model validation and sensitivity assessment. We also perform a cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) to identify the most economically efficient intervention strategy. All analytical findings of the study are demonstrated and validated through numerical simulations.
{"title":"Controlling illicit alcohol consumption through awareness and disulfiram: A mathematical study","authors":"Sanju Sardar, Satyajit Mukherjee, Priti Kumar Roy","doi":"10.1016/j.rico.2025.100621","DOIUrl":"10.1016/j.rico.2025.100621","url":null,"abstract":"<div><div>Methanol poisoning is a major and highly concerning public health issue worldwide, particularly in developing and underdeveloped countries. Factors, such as inadequate regulations, lack of awareness about its dangers, and deeply rooted cultural traditions play a major role in the widespread consumption of adulterated alcohol. To address such challenges, we propose a four-dimensional mathematical model to analyze the impact of awareness interventions on reducing methanol toxicity and illicit alcohol consumption. Some basic properties of the model, such as non-negativity, boundedness, existence of equilibria, and their stability, have been analyzed. An optimal control system is developed by incorporating two control measures—awareness campaigns and anti-drinking medication to minimize illegal alcohol consumption and related management costs. We establish the existence of an optimal control pair and characterize it through Pontryagin’s minimum principle. To ensure robustness and understand parameter influence, Latin Hypercube Sampling (LHS) and Sobol sensitivity analysis are used for model validation and sensitivity assessment. We also perform a cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) to identify the most economically efficient intervention strategy. All analytical findings of the study are demonstrated and validated through numerical simulations.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100621"},"PeriodicalIF":3.2,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362462","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}
Multi-floor layout optimization for empty fruit bunch (EFB) biodiesel plants presents a large-scale computational challenge requiring simultaneous consideration of economics, safety, and structural constraints. This study formulates a Mixed-Integer Nonlinear Programming (MINLP) model for multi-floor plant layout that co-optimizes equipment materials, floor assignments, section placement, and passive protection under quantified safety. The model is implemented in the General Algebraic Modeling System (GAMS) and solved using a three-stage decomposition—(i) material selection and structural capacity, (ii) section-level multi-floor layout, and (iii) plant-wide section integration—because a monolithic MINLP exceeds academic license limits and fails to converge within a standard time budget, whereas the staged approach solves with tight optimality gaps (total CPU 22.97 min). In a 1,000 t/d EFB case (57 items, seven sections), the optimized design reduces total capital by 28.9 %, cuts land footprint by 51.97 %, and—under Dow’s Fire and Explosion Index (F&EI) separations with device crediting—lowers Maximum Probable Property Damage (MPPD) before protection by 18.8 % and after protection by 69.0 %. The decomposition strategy demonstrates that complex industrial layout problems with safety constraints can be solved efficiently using hierarchical approaches, providing a framework applicable to other multi-criteria facility design challenges in the chemical and energy sectors.
{"title":"Comprehensive plant layout optimization for empty fruit bunch biodiesel production: A multi-floor MINLP approach with safety integration","authors":"Somboon Sukpancharoen , Chayangkul Janta-in , Pakon Sakdee , Thongchai Rohitatisha Srinophakun","doi":"10.1016/j.rico.2025.100624","DOIUrl":"10.1016/j.rico.2025.100624","url":null,"abstract":"<div><div>Multi-floor layout optimization for empty fruit bunch (EFB) biodiesel plants presents a large-scale computational challenge requiring simultaneous consideration of economics, safety, and structural constraints. This study formulates a Mixed-Integer Nonlinear Programming (MINLP) model for multi-floor plant layout that co-optimizes equipment materials, floor assignments, section placement, and passive protection under quantified safety. The model is implemented in the General Algebraic Modeling System (GAMS) and solved using a three-stage decomposition—(i) material selection and structural capacity, (ii) section-level multi-floor layout, and (iii) plant-wide section integration—because a monolithic MINLP exceeds academic license limits and fails to converge within a standard time budget, whereas the staged approach solves with tight optimality gaps (total CPU 22.97 min). In a 1,000 t/d EFB case (57 items, seven sections), the optimized design reduces total capital by 28.9 %, cuts land footprint by 51.97 %, and—under Dow’s Fire and Explosion Index (F&EI) separations with device crediting—lowers Maximum Probable Property Damage (MPPD) before protection by 18.8 % and after protection by 69.0 %. The decomposition strategy demonstrates that complex industrial layout problems with safety constraints can be solved efficiently using hierarchical approaches, providing a framework applicable to other multi-criteria facility design challenges in the chemical and energy sectors.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100624"},"PeriodicalIF":3.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417189","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}
Pub Date : 2025-10-10DOI: 10.1016/j.rico.2025.100622
Faisal Yousafzai , Muhammad Danish Zia , Yuan Miao , Saleem Abdullah , Yiu-Yin Lee
Decision-making is a complex process influenced by multiple factors, including available data, expert knowledge, contextual variables, stakeholder preferences, and emotional considerations. Despite the numerous methodologies proposed in recent years, there remains a need for more effective and intelligent techniques that address uncertainty in nonlinear, sentiment-based decision-making environments. In this paper, we develop and apply advanced nonlinear fuzzy models using quadratic Diophantine fuzzy soft sets and quadratic Diophantine fuzzy cognitive maps, with a particular focus on patient well-being through medical sentiment analysis. We begin with the quadratic Diophantine fuzzy soft set method to enhance the assessment of patient conditions through the analysis of symptoms, providing a precise evaluation of health status by considering multiple key factors. Next, the quadratic Diophantine fuzzy cognitive map method is used to identify the main elements influencing patient satisfaction by analyzing reviews on depression medications. This analysis also incorporates VADER-based sentiment analysis, implemented in Python, along with correlation analysis to quantify sentiment polarity in patient feedback on depression treatments. Collectively, these methods introduce nonlinear fuzzy tools that enhance evaluations and satisfaction assessments for effective sentiment-based decision-making.
{"title":"Quadratic Diophantine fuzzy sentiment-based nonlinear decision-making for medical diagnostics through soft sets and cognitive maps","authors":"Faisal Yousafzai , Muhammad Danish Zia , Yuan Miao , Saleem Abdullah , Yiu-Yin Lee","doi":"10.1016/j.rico.2025.100622","DOIUrl":"10.1016/j.rico.2025.100622","url":null,"abstract":"<div><div>Decision-making is a complex process influenced by multiple factors, including available data, expert knowledge, contextual variables, stakeholder preferences, and emotional considerations. Despite the numerous methodologies proposed in recent years, there remains a need for more effective and intelligent techniques that address uncertainty in nonlinear, sentiment-based decision-making environments. In this paper, we develop and apply advanced nonlinear fuzzy models using quadratic Diophantine fuzzy soft sets and quadratic Diophantine fuzzy cognitive maps, with a particular focus on patient well-being through medical sentiment analysis. We begin with the quadratic Diophantine fuzzy soft set method to enhance the assessment of patient conditions through the analysis of symptoms, providing a precise evaluation of health status by considering multiple key factors. Next, the quadratic Diophantine fuzzy cognitive map method is used to identify the main elements influencing patient satisfaction by analyzing reviews on depression medications. This analysis also incorporates VADER-based sentiment analysis, implemented in Python, along with correlation analysis to quantify sentiment polarity in patient feedback on depression treatments. Collectively, these methods introduce nonlinear fuzzy tools that enhance evaluations and satisfaction assessments for effective sentiment-based decision-making.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100622"},"PeriodicalIF":3.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269090","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}
Pub Date : 2025-09-26DOI: 10.1016/j.rico.2025.100616
Siwei Liu, Qing-Shan Jia
With the continuous development of autonomous vehicles technology, the feasibility probability estimation of the sample paths has become a key requirement in the performance evaluation of its control policies and the policy optimization with chance constraints. Aiming at the defect that current autonomous driving testing methods generally rely on human prior knowledge for sampling allocation, this paper proposes a method that can allocate the number of samples according to the feasibility probability and state occurrence probability, and proves its optimality. In this paper, we first propose an optimal sampling times allocation method to minimize probabilistic estimation variance, which can obtain an acceleration effect that is reciprocal to the probability of occurrence of the most critical state. For the actual task requirement, we also propose algorithms with iterative estimation and low-fidelity models. The results from numerical experiments with two initial states and intelligent vehicle cornering cruise experiments under ten initial states demonstrate that our method can achieve the same prediction estimation error with fewer samples.
{"title":"An accelerated black-box sample paths feasibility probability estimation method for control policies of autonomous vehicles","authors":"Siwei Liu, Qing-Shan Jia","doi":"10.1016/j.rico.2025.100616","DOIUrl":"10.1016/j.rico.2025.100616","url":null,"abstract":"<div><div>With the continuous development of autonomous vehicles technology, the feasibility probability estimation of the sample paths has become a key requirement in the performance evaluation of its control policies and the policy optimization with chance constraints. Aiming at the defect that current autonomous driving testing methods generally rely on human prior knowledge for sampling allocation, this paper proposes a method that can allocate the number of samples according to the feasibility probability and state occurrence probability, and proves its optimality. In this paper, we first propose an optimal sampling times allocation method to minimize probabilistic estimation variance, which can obtain an acceleration effect that is reciprocal to the probability of occurrence of the most critical state. For the actual task requirement, we also propose algorithms with iterative estimation and low-fidelity models. The results from numerical experiments with two initial states and intelligent vehicle cornering cruise experiments under ten initial states demonstrate that our method can achieve the same prediction estimation error with fewer samples.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100616"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221912","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}
Interconnected power systems are increasingly vulnerable to parameter deviations—such as mechanical wear, blade loss, inertia degradation, or cyber-physical attacks—in turbine–governors, generators, and transmission lines. These deviations compromise stability and may lead to severe disturbances if not detected and isolated promptly. Conventional observer-based fault detection methods can identify anomalies but often fail to pinpoint the exact parameter responsible.
This paper proposes a robust Fault Detection and Isolation (FDI) framework capable of estimating and isolating key dynamic parameters, including turbine (Tt) and governor (Tg) time constants, inertia (H), damping (D), and tie-line synchronizing coefficients (Tij). The method integrates an H∞/H₂ observer with pole placement for disturbance attenuation and rapid residual generation, followed by an adaptive sliding mode estimator for parameter-specific isolation. This two-stage scheme enables precise differentiation between faults and noise, as well as between different types of parametric shifts.
Simulation studies on a multi-area load frequency control (LFC) system validate the accuracy and robustness of the proposed approach under diverse fault scenarios. Unlike conventional FDI techniques, the framework not only detects faults but also isolates their root causes, thereby providing actionable insights for operators and enhancing the resilience of modern interconnected power networks.
{"title":"Robust FDI for turbine-governor and network parameters in interconnected power systems via mixed H∞/pole placement observers","authors":"Chadi Nohra , Raymond Ghandour , Mahmoud Khaled , Rachid Outbib","doi":"10.1016/j.rico.2025.100619","DOIUrl":"10.1016/j.rico.2025.100619","url":null,"abstract":"<div><div>Interconnected power systems are increasingly vulnerable to parameter deviations—such as mechanical wear, blade loss, inertia degradation, or cyber-physical attacks—in turbine–governors, generators, and transmission lines. These deviations compromise stability and may lead to severe disturbances if not detected and isolated promptly. Conventional observer-based fault detection methods can identify anomalies but often fail to pinpoint the exact parameter responsible.</div><div>This paper proposes a robust Fault Detection and Isolation (FDI) framework capable of estimating and isolating key dynamic parameters, including turbine (Tt) and governor (Tg) time constants, inertia (H), damping (D), and tie-line synchronizing coefficients (Tij). The method integrates an H∞/H₂ observer with pole placement for disturbance attenuation and rapid residual generation, followed by an adaptive sliding mode estimator for parameter-specific isolation. This two-stage scheme enables precise differentiation between faults and noise, as well as between different types of parametric shifts.</div><div>Simulation studies on a multi-area load frequency control (LFC) system validate the accuracy and robustness of the proposed approach under diverse fault scenarios. Unlike conventional FDI techniques, the framework not only detects faults but also isolates their root causes, thereby providing actionable insights for operators and enhancing the resilience of modern interconnected power networks.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100619"},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221913","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}
This study aims to develop a multi-objective decision-making technique, called Simultaneous Evaluation of Criteria and Alternatives (SECA), to optimally rank green construction project contractors. The SECA approach is formulated as a multi-objective nonlinear programming problem comprising three objective functions: (1) maximizing the overall performance of alternatives, (2) minimizing the deviation of criteria weights from a reference point based on intra-criteria variation information, and (3) minimizing the deviation of criteria weights based on inter-criteria variation information, subject to two constraints on criteria weights. To implement the method, 16 key contractor prequalification criteria were first identified, followed by the selection of 10 qualified applicants. A decision matrix was constructed, and reference points were calculated. Variable weights for criteria and rankings of alternatives were obtained by coding the SECA method in Lingo software, considering different values of β. Determining the optimal β was a critical step, with values ranging from 0.1 to 7 evaluated. Results indicated that the maximum objective function value of 0.786 was achieved at β = 6, with the weight effect of the proposed price criterion being 0.0737. Contractor A3, with a score of 0.8887, was identified as the top-ranked contractor. Overall, the findings indicate that the SECA-based optimization method not only supports decision-makers in improving quality and reducing costs but also enhances transparency and trust in the selection process. By simultaneously evaluating criteria and alternatives and determining objective weights based on standard deviation and inter-criteria correlations, the method strengthens both transparency and reliability through reproducible and comparable analyses.
{"title":"Development of SECA multi criteria decision making method to optimally select contractors for green construction projects (Case study for Iran)","authors":"S․Ali Moayeripour , S․Mohammad Mirhosseini , Mohammad Ehsanifar , Ehsanollah Zeighami","doi":"10.1016/j.rico.2025.100617","DOIUrl":"10.1016/j.rico.2025.100617","url":null,"abstract":"<div><div>This study aims to develop a multi-objective decision-making technique, called Simultaneous Evaluation of Criteria and Alternatives (SECA), to optimally rank green construction project contractors. The SECA approach is formulated as a multi-objective nonlinear programming problem comprising three objective functions: (1) maximizing the overall performance of alternatives, (2) minimizing the deviation of criteria weights from a reference point based on intra-criteria variation information, and (3) minimizing the deviation of criteria weights based on inter-criteria variation information, subject to two constraints on criteria weights. To implement the method, 16 key contractor prequalification criteria were first identified, followed by the selection of 10 qualified applicants. A decision matrix was constructed, and reference points were calculated. Variable weights for criteria and rankings of alternatives were obtained by coding the SECA method in Lingo software, considering different values of β. Determining the optimal β was a critical step, with values ranging from 0.1 to 7 evaluated. Results indicated that the maximum objective function value of 0.786 was achieved at β = 6, with the weight effect of the proposed price criterion being 0.0737. Contractor A3, with a score of 0.8887, was identified as the top-ranked contractor. Overall, the findings indicate that the SECA-based optimization method not only supports decision-makers in improving quality and reducing costs but also enhances transparency and trust in the selection process. By simultaneously evaluating criteria and alternatives and determining objective weights based on standard deviation and inter-criteria correlations, the method strengthens both transparency and reliability through reproducible and comparable analyses.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100617"},"PeriodicalIF":3.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269089","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}
Pub Date : 2025-09-24DOI: 10.1016/j.rico.2025.100618
Kyvalya Garikapati , Sugyanta Priyadarshini , Nisrutha Dulla , Snigdharani Panda , Sumita Mishra , Jayalaxmi Samal
The study aims to provide an insight into the performance of Sukanya Samriddhi Yojana (SSY). The primary objective of the study is to identify and understand the frequent barriers obstructing implementation of SSY scheme in rural areas and to determine the interrelationships between these barriers. The analysis made use of Interpretive Structural Modelling (ISM) approach and MICMAC analysis through a combination of expert opinion on validation of barriers. First, the study has identified 225 published documents retrieved from google scholar (n=222), Scopus (n=2) and Web of Science (n=1) data base for reviewing the literature and identifying barriers. Finally, 51 documents fulfilled the objective in identifying 9 Sukanya Samriddhi Yojana Implementation Barriers (SSYIBs). Secondly, ISM Identifies “Lack of communication” with Dependence Power (DP:1) and strong Driving Power (DRP:9) is identified as the major barrier among the 9 validated barriers in the context of rural India in implementing SSY scheme. MICMAC analysis identifies 5 barriers (Perceived cognizance, Societal Stigma, Cultural Inhibition, Lack of educational infrastructure, and Gender discrimination) as linkage variables under Quadrant 3 with strong DP (9) and DRP (5). Further, 4 barriers (Lack of awareness, Lack of knowledge, Lack of financial literacy) as independent variables under Quadrant 4 pursue weak DP (4) and strong DRP (8). This study can possibly be beneficial for academic researchers and policy makers by overcoming the gaps by assembling evidence from literature and integrating the findings for a clear understanding of the matter.
这项研究的目的是提供一个洞察Sukanya Samriddhi Yojana （SSY）的表现。这项研究的主要目标是确定和了解阻碍在农村地区实施可持续发展计划的常见障碍，并确定这些障碍之间的相互关系。分析利用解释结构建模（ISM）方法和MICMAC分析，结合专家意见对障碍的验证。首先，从谷歌scholar （n=222）、Scopus （n=2）和Web of Science （n=1）数据库中检索225篇已发表的文献，进行文献综述和障碍识别。最后，51份文件实现了确定9个“苏坎亚Samriddhi Yojana”实施障碍（ssyib）的目标。其次，ISM认为“缺乏沟通”与依赖力（DP:1）和强驱动力（DRP:9）是印度农村实施SSY计划的9个有效障碍中的主要障碍。MICMAC分析确定了5个障碍（感知认知、社会污名、文化抑制、缺乏教育基础设施和性别歧视）作为象限3下具有强DP(9)和DRP(5)的联动变量。此外，作为自变量的4个障碍（缺乏意识、缺乏知识、缺乏金融素养）在象限4下追求弱DP(4)和强DRP(8)。本研究可以通过收集文献证据和整合研究结果来克服差距，从而清晰地了解问题，从而可能对学术研究人员和政策制定者有益。
{"title":"Strategic decision-making model for addressing barriers in Sukanya Samriddhi Yojana inclusion: An ISM-MICMAC analytical framework","authors":"Kyvalya Garikapati , Sugyanta Priyadarshini , Nisrutha Dulla , Snigdharani Panda , Sumita Mishra , Jayalaxmi Samal","doi":"10.1016/j.rico.2025.100618","DOIUrl":"10.1016/j.rico.2025.100618","url":null,"abstract":"<div><div>The study aims to provide an insight into the performance of Sukanya Samriddhi Yojana (SSY). The primary objective of the study is to identify and understand the frequent barriers obstructing implementation of SSY scheme in rural areas and to determine the interrelationships between these barriers. The analysis made use of Interpretive Structural Modelling (ISM) approach and MICMAC analysis through a combination of expert opinion on validation of barriers. First, the study has identified 225 published documents retrieved from google scholar (n=222), Scopus (n=2) and Web of Science (n=1) data base for reviewing the literature and identifying barriers. Finally, 51 documents fulfilled the objective in identifying 9 Sukanya Samriddhi Yojana Implementation Barriers (SSYIBs). Secondly, ISM Identifies “Lack of communication” with Dependence Power (DP:1) and strong Driving Power (DRP:9) is identified as the major barrier among the 9 validated barriers in the context of rural India in implementing SSY scheme. MICMAC analysis identifies 5 barriers (Perceived cognizance, Societal Stigma, Cultural Inhibition, Lack of educational infrastructure, and Gender discrimination) as linkage variables under Quadrant 3 with strong DP (9) and DRP (5). Further, 4 barriers (Lack of awareness, Lack of knowledge, Lack of financial literacy) as independent variables under Quadrant 4 pursue weak DP (4) and strong DRP (8). This study can possibly be beneficial for academic researchers and policy makers by overcoming the gaps by assembling evidence from literature and integrating the findings for a clear understanding of the matter.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100618"},"PeriodicalIF":3.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221914","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}
Pub Date : 2025-09-13DOI: 10.1016/j.rico.2025.100612
Min Luo , Mei Xiong , Longwei Chen , Yimin Yu
This paper mainly studies the stability of a class of proportional delay complex-valued BAM neural networks. Using the Banach fixed-point theorem, we obtain that the equilibrium points of the neural network exist uniquely, and at the same time, we also obtain its global exponential stability. Different from previous studies, we consider neural network systems in the complex number domain. Thus, the conclusions obtained have broader applicability. Finally, we present a numerical example to verify the validity of the result.
{"title":"Stability of a class of complex-valued BAM neural networks with proportional delays and impulse via fixed point theory","authors":"Min Luo , Mei Xiong , Longwei Chen , Yimin Yu","doi":"10.1016/j.rico.2025.100612","DOIUrl":"10.1016/j.rico.2025.100612","url":null,"abstract":"<div><div>This paper mainly studies the stability of a class of proportional delay complex-valued BAM neural networks. Using the Banach fixed-point theorem, we obtain that the equilibrium points of the neural network exist uniquely, and at the same time, we also obtain its global exponential stability. Different from previous studies, we consider neural network systems in the complex number domain. Thus, the conclusions obtained have broader applicability. Finally, we present a numerical example to verify the validity of the result.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100612"},"PeriodicalIF":3.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159135","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}
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