Control Rules Research Articles

Assay error detection when using common quality control targets across multiple instruments: An analysis using simulated and real-world data.

Clinical laboratories frequently implement the same tests and internal quality control (QC) rules on identical instruments. It is unclear whether individual QC targets for each analyser or ones that are common to all instruments are preferable. This study modelled how common QC targets influence assay error detection before examining their effect on real-world data. The effect of variable bias and imprecision on error detection and false rejection rates when using common or individual QC targets on two instruments was simulated. QC data from tests run on two identical Beckman instruments (6-month period, same QC lot, n > 100 points for each instrument) determined likely real-world consequences. Compared to individual QC targets, common targets had an asymmetrical effect on systematic error detection, with one instrument assay losing detection power more than the other gained. If individual in-control assay standard deviations (SDs) differed, then common targets led to one assay failing QC more frequently. Applied to two analysers (95 QC levels and 45 tests), common targets reduced one instrument's error detection by ≥ 0.4 sigma on 15/45 (33%) of tests. Such targets also meant 14/45 (31%) of assays on one in-control instrument would fail over twice as frequently as the other (median ratio 1.62, IQR 1.20-2.39) using a 2SD rule. Compared to instrument-specific QC targets, common targets can reduce the probability of detecting changes in individual assay performance and cause one in-control assay to fail QC more frequently than another. Any impact on clinical care requires further investigation.

Signature functional control of digital automatic device register operations

The development of digital operating automatic devices models of the systems with the property of high functional controllability of hardware and software tools for the performance of computational functions (operations) on a real scale becomes a priority task in the context of ensuring high efficiency and reliability of monitoring the functioning of digital metrological telemetric measurement and control systems of critical infrastructure problem growing relevance. In the article, based on a critical analysis of known methods for digital systems functional control, a method of ensuring the operational controllability of digital automatic devices is proposed based on the concept implementation of predicting (forecasting) control signatures of tuples and their state. Relevant concepts are introduced, and a structural-functional model of functional control for digital automatic device with «hard» logic based on the method of signature prediction is proposed. In the context of the practical implementation of the proposed method of predicting signatures on each operating cycle of the digital automatic device, a system of control signature equations formulas (signature functional control rules) is given for the basic types of digital automatic device register operations with «hard» logic. This makes it possible to implement a new functional control principle (signature functional control by the method of signature prediction) of digital automatic devices by introducing a unified hardware redundancy (synthesized hardware node of a signature functional control) from functionally complete elementary combinational structures from a finite set (standard set). In this regard, the relevance of functional control by the method of predicting signatures for generators of pseudorandom sequences of the maximum length in the digital automatic device of cryptographic encryption by jamming measurement information during its transmission in telecommunication channels of management systems of critical infrastructure objects in the conditions of possible cyberattacks is reasonably grounded. The scientific novelty of the obtained theoretical results is the authors' proof of the need to develop further the methodology of functional control of a general-purpose digital automatic device and, in particular, of telemetric measurement and control systems of critical infrastructure based on the use of the author's mathematical apparatus of synthesis «signature Boolean-polynomial algebra».

Development of a new control rule for managing anthropogenic removals of protected, endangered or threatened species in marine ecosystems.

Human activities in the oceans are increasing and can result in additional mortality on many marine Protected, Endangered or Threatened Species (PETS). It is necessary to implement ambitious measures that aim to restore biodiversity at all nodes of marine food webs and to manage removals resulting from anthropogenic activities. We developed a stochastic surplus production model (SPM) linking abundance and removal processes under the assumption that variations in removals reflect variations in abundance. We then consider several 'harvest' control rules, included two candidate ones derived from this SPM (which we called 'Anthropogenic Removals Threshold', or ART), to manage removals of PETS. The two candidate rules hinge on the estimation of a stationary removal rate. We compared these candidate rules to other existing control rules (e.g. potential biological removal or a fixed percentage rule) in three scenarios: (i) a base scenario whereby unbiased but noisy data are available, (ii) scenario whereby abundance estimates are overestimated and (iii) scenario whereby abundance estimates are underestimated. The different rules were tested on a simulated set of data with life-history parameters close to a small-sized cetacean species of conservation interest in the North-East Atlantic, the harbour porpoise (Phocoena phocoena), and in a management strategy evaluation framework. The effectiveness of the rules were assessed by looking at performance metrics, such as time to reach the conservation objectives, the removal limits obtained with the rules or temporal autocorrelation in removal limits. Most control rules were robust against biases in data and allowed to reach conservation objectives with removal limits of similar magnitude when averaged over time. However, one of the candidate rule (ART) displayed greater alignment with policy requirements for PETS such as minimizing removals over time.

Management strategy evaluation of harvest control rules for Pacific Herring in Prince William Sound, Alaska

Abstract Management strategy evaluation (MSE) provides a mechanism to test the relative performance of alternative management strategies on a fishery. For Pacific herring in Prince William Sound, Alaska, no directed fisheries have occurred in over 30 years, providing an opportunity to evaluate potential management strategies before a fishery is opened. Here we evaluate and compare 10 harvest control rules (HCRs) ranging from simple threshold rules to rules accounting for population age structure, biomass trends, and weight distribution using an MSE integrated with a fully Bayesian stock assessment estimation model. We developed a utility function that shows simple threshold HCRs outperform the more complex rules, especially for catch stability. According to this utility function, the best rule had a lower limit threshold than the current default rule, while the worst rule had a higher limit threshold. Our simulations demonstrate that sufficient computing power exists for MSEs based on Bayesian estimation methods, thus opening a pathway for MSEs to simulation test probabilistic control rules, which provide a buffer against scientific uncertainty and should reduce the risk of overfishing.

EASS: An automatic steering system for agricultural wheeled vehicles using fuzzy control

Automatic steering is a crucial technology for achieving automatic navigation and unmanned driving of agricultural machinery. In this research, agricultural wheeled vehicles with the HPAS (Hydraulic Power Assistant Steering) system were selected as the research object and the EASS (Electric Automatic Steering System) was proposed by using fuzzy control to realize effective steering instead of human drivers. The PID (Proportional Integral Differential) algorithm was used to process the difference between the desired and actual steering angles and to realize automatic control of rotation of directional wheels. Fuzzy control was adopted to achieve self-adjusting of PID parameters and to optimize working state of the steering motor in real-time for different agricultural wheeled vehicles under various operating conditions. Fuzzy control rules were explored and formulated by analyzing variation of the steering angle under different resistive torque values. For evaluation of its feasibility, the newly developed EASS was applied in automatic navigation of a rice transplanter, a tractor, and a high-clearance sprayer. Field testing results showed that the maximum path tracking lateral error and heading error were 4.39 cm and 2.13°, respectively. And the maximum RMS (Root Mean Square) errors were 3.75 cm and 1.02°, respectively. Therefore, the fuzzy control-based the EASS in this research was applicable to various agricultural wheeled vehicles.

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems.

Black-box artificial intelligence (AI) induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represented using a black-box AI are capable of efficiently executing the underlying control task and achieving optimal closed-loop performance-controlling the agent from the initial time step until the successful termination of an episode, the developed control rules are often complex and neither interpretable nor explainable. In this article, we use a recently proposed nonlinear decision-tree (NLDT) approach to find a hierarchical set of control rules in an attempt to maximize the open-loop performance for approximating and explaining the pretrained black-box DRL (oracle) agent using the labeled state-action dataset. Recent advances in nonlinear optimization approaches using evolutionary computation facilitate finding a hierarchical set of nonlinear control rules as a function of state variables using a computationally fast bilevel optimization procedure at each node of the proposed NLDT. In addition, we propose a reoptimization procedure for enhancing the closed-loop performance of an already derived NLDT. We evaluate our proposed methodologies (open- and closed-loop NLDTs) on different control problems having multiple discrete actions. In all these problems, our proposed approach is able to find relatively simple and interpretable rules involving one to four nonlinear terms per rule, while simultaneously achieving on par closed-loop performance when compared to a trained black-box DRL agent. A postprocessing approach for simplifying the NLDT is also suggested. The obtained results are inspiring as they suggest the replacement of complicated black-box DRL policies involving thousands of parameters (making them noninterpretable) with relatively simple interpretable policies. The results are encouraging and motivating to pursue further applications of proposed approach in solving more complex control tasks.

Intermediate Tuning Proposal for Fractional Order PID Controllers with Balanced Servo/Regulation Operation

Many tuning rules for controllers consider each operating mode separately, including servo-control and regulatory-control. This paper considers the problem where an optimal controller for one of these modes has to operate in a different mode than the one for which it was tuned. A weighted performance degradation index is analyzed and an intermediate tuning rule is proposed for fractional-order PID controllers, taking into account a robustness constraint. This rule serves as a proposal for further research on the application of this methodology in the design of tuning rules for FOPID controllers.

Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources

The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options. Such rules enable DERs to respond autonomously in response to time-varying grid loading conditions. The rules comprise affine droop control augmented with a deadband and saturation regions. Nonetheless, selecting the shape of these rules is not an obvious task, and the default options may not be optimal or dynamically stable. To this end, this work develops a novel methodology for customizing Volt/VAR rules on a per-bus basis for a single-phase feeder. The rules are adjusted by the utility every few hours depending on anticipated demand and solar scenarios. Using a projected gradient descent-based algorithm, rules are designed to improve the feeder’s voltage profile, comply with IEEE 1547 constraints, and guarantee stability of the underlying nonlinear grid dynamics. The stability region is inner approximated by a polytope and the rules are judiciously parameterized so their feasible set is convex. Numerical tests using real-world data on the IEEE 141-bus feeder corroborate the scalability of the methodology and explore the trade-offs of Volt/VAR control with alternatives.

Optimal Fuzzy Logic Proportional Integral Derivative Controller Design by Bee Algorithm for Hydro-Thermal System

This paper proposes a robustness and an optimum of the fuzzy logic-proportional integral derivative (FLPID) controllers for automatic generation control (AGC) of a two area interconnected hydro-thermal system and an application of bee algorithm (BA) in order to design FLPID controllers. Traditionally, membership functions (MF) and control rules (CR) of the fuzzy logic controller (FLC) were achieved by trial and error methods of designers. To solve this problem, BA was proposed to concurrently tune PID gains, MF and CR of the FLPID controllers to minimize frequency deviations and tie-line power deviations of power system. Simulation results clearly showed that the performance and the robustness of the optimal FLPID controllers were superior to the conventional PID and the FLPID controllers in terms of settling time and overshoot.

ОБОСНОВАНИЯ ПО ПРИМЕНЕНИЮ ОПЫТА ПРОТИВОТУБЕРКУЛЁЗНОЙ СЛУЖБЫ ДЛЯ АДЕКВАТНОГО РЕАГИРОВАНИЯ В ПЕРИОД ПАНДЕМИИ COVID-19

Objective: To summarize the experience of the anti-tuberculosis care (ATC) units of the Republic of Tajikistan (RT) to optimize medical aid to the population during potential outbreaks of respiratory infectious diseases. Methods: This study examines the experience and lessons learned after the COVID-19 pandemic in the RT, as well as the main provisions recommended for the partial reorganization of the ATC to ensure its readiness to function during outbreaks of respiratory infections. The standards and rules of antituberculosis infection control were used to develop preventive measures against the nosocomial spread of aerogenic infections in healthcare facilities and society. Results: The analysis revealed that maintaining of a sanitary and anti-epidemic regimen in healthcare institutions primarily involved a routine approach to the disinfection of medical facilities. However, in respiratory infections, adopting the experience of ATC RT was beneficial. It included strict adherence to infection control rules, social distancing, avoidance of crowded public places, restriction of unnecessary migration and public events, and the rigorous use of means of personal protection. The ATC also pioneered innovative approaches through various software, which have proven effective even under the restrictions imposed during the COVID-19 pandemic. Conclusion: ATC can serve as a coordinating link and an excellent example of how to prepare the healthcare system for various potential outbreaks of respiratory infections. It is necessary to integrate the ATC with other relevant institutions of the healthcare system, both functionally and structurally, to increase the effectiveness of preventive measures against the transmission of respiratory infections. Keywords: TB care, health care facilities, COVID-19, respiratory infections.

Tobacco Labelling and Advertising Rules: Lessons from Other Countries for Zimbabwe

This paper analysed the approach that has been taken by four countries in controlling tobacco usage through advertising and labelling rules. The paper is a documentary analysis and literature review of primary and secondary legal sources. The paper considered the national approaches adopted by the Commonwealth of Australia, the United Kingdom (UK), Thailand and Zimbabwe. The first three nations were used as examples that can offer lessons to Zimbabwe on how international tobacco control obligations are met. Australia and the UK were the first two countries to adopt plain packaging and hence offer some best practices. Thailand, on the other hand, was the first developing country to adopt plain packaging rules. Thailand’s case shows that developing countries can also adopt sound tobacco control rules. The Australian law led to several national and international legal challenges that were resolved in favour of Australia. The legal domestic challenges against standardised packaging in the United Kingdom were also resolved in favour of the British government. It was established in the study that, despite acceding to the World Health Organisation Framework Convention for Tobacco Control (WHO FCTC), the Zimbabwean national legal framework is not yet compliant with the FCTC international treaty obligations. The laws of Zimbabwe are scanty and leave a lot of gaps in the control framework which have been exploited by tobacco manufacturers. This is unsatisfactory and calls for action on the part of the government.

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.

Securely Cloud Data Storage and Sharing

With the increasing adoption of cloud computing, data storage and sharing have be-come integral parts of our digital lives. However, ensuring the security and privacy of data stored in the cloud remains a significant challenge. This paper proposes a novel approach for securely storing and sharing data in the cloud, addressing the vulnerabilities associated with traditional cloud storage models. The proposed approach utilizes advanced cryptographic techniques, including hyperchaotic encryption and hash functions, to protect the confidentiality and integrity of data stored in the cloud. The hyperchaotic encryption algorithm provides a high level of security by introducing chaos-based dynamics into the encryption process, making it resistant to various attacks. Additionally, the hash function ensures the integrity of data by generating unique identifiers for each file stored in the cloud. To enhance data sharing security, the proposed approach employs access control mechanisms and user authentication protocols. Access control rules are enforced to restrict unauthorized access to data, while user authentication ensures that only legitimate users can access and modify the shared data.

Tuberculosis experience in possible respiratory infectious disease outbreaks with lessons learned during the COVID-19 pandemic

The analysis of the lessons learned during the COVID-19 pandemic was carried out and the provision of treatment and preventive services to the population in health care facilities was improved in case of possible emergencies in connection with possible outbreaks of respiratory infectious diseases in the Republic of Tajikistan using the experience of the anti-tuberculosis service.During the COVID-19 pandemic, individual employees of anti-tuberculosis facilities reflected the relationship of coronavirus infection with contacts at work. The relationship between the dynamics of reducing the incidence of tuberculosis among employees of specific medical institutions and the volume of implemented interventions for anti-tuberculosis infection control, which, in combination with other anti-epidemic measures, plays a significant role in reducing the risk of spreading tuberculosis infection and is an essential element of protecting the health of medical workers, has been scientifically proven. The experience of TB facilities in complying with infection control rules and regulations can be used to prepare health systems for various likely outbreaks of respiratory infections.

Mental health of primary health care physicians and nurses following prolonged infection control rules: a national survey in China.

This study examined the prevalence and correlates of probable mental health disorders, including psychological distress, somatization, depression, anxiety, phobic anxiety (PHO), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and insomnia among Chinese primary health care (PHC) physicians and nurses amid the post-pandemic period in 2022. Region-stratified sampling was conducted to recruit a national sample of 4,246 respondents from 31 July 2022 to 12 August 2022. A total of 692 primary healthcare institutions were identified in 30 provincial-level administrative regions of China. An online questionnaire was used for assessing probable mental health disorders using Symptoms Checklist-90-Revised (SCL-90-R) and PTSD Checklist for DSM-5 (PCL-5), and sleeping problems using Insomnia Severity Index (ISI). Data on demographics and work were also collected. Bivariate analysis and multiple logistic regression were conducted to identify significant correlates of probable mental health disorders. A total of 4,246 valid questionnaires were identified. Results showed that relative to the prevalence of probable mental health disorders among health care workers at the early stage of the pandemic in China, there was an overall decreased prevalence except for somatization, PHO, and OCD among the current PHC physicians and nurses. Multiple logistic regressions showed that significant risk factors of common probable mental health disorders, namely psychological distress, SOM, DEP, ANX, PHO, OCD, PTSD, and insomnia, were female gender, multimorbidity, history of psychiatric disorders, quarantine experience, never asking anyone for help, and overtime work. Attention should be given to preexisting psychiatric and multimorbid conditions, social support, and work-related stressors. Regular assessment and psychological interventions are needed to enhance the mental health of PHC professionals even after public health crisis.

Fuzzy Logic-Based Energy Storage Control in Smart Grids for Grid Stability

This study studies the usefulness of fuzzy logic-based control systems for improving energy storage control inside smart grids to promote grid stability. The study combines empirical data analysis, including energy storage system (ESS) specifications, smart grid operational data, fuzzy logic-based control rules, and ESS state variables, to demonstrate the suitability and efficiency of using fuzzy logic-based control mechanisms in dynamic grid environments. The examination of ESS specs revealed a wide range of maximum capacities, spanning from 100 kWh to 200 kWh. Additionally, the charge and discharge efficiencies exhibited variations, ranging from 85% to 96%. An analysis of operational data from the smart grid revealed significant variations in grid frequency, ranging from 50.0 Hz to 50.3 Hz. Voltage levels also exhibited fluctuations, ranging from 229 kV to 232 kV. Additionally, renewable energy generation from solar and wind sources showed fluctuations between 1400 kW to 1650 kW and 800 kW to 850 kW, respectively. The incorporation of linguistic factors and fuzzy rules based on grid parameters facilitated the adaptive control of ESS units in the construction of fuzzy logic-based control rules. The analysis of ESS state variables revealed dynamic changes in the state of charge, which ranged from 60% to 90%. Additionally, oscillations in available energy were observed across different timestamps and ESS units. An investigation of in state variables, revealed adaptive changes percentage change demonstrating varying degrees of variations in state of charge, available energy, and operational states at various timestamps. The results emphasize the flexibility and efficiency of control systems based on fuzzy logic in improving energy storage operations in smart grids, highlighting their capacity to improve grid stability and efficiently handle changing grid characteristics.

Mediating Role of Good Governance between Debt Management and Economic Performance

No one country of the planet is self-sufficient for financial and non-financial resources. Some have abundant of natural resources while someone have abundant of human resources and so on. Due to scarce of resources a country needs effective and efficient utilization of resources. Effective and efficient utilization of resources depends upon good governance, competent and sincere economic managers. Keeping in view of the said perception, current study examined the role of good governance in the debt management and economic performance. Current study used the World Bank data from the period of 1990 to 2020. Economic performance is measured by the per capita income (PCI) and taken as dependent variable. Whereas, debt to gross domestic product (GDP) is taken an independent variable. While good governance (GG) is taken as mediating variable and measured through The Worldwide Governance Indicators (WGI) project constructs aggregate indicators of six broad dimensions of governance such as voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of Law and Control of Corruption. Econometric equation is developed to measure the direct and indirect effect of GG with respect to debt to GDP and PCI. The study results concluded that there is no relationship between debt to GDP and economic development. While interesting finding is that there is significant relationship between GG and economic performance. Study also found insignificant mediation of GG between debt to GDP and economic development.

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

KPI-HGNN: Key provenance identification based on a heterogeneous graph neural network for big data access control

Dynamic access control of big data has received much attention in recent years because of the characteristics of dynamic generation and multi-source aggregation of big data resources. Provenance-based access control defines dependency paths between resource states through provenance data, and it can dynamically realize access control according to different stages and states of resources. However, big data systems involve huge resources, and the state transition process is complex. The existing research on provenance-based access control techniques faces problems such as low efficiency of manually formulating dependency paths, redundant information in provenance data, and uneven distribution of resources. To solve these problems, this paper proposes a key provenance identification framework KPI-HGNN based on the heterogeneous graph neural network. In the framework, a community detection algorithm based on the heterogeneous graph neural network is designed to realize automatic identification and division of corresponding regions of big data resources through feature fusion of multiple types of nodes and edges. Meanwhile, a key node identification algorithm based on the heterogeneous graph attention network is designed to identify the key node in each community by weighted aggregation of the neighboring node attention coefficients. Besides, a key dependency path discovery algorithm is designed, and access control rules are automatically generated based on key dependency paths. The experimental results indicate that the community detection clustering index of the proposed method is better, the Top-5% key node identification accuracy is higher, and the resource coverage rate and the average percentage number product of key nodes of the provenance-based access control rules are better than those of the baseline method. These results indicate that the proposed key provenance identification framework can efficiently solve the problems in identifying key provenance information faced by dynamic access control of big data.

MODEL-BASED OPTIMIZATION OF ICVs’ CONTROL FOR PRODUCERS, INJECTORS, AND WAG-CO2 CYCLES SIZE IN A CARBONATE RESERVOIR UNDER UNCERTAINTY

Life cycle management of oil fields involves complex decisions, with many variables to be considered. In some fields of the Brazilian pre-salt, this task is even more challenging given the ultra-deep water location, with an expressive amount of gas (including CO2) production. This feature becomes a restricting factor for oil production in these types of fields, and a proper management is essential to overcome this issue, improving both production and economic returns. Therefore, Water-Alternate-Gas (WAG) is often used as an Enhanced Oil Recovery- EOR mechanism, providing a better mobility control, improving reservoir sweep and oil recovery. Moreover, WAG can be an alternative for recycling the produced gas, providing an adequate destination (mainly for CO2) and avoiding environmental problems. In this case, we name the method as WAG-CO2. Moreover, Interval Control Valves (ICVs) are also a good alternative to deal with these fields, since they work as flow control devices and allow for a better balance of the fluid flow in the reservoir. In this work, we evaluate the impact of the use of ICVs in producers, injectors, along with WAG cycles size to improve the performance of a developed field. The work consists of two parts. In Part I, using a single simulation model, we performed (1) six hierarchical optimization approaches to verify if the order in which the optimization occurs influences the final results, and (2) a joint optimization of all parameters for comparison purposes. The final strategies are simulated in a reference case (representing the “true response” of the field) to evaluate how the achieved control rules would behave in a real field. In Part II, we considered a probabilistic procedure, applying one of the hierarchical approaches in several representative models (RMs) expressing uncertain scenarios. In this case, we compared nominal optimization of individual RMs against a robust optimization (in which all RMs are optimized simultaneously). The study is applied to the benchmark case SEC1_2022, a synthetic model analogous to a pre-salt field. The results show an expressive increase in the economic return of the field with a proper operation of the ICVs and size of the WAG-CO2 cycles, despite the platform being restricted by gas production capacity. This result occurred because of a better balance in fluid flow through the reservoir and among wells. Moreover, in the first part of the work, all hierarchical approaches presented very similar results in terms of NPV, so the order of the optimization steps did not significantly affect the results. The non-hierarchical approach presented a slightly better result, but still close to those obtained in the hierarchical approaches. Furthermore, the most impacting variable was the ICV for producers, followed by ICV for injectors and, then, WAG-CO2 cycles. Similar tendencies were observed in the second part of the work, with higher expected economic return for the robust optimization.