Operational Problems Research Articles

Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

Prospects and Trends of Digitalization of Project Management in the Region

Relevance. Digital transformation at the present stage in the Russian Federation seems to be a complex and ambitious task, the successful implementation of which depends on the adaptability of public authorities to digital changes and their restructuring to new decision-making processes. Despite the fact that digital technologies were one of the last to enter the public administration sector, their implementation has made it possible to speed up the process of solving large-scale and operational problems. Improving the efficiency of the state and regional management system can be identified as a key factor in the sustainable development of the economy of the Russian Federation. Its stability is ensured as a result of the formation, legislative consolidation and existence of control over the implementation of government programs. In this regard, the digitalization of project management is of particular relevance, since it is the driving force for regional development and is used to eliminate development imbalances. The purpose is to study the prospects and identify trends in the digitalization of project management in the region.Objectives: to consider the concepts of digitalization and digital transformation, to identify the directions of the digitalization process of public administration at the present stage in the Russian Federation, to form ideas about software products to be implemented in the public administration sector and to propose directions for further development of the process of digitalization of the public administration sector of project activities.Methodology. In the process of studying the topic under consideration, methods such as analysis, synthesis, generalization, logical, and graphic methods of information processing were used.Results. Project management of the region seems to be a promising and effective way to eliminate the imbalance of regional development, therefore the introduction of specialized software products with functionality specific to the needs is a strategic task.Conclusions. In order to improve project management of regional development, a comprehensive software product is proposed for implementation. Further development of regional project management is not possible without the creation of a general information system that allows for unified management of all projects in the region.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

The Hourly Peak Coefficient of Single-Family and Multi-Family Buildings in Poland: Support for the Selection of Water Meters and the Construction of a Water Distribution System Model

Taking care of water resources and minimizing water losses in water supply networks requires a broad approach to identifying and neutralizing operational problems. The correct selection of water meters to minimize apparent losses requires knowledge of the characteristic flows that may occur in the facility to which water is supplied. The research aimed to develop tools in the form of mathematical models and water consumption curves along with hourly water consumption coefficients to facilitate the process of selecting water meters for engineers and creating computer models of water supply systems. The research involved monitoring the flow of 76 single-family and multi-family buildings in four towns in Poland, followed by data analysis and development of tools supporting the selection of water meters and the construction of computer models of water distribution networks. High correlation coefficients of the studied variables indicate the results’ usefulness. Four models were developed to determine the maximum flow values in multi-family buildings (three models) and single-family buildings (one model) in the range of water meter diameters DN15-DN40. Characteristics of the average hourly peak coefficient (HPC) values were also developed, along with the range of changes in HPC values for single-family and multi-family buildings.

Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services

Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.

Fair and efficient resource allocation via vehicle-edge cooperation in 5G-V2X networks

To cope with safety risks and operational efficiency problems, it is of paramount importance to ensure high data rates and meet the latency requirements in Connected and Autonomous Vehicles. The problem in such environments is two-fold: 1. Heavy load on the network due to increasing demands; 2) Resource imbalance, due to variations in the vehicular traffic density in certain regions. The consequences of these two phenomena may lead to service disruptions, as well as the fairness of resource allocation across vehicles. In this work, we propose a resource allocation method that distributes high workload among edge nodes and allocates network resources efficiently and fairly. Performance of the proposed method is evaluated under realistic scenarios, and compared to the state-of-the-art approaches in the literature. Behavior and performance of all methods in overload conditions in certain regions were analyzed. Simulation results exhibit a 38% improvement in the successful demand rate and a 48% improvement in capacity usage compared to state-of-the-art approaches.

Study Details Evolution of Operator’s Completions in Vaca Muerta

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212574, “From Exploration to Development: The Completion Evolution in Vaca Muerta,” by Juan C. Bonapace, SPE, Luis A. Riolfo, and Rodrigo Zrain, Total, et al. The paper has not been peer reviewed. _ Operators in the Neuquén Basin have developed Vaca Muerta with different results. In the case of the operator whose work is featured in the complete paper, vertical exploratory wells almost 10 years ago have given way to a current development phase with multihorizontal well pads targeting different landings. The authors discuss how the operator has become more efficient and cost-effective by increasing production and reducing well-delivery-cycle time, while fostering the long-term sustainability of the project by reducing the environmental impact of operations. Evolution of Field Completions The authors focus on the operator’s Field A, where the most progress has been made over the years. The exploratory and appraisal phases of the development are detailed in the complete paper. Pilot Phase. The primary goal for this phase was to validate the productivity of two previously identified landing zones, namely Zones B and D. The wells were located near the vertical wells. Completion. The campaign program accounted for 12 drilled horizontal wells, only 10 of which were completed (two wells were lost because of drilling and casing-integrity issues). Well lateral length ranged between 1000 and 1500 m. The final well architecture was composed of three strings: a 13⅜-in. surface casing, a 9⅝-in. intermediate casing, and a 5½-in. production casing. The completion technique applied was plug-and-perf (PnP) and considered an average stage length of 100 m. Two base designs were defined for each landing zone according to the results obtained in the vertical exploratory wells. An initial sensitivity trial was completed in Zone B, with two different treatment sizes, base and alternative, designed to evaluate the effect on cost and production performance. The results showed a lower production for the wells with the alternative treatment. Operational Issues. Operational problems included casing restriction, detected only once in a well in Zone D during plug drillout operations; and well interference, in that one well presented a change in production during the flowback period on a new neighboring well. This was the result of reduced spacing because of drilling conditions between the new well and the pre-existing one. This phase showed wells in Zone B performing beyond expected production, while those in Zone D underperformed. Development Phase I. It was decided to locate all wells of this phase on the east side of the pilot in the dry-gas window. The main driver for this decision was expected lower CO2 production in this region, based on observations from appraisal wells. Completion. Initial development considered 20 horizontal wells, mainly targeting Zone B. Two additional target zones were added for testing, Zones A and C. In pursuing cost optimization for this development phase, a change to a slimmer architecture was made. Finally, 19 wells were completed. The final well architecture was composed of three strings: a 10¾-in. surface casing, a 7⅝-in. intermediate casing, and a final 4½-in. production casing.

Optimal planning of solar energy using a sensitivity factor for rural electricity needs in an off-grid system (case study: Sebesi Island, South Lampung, Indonesia)

ABSTRACT Small islands in Indonesia usually have a million beauty and extraordinary natural wealth. However, the development of remote islands is prolonged due to the limited electricity supply on these islands. This limitation occurs because the island is far from the primary power grid or uses a diesel generator (DG). Using the DG will increase the cost of shipping fuel oil through limited sea transportation. Therefore, this study aims to conduct a feasibility study of a solar power plant to meet the electricity needs of Sebesi Island, South Lampung, Indonesia. The availability of solar Energy and limited battery life are the main challenges in providing solar Energy today. Therefore, this study also considers the uncertainty of resources by using the sensitivity factor. This research uses HOMER software. This study assumes operating reserve peak, operating reserve, solar reserve, and lifetime battery life to account for resource uncertainty. This method is very effective in overcoming operational limitations and problems. The research results show that the optimal peak operating reserve, operating reserve, solar reserve, and lifetime battery are 10%, 10%, 10–30%, and 5 years, respectively. To meet the daily electrical energy needs of 505.1 kWh, the minimum net current cost (NPC) and energy cost (COE) are $1.26 million and 0.346 $/kWh, respectively. Therefore, this research succeeded in finding a scenario that takes into account sensitivity factors that are more economical. Compared with a diesel generator (DG) with a capacity of 100 kW, the NPC and COE values are $1.29 million and 0.397 $/kWh. Meanwhile, in terms of technology, solar power systems produce much more energy than DG. This system has an energy of 583,316 kWh/year, while the DG system can produce an energy of 268,469 kWh/year. As a result, this system can also reduce CO2 emissions by 241,812 kg/year.

ANALISIS MANAJEMEN RESIKO TEKNOLOGI INFORMASI DAN PEMETAAN MATURITY LEVEL MENGGUNAKAN FRAMEWORK COBIT 4.1 (Studi Kasus: PT. CAHAYA BINTANG)

Due to the rapid development of information technology now, every business must always adapt and take advantage of the latest information technology. The use of information technology provides solutions and benefits for companies in operational, business and financial problems. Information technology certainly needs a kind of framework that makes it easier for companies to map the risks that might occur and in what way the company determines the strategy used so that these risks can be resolved. However, the use of information technology has a significant risk of failure and requires a relatively high investment cost. Such businesses require consistent management of information technology applications. Companies that want to know what risks will arise when implementing information technology really need risk management. To prove that information technology resources are aligned with business processes, carry out risk assessments, manage risks perfectly, carry out maturity level processes, and make recommendations in order to achieve targets that are comparable to company goals, the COBIT 4.1 framework is the framework that best suits the company's interests. The method used for this research consists of several stages, namely, literature study, information gathering, objective setting, risk identification, risk assessment, risk response, risk monitoring, maturity level determination, and making recommendations.

Ship coping strategies for hurricane-induced port disruptions

PurposeDisruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.Design/methodology/approachThis paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.FindingsThe results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.Originality/valueSince the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Design and analysis of ride-sourcing services with auxiliary autonomous vehicles for transportation hubs in multi-modal transportation systems

Autonomous vehicles (AVs), which can be fully controlled by remote/online operators, could be an extension of ride-sourcing services provided by transportation network companies (TNCs). Meanwhile, substitutive and complementary relationships between ride-sourcing and public transit could also help TNCs increase their profit under certain strategies. Unlike previous studies that generally ignored either AVs or interactions between public transit and ride-sourcing, we introduce AV-involved operational strategies into a multi-modal system. In this paper, we focus on transportation hubs in an urban area and let the TNC assign AVs to these hubs for serving only within that particular hub at a differentiated fare; meanwhile, passengers could choose among AVs, human-driven vehicles (HVs), combined modes with public transit and AVs or HVs, or other travel modes to fulfill their travel needs. We develop a mathematical model to investigate the impacts of such an operational strategy, and formulate an optimisation problem to maximise the TNC's profit and minimise total waiting time simultaneously by adjusting AV fare and fleet size. We further propose a comprehensive modeling framework with the analytical model, optimisation problem, calibration method, and heuristic algorithm, making it a general approach for different real-world scenarios. Following the framework, we conduct a case study based on real-world datasets of public transit and ride-sourcing services in Hangzhou, China. Different market schemes are analyzed and compared with the currently existing situation. The results demonstrate that by adopting this auxiliary-AV-oriented operational strategy, the TNC's profit and public transit ridership can both be increased, and passengers could enjoy a shorter waiting time for HV ride-sourcing trips. Moreover, the TNC is more inclined to allocate more AVs to hubs with large commute needs and uncongested traffic, leading to a high profit for the TNC and a short waiting time for passengers. Societal preferences towards AV trips are also analyzed. The results provide in-depth references for real-world AV-related ride-sourcing operational problems.

Optimal Economic Research of Microgrids Based on Multi-Strategy Integrated Sparrow Search Algorithm under Carbon Emission Constraints

In the global transition towards sustainable energy, microgrids are emerging as a core component of distributed energy systems and a pivotal technology driving this transformation. By integrating renewable energy sources such as solar and wind power, microgrids not only enhance energy efficiency and reduce reliance on traditional energy sources but also bolster grid stability and mitigate the risk of widespread power outages. Consequently, microgrids demonstrate significant potential in improving the reliability of power supply and facilitating flexibility in energy consumption. However, the operational planning and optimization of microgrids are faced with complex challenges characterized by multiple objectives and constraints, making the reduction in operational costs a focal point of research. This study fully considers an operational model for a microgrid that incorporates distributed energy resources and comprehensive costs, integrating a battery storage system to ensure three-phase balance. The microgrid model includes photovoltaic power generation, wind power generation, fuel cells, micro-gas turbines, energy storage systems, and loads. The objectives of operating and maintaining this microgrid primarily involve optimizing dispatch, energy consumption, and pollution emissions, aiming to reduce carbon emissions and minimize total costs. To achieve these goals, the study introduces a carbon emission constraint strategy and proposes an improved Multi-Strategy Integrated Sparrow Search Algorithm (MISSA). By applying the MISSA to solve the operational problems of the microgrid and comparing it with other algorithms, the results demonstrate the effectiveness of the carbon emission constraint strategy in the microgrid’s operation. Furthermore, the results prove that the MISSA can achieve the lowest comprehensive operational costs for the microgrid, confirming its effectiveness in addressing the operational challenges of the microgrid.

New insights into dynamic disaster monitoring through asynchronous deformation induced coal-gas outburst mechanism of tectonic and raw coal seams

The coalbed methane energy resources waste, environmental pollution and miner's health remain challenging problems in worldwide mining operations accompanied by coal-gas outbursts; thus, the safe and efficient yield of these resources plays a vital role in the world's energy market. The tectonic coal development is a significant geological feature of coal and gas outburst sites, and the study of coal and gas outburst mechanism should be based on more precise geological occurrence characteristics. In this paper, the geological characteristics of coal and gas outburst sites were analyzed. The failure law of coal body under the stratified occurrence of tectonic coal and raw coal seam with different thickness was studied. The mechanical equilibrium equation of non-synchronous deformation of tectonic coal and raw coal was constructed, the initiation criterion of coal and gas outburst was established based on the support body model, and a case study was carried out. The results show that with the thickness increase of the tectonic coal seam, the peak displacement of the driving face increases. The outburst start-up and development of coal and gas outburst, coal and gas extrusion outburst and coal and gas collapse outburst were deeply analyzed, as well as the internal reasons of the occurrence of coal-gas outburst portent are explained, based on the support body model. The development process from the start to the end of coal and gas outburst is revealed, and a new perspective is proposed for the prevention and control of coal and gas outburst: improving the strength of support body is also of great significance for the prevention and control of coal and gas outburst.

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning

Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of attention within the constrained optimization (CO) and machine learning (ML) communities towards combining prediction and optimization models. The so-called end-to-end (E2E) learning captures the task-based objective for which they will be used for decision making. Although a large variety of E2E algorithms have been presented, it has not been fully investigated how to systematically address uncertainties involved in such models. Most of the existing work considers the uncertainties of ML in the input space and improves robustness through adversarial training. We extend this idea to E2E learning and prove that there is a robustness certification procedure by solving augmented integer programming. Furthermore, we show that neglecting the uncertainty of COs during training causes a new trigger for generalization errors. To include all these components, we propose a unified framework that covers the uncertainties emerging in both the input feature space of the ML models and the COs. The framework is described as a robust optimization problem and is practically solved via end-to-end adversarial training (E2E-AT). Finally, the performance of E2E-AT is evaluated by a real-world end-to-end power system operation problem, including load forecasting and sequential scheduling tasks.

Novel indole-based self-polishing environmentally friendly acrylic antifouling coatings

Marine biological pollution poses a significant threat to both marine activities and human biomedical applications. The pivotal solution lies in the development of environmentally friendly antifouling coatings. In this study, indole derivatives containing carbon-carbon double bond (specifically allyl 5-Cl/NO2-indole-carboxylate, abbreviated as ACIC/ANIC) were synthesized from 5-Cl/NO2-indole and incorporated into zinc acrylate resins via N-acylation reaction. The resulting mixture of zinc acrylate resin and polycaprolactone (PCL) was integrated into acrylic urethane lacquers (AUL), and the self-polishing and antifouling properties of these lacquers were comprehensively evaluated through antimicrobial experiments, algal inhibition studies, static simulations, and antifouling experiments conducted in marine environments. The findings demonstrate that these coatings not only exhibit effective self-polishing properties but also outperform pure zinc acrylate resins in terms of antifouling properties. Moreover, the incorporation of PCL prolongs the service life of the coatings. In specific, the coatings containing ANIC showcased impressive antimicrobial activity with antimicrobial rates of 92.79 % and 99.71 % against typical Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, respectively. Additionally, the algal inhibition rate against Nitzschia closterium and Phaeodactylum tricornutum reached 90.35 % and 79.74 %, respectively. In summary, ANIC emerges as an efficient and environmentally friendly coating material, offering a promising approach to address the economic and operational problems associated with marine bio-pollution.

Abstract P345: Is a Fixed Value of the Rate of Gas Exchange Appropriate to Indicate the Maximum Effort of Obese and Nonobese Adults Undergoing a Treadmill Cardiopulmonary Exercise Test?

Introduction: Obese individuals present a higher O2 uptake/workload during a treadmill cardiopulmonary exercise test (CPET) in addition to chronotropic and biomechanical impairment, which preclude them, many times, from reaching a maximal rate of gas exchange (Rmax) > 1.0. Therefore, it is necessary to develop reference values for Rmax for a treadmill CPET according to age, sex, and a wide range of body mass index (BMI). Objectives: To establish reference values for Rmax for a treadmill CPET according to age, sex, and BMI. Methods: We re-examined 1,612 treadmill ramp CPETs of adults (893 women), of which 772 were obese (BMI ≥ 30 kg/m 2 ). We excluded tests of nonobese participants with HRmax < 85% of predicted, of any participant with a rate of perceived exertion in the BORG scale < 4 (intense), and of those that we had operational problems during the test. We produced a Rmax percentile table according to sex, age (< 33, 34 to 46, and > 46 years), and BMI (< 30 and ≥ 30 kg/m 2 ). We fit stepwise multiple regressions to investigate the influence of age, sex, BMI, and cardiovascular risk on Rmax. We also presented an obesity-specific equation to estimate maximum heart rate (HRmax). Results: We evaluated 442 eutrophic participants (BMI < 25 kg/m 2 ), 398 were overweighted (BMI = 25 - 29.99 kg/m 2 ), 295 had class 1 obesity (BMI = 30 - 34.99 kg/m 2 ), 247 had class 2 obesity (BMI = 35 - 39.99 kg/m 2 ) and 230 had class 3 obesity (BMI ≥ 40 kg/m 2 ). Age, sex, and BMI were the main determinants of Rmax (R 2 = 0.195). The table with percentiles confirmed that the lower limits of Rmax did not reach 1.0, especially in older, obese, and female participants (Table 1). In the obese participants, we could predict HRmax as 195.2 - (0.796 x age). Conclusions: The Rmax values in a treadmill CPET should be chosen considering attributes such as age, sex, and BMI. Our results suggest that fixed values of Rmax are not appropriate to indicate maximum effort, especially in people with low cardiorespiratory fitness.

Consensus-Based Merging and Spacing Method for Aircraft Self-Separation at Intersections

Within the context of delegating separation related tasks from an air traffic controller to the flight deck, this paper analyzes the merging and spacing problem for autonomous aircraft operations at busy airway intersections. A consensus-based control protocol is developed to achieve an ordered and safe traffic flow at a given intersection waypoint. Two primary implementation steps are covered in the protocol. In the first step, consensus on the target merging flow is achieved, where the merging stream is modeled as a straight line with the minimum total converging distance. In the second step, multiple aircraft with double-integrator dynamics and time-varying communication topologies are guided to merge into the target flow while maintaining appropriate interaircraft spacing. A state control law is designed, and the Lyapunov analysis method is leveraged to derive sufficient conditions on the communication topology and the control parameter for merging agents to reach consensus in finite time. Performance of the proposed method is evaluated through experimental simulations under different hypothetical scenarios in the two-dimensional plane.

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Analyzing Primary Healthcare Governance in Indonesia: Perspectives of Community Health Workers.

Background Community-integrated health posts (Posyandu) are crucial for extending primary healthcare across diverse geographical and demographic landscapes in Indonesia. Community health workers (CHWs) currently function as the main service delivery actors for Posyandu. However, Posyandu's performance remains below the standards set by the Ministry of Health. This study examines health system determinants that explain the poor performance of Posyandu and, in particular, examines the roles of village and township governance and community health center management in supporting the effectiveness of primary healthcare programs. Methodology We analyzed 638 Posyandus across 13 Indonesian provinces, utilizing data from the 2014 Indonesia Family Life Survey. We evaluated eight health system determinants based on the perceptions of CHWs concerning challenges encountered in Posyandus. These factors were ranked and analyzed to determine the variables that affected Posyandu's poor performance. Both unadjusted and adjusted odds ratios were calculated. Results This study revealed that nearly half of the Posyandus in Indonesia are underperforming, particularly in rural areas. Common challenges include insufficient funds, inadequate equipment, and a lack of permanent buildings. Although only a small percentage mentioned minimal support from village and Puskesmas authorities, the weak governance roles of village heads are reflected in all the concerns mentioned by CHWs. Conclusions The absence of village heads from governance roles has contributed to Posyandu's operational problems as perceived by CHWs. Community health centers (Puskesmas), responsible for providing technical support to Posyandu, should be part of CHW teams and networks. Further discussions are needed to choose a workable governance model to ensure practical, accessible, and sustainable primary healthcare services at the grassroots level.

Quantenalgorithmen zur Optimierung von Produktions- und Logistikprozessen

Abstract Quantum computing is an important building block for the digitalized future of a company. Successes in recent years – in the development, construction and operation of quantum computers – have led to the achievement of a preliminary stage of industrialization. Quantum-based algorithms have the potential to solve operational decision-making problems more quickly and thus to manage complex decision-making situations dynamically. German industrial companies can already demonstrate the first concrete use cases for quantum algorithms to optimize factory processes.