Optimal Planning Research Articles

Endovascular and percutaneous embolization of a giant post traumatic arteriovenous fistula of inferior epigastric vessels

BackgroundArteriovenous fistulas involving the anterior abdominal wall can result from trauma. Such fistulas may remain asymptomatic and undetected for a prolonged duration of time. They tend to recruit multiple arterial feeders with remodelling in the feeding arteries, making them challenging to treat.Case presentationWe discuss a rare case of a 60-year-old male who presented with complaints of a progressive painless swelling in right lower abdomen. There was a history of blunt injury to abdomen at the same site during alleged road traffic accident 3 years ago. On CT angiography, an arteriovenous fistula was localised to the anterior abdominal wall arising predominantly from the right inferior epigastric artery with a giant venous sac and terminating as a tortuous single venous channel into the right external iliac vein. Few other small feeders were also seen arising from branches of right superior epigastric artery along Winslow’s pathway. The main challenge in endovascular management of this patient was embolization of a high flow shunt with a large venous sac and multiple arterial feeders. The dominant arterial feeder was embolized using vascular plug. The superficial location of the lesion offered an additional percutaneous window besides endovascular approach. The venous sac was percutaneously accessed and embolized using n-butyl cyanoacrylate after balloon occlusion of outflow vein. On follow up ultrasonographic evaluation at 3 months, near complete thrombosis of the venous sac was achieved.ConclusionsTraumatic arteriovenous fistulas involving the inferior epigastric vessels are rare clinical entities. CT angiogram and digital subtraction angiography help in the optimal diagnosis and treatment planning. The use of mechanical embolization devices to cause flow arrest offers an opportunity to use liquid embolic agents which offer better percolation within the lesion. Interventional radiology offers an ideal management of these complex high flow fistulas with a good technical success and acceptable safety profile.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Optimal Planning of Multiple Geosynchronous Spacecraft Flyby Inspection Mission with Multiple Servicing Space Robots

Optimal Planning of Multiple Geosynchronous Spacecraft Flyby Inspection Mission with Multiple Servicing Space Robots

ParallEdge: Exploiting Computing-Mobility Parallelism for Efficient 5G/6G Edge Computing

With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt$</tex-math></inline-formula> 10000 times larger than the deployment cost of the 4G infrastructure. To address this fundamental limit, we propose ParallEdge, a deployment scheme that employs mobile edge servers for cost-effective service coverage. ParallEdge is designed based on the observation that the processing delay and server moving delay become comparable in many computing-intensive applications in the 5G/6G edge computing. Unlike the traditional “move-then-process” frameworks, ParallEdge follows a “move-while-processing” paradigm, which exploits the parallelism between task processing and server movement to enable resource sharing among more users. Moreover, with the joint optimization of path planning and task scheduling for multiple mobile servers, the deployment cost for service coverage can be further reduced. We analyze the approximation gap of the proposed algorithm, and conduct extensive simulation experiments based on the real-world application data, and the results show that ParallEdge can significantly reduce deployment cost and improve resource utilization compared to the state-of-the-art schemes.

Characterizing the Rhinoplasty Outcomes Most Important to Patients, Surgeons, and the General Population.

Understanding rhinoplasty characteristics important to patients, physicians, and society is essential for evaluating outcomes and designing optimal treatment plans. The authors aimed to elucidate specific rhinoplasty-related outcomes that are most important to patients, surgeons, and the general population. A cross-sectional survey comprising 11 rhinoplasty-specific characteristics, was distributed to patients, facial plastic surgeons, and the general population. Adult patients presenting for rhinoplasty consideration or follow-up after undergoing rhinoplasty were recruited. Characteristics rankings were compared between the 3 respondent groups using Spearman's rank correlation coefficient (ρ). Responses from 150 surgeons, 111 patients, and 102 lay individuals from the general population were included for analysis. When ranking rhinoplasty-specific characteristics in order of importance, patients and the general population ranked "ability to breathe through nose while awake" first and "overall appearance of nose" as second. Surgeons ranked "overall appearance of nose" first and "ability to breathe through nose while awake" second. There were strong correlations between patients' and surgeons' rankings (Spearman's ρ=0.836, P=0.002), between patients' and the general population's rankings (Spearman's ρ=0.773, P=0.007), and between surgeons' and the general population's rankings (Spearman's ρ=0.782, P=0.006). Our results highlight a significant correlation between characteristics of the "ideal" nose as determined by patients, surgeons, and the general population.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans

PurposeTest whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments. Materials and methodsA KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm. ResultsThe plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans. ConclusionA KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Exploring Optimal Signal Plans for Isolated Signalized Intersections with Central Pedestrian Refuges

Exploring Optimal Signal Plans for Isolated Signalized Intersections with Central Pedestrian Refuges

Optimal Planning of Battery Swapping Stations Incorporating Dynamic Network Reconfiguration Considering Technical Aspects of the Power Grid

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.

Optimization model for multibeam sounding line deployment based on genetic algorithm

Multibeam sounding technology is a cutting-edge method used for measuring underwater topography, widely applied across various fields. The actual seafloor terrain often exhibits significant fluctuations, leading to challenges such as insufficient coverage in shallow areas and excessive overlap in deep-water zones, resulting in inefficient operations. Rational selection of line spacing helps improve measurement efficiency and ensures data integrity. In this paper, taking the 2023 National College Student Mathematical Modeling Competition Problem B as a background, we conduct a thorough analysis of the variation patterns of the coverage width of multibeam sounding strips with respect to various parameters. We establish models for ocean depth and coverage width, determining how the strip coverage width changes with distance and the angle between survey lines in different scenarios. Finally, employing genetic algorithms and differential methods, we identify decision variables, objective functions, and constraints to formulate a nonlinear programming model. Through iterative searches, we obtain an optimal sounding line deployment plan that maximizes coverage of the target sea area while keeping the overlap rate below 20%. The calculated missed measurement error is approximately 32.774%, and the overlap rate is around 10.477%, indicating a high level of model accuracy.

Cyclotron and linear accelerator generated scanning proton beams for lung cancer SBRT: Interplay effects and mitigations.

Pencil beam scanning (PBS) proton therapy for moving targets is known to be impacted by interplay effects between the scanning beam and organ motion. While respiratory motion in the thoracic region is the major cause for organ motion, interplay effects depend on the delivery characteristics of proton accelerators. To evaluate the impact of different types of PBS proton accelerators and spot sizes on interplay effects, mitigations, and plan quality for Stereotactic Body Radiation Therapy (SBRT) treatment of non-small cell lung cancer (NSCLC). Twenty NSCLC patients treated with photon SBRT were selected to represent varying tumor volumes and respiratory motion amplitudes (median: 0.6cm with abdominal compression) for this retrospective study. For each patient, plans were created using: (1) cyclotron-generated proton beams (CPB) with spot sizes of σ=2.7-7.0mm; (2) linear accelerator proton beams (LPB) (σ=2.9-5.5mm); and (3) linear accelerator proton minibeams (LPMB) (σ=0.9-3.9mm). The energy switching time is one second for CPB, and 0.005 s for LPMB and LPB. Plans were robustly optimized on the gross tumor volume (GTV) using each individual phase of four-dimensional computed tomography (4DCT) scans. Initially, single-field optimization (SFO) plans were evaluated; if the plan quality did not meet the dosimetric requirement, multi-field optimization (MFO) was used. MFO plans were created for all patients for comparisons. For each patient, all plans were normalized to have the same dose received by 99% of the GTV. Interplay effects were evaluated by computing the dose on 10 breathing phases, based on the spot distribution. Volumetric repainting (VR) was performed 2-6 times for each plan. We compared volume receiving 100% of the prescribed dose (V100%RX) of the GTV, and normal lung V20Gy. Twelve of 20 plans can be optimized sufficiently with SFO. SFO plans were less sensitive to the interplay effect compared to MFO plans in terms of target coverage for both LPB and LPMB. The following comparisons showed results utilizing the MFO technique. In the interplay evaluation without repainting, the mean V100%RX of the GTV were 99.42±0.6%, 97.52±3.9%, and 94.49±7.3% for CPB, LPB, and LPMB plans, respectively. Following VR (2 × for CPB; 3 × for LPB; 5 × for LPMB), V100%RX of the GTV were improved (on average) by 0.13%, 1.84%, and 4.63%, respectively, achieving the acceptance criteria of V100%RX>95%. Because of fast energy switch in linear accelerator proton machines, the delivery time for VR plans was the lowest for LPB plans, while delivery time for LPMB was on average 1 min longer than CPB plans. The advantage of small spot machines was better sparing in normal lung V20Gy, even when VR was applied. In the absence of repainting, proton machines with large spot sizes generated more robust plans against interplay effects. The number of VR increased with decreasing spot sizes to achieve the acceptance criteria. VR improved the plan robustness against interplay effects for modalities with small spot sizes and fast energy changes, preserving the low dose sparing aspect of the LPMB, even when motion is included.

Optimal Capacity Planning of Green Electricity-Based Industrial Electricity-Hydrogen Multi-Energy System Considering Variable Unit Cost Sequence

Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy can promote the accommodation of green electricity. This paper proposes an optimal capacity planning approach for an industrial electricity-hydrogen multi-energy system (EHMES) aimed to achieve the local utilization of RES and facilitate the transition to carbon reduction in industrial settings. The proposed approach models the EHMES equipment in detail and divides the system’s investment and operation into producer and consumer sides with energy trading for effective integration. Through this effort, the specialized management for different operators and seamless incorporation of RES into industrial users can be achieved. In addition, the variations in investment and operating costs of equipment across different installed capacities are considered to ensure a practical alignment with real-world scenarios. By conducting a detailed case study, the influence of various factors on the capacity configuration outcomes within an EHMES is analyzed. The results demonstrate that the proposed method can effectively address the capacity configuration of equipment within EHMES based on the local accommodation of RES and variable unit cost sequence. Wind power serves as the primary source of green electricity in the system. Energy storage acts as crucial equipment for enhancing the utilization rate of RES.

Optimal Investment Strategy Analysis of On-Site Hydrogen Production Based on the Hydrogen Demand Prediction Using Machine Learning

In order to achieve the hydrogen economy and respond to initial hydrogen demand appropriately, a hydrogen production and operation methodology is required to secure the economic feasibility of long-term on-site HRS. This study proposes a novel investment strategy for on-site hydrogen production to meet future hydrogen demand. The optimal investment strategy based on the dual-modal mode of combined autothermal reforming (ATR) and steam methane reforming (SMR) is proposed for hydrogen production using natural gas (NG) as a raw material. To predict hydrogen demand from 2020 to 2030, the machine learning (ML) technique was adopted. R2 and MSE as result using ML were 0.9936 and 6.88×10−5, respectively. In addition, the ATR-SMR hydrogen strategy (ASHS) process was analyzed and compared with the SMR-SMR and ATR-ATR hydrogen strategy (SSHS and AAHS) processes in terms of optimal operation rate, storage tank management, economics, and environmental impacts. The operation rate of proposed hydrogen production processes was determined by the hydrogen demand and storage tank level, and the optimal investment plan to install additional hydrogen process depends on the total amount of hydrogen production. In this study, these results were observed due to the effective combination of the strengths of ATR and SMR. Consequently, the ASHS had the best cost-effectiveness (LCOH at $5.63/kg H2) and environmental friendliness (unit CO2eq emissions at 10.21 kg CO2eq/kg H2 and 1.73 kg CO2eq/kg H2 with CCS). This study includes sensitivity analysis and a comparison of CO2 taxes by the country for three proposed hydrogen production processes. It could contribute to the optimal operation of the on-site hydrogen production system in preparation for future hydrogen demand.

Conditions When the Problems of Linear Programming Are Algorithmically Unsolvable

We study the properties of the constructive linear programming problems. The parameters of linear functions in such problems are constructive real numbers. Solving such a problem involves finding the optimal plan with the constructive real number components. We show that it is impossible to have an algorithm that solves an arbitrary constructive real programming problem.

Research on Multi-Objective Optimization Design of University Student Center in China Based on Low Energy Consumption and Thermal Comfort

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building’s exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs.

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization.

Treatment plan optimization in high dose rate (HDR) brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI).&#xD;&#xD;Approach: The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations.&#xD;&#xD;Main results: MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 seconds. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.&#xD;&#xD;Significance: MOBO-qNEHVI can automatically explore the trade-offs between treatment plan objectives in a patient-specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience.

Research on the trajectory planning and global optimization strategy of cold spraying technique for complex products coating preparation

PurposeThis paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.Design/methodology/approachThe complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.FindingsThe simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.Originality/valueThe proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Mathematical modeling of minimization of electricity consumption by industrial enterprises with continuous production

Relevance. Determined by the importance of minimizing electrical power consumption in industrial enterprises with continuous production, considering the specific characteristics of their technological processes and the requirements to maintain the output volume of their products. Aim. To solve the task of minimizing electrical power consumption based on a mathematical model and gradient method under optimal planning of the production volume of the industrial enterprises with continuous production; to develop a mathematical model for optimal distribution of production over a time cycle (month, quarter, year) across departments, taking into account both simple and functional constraints, derived from the condition of ensuring minimal electrical power consumption in industrial enterprises with continuous production. Methods. When developing the mathematical model for ensuring minimal electrical power consumption while preserving the production volume, classic Lagrange optimization methods were used. To ensure sufficient calculation accuracy, iterative methods were also applied. For the task under consideration, a calculation error margin of ε=0,1 was assumed and established. It is known that the choice of calculation error margin depends on the specifics of the problem at hand and the decision-maker. To verify the adequacy of the developed model, the method of finding the relative extremum of a function of several variables was used. Results. The use of the mathematical model, which takes into account the nature of the technological process and boundary conditions in both simple and integral forms, demonstrated the feasibility of optimal planning of electrical power consumption by the enterprise. The effectiveness of the developed approaches was verified using a metallurgical enterprise as an example of an industrial enterprise with continuous production, in solving the task of minimizing electrical power consumption for products produced during the reporting period. The use of the proposed model allowed for a reduction in annual electrical power consumption by 2.5% while maintaining the same production volume. One of the classic optimization methods – the method of finding the relative extremum of functions of several variables – showed almost identical results upon verification. This serves as further evidence of the adequacy of the proposed model.

Gas turbine prognostics via Temporal Fusion Transformer

Abstract Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

A Simulation–Optimization Model for Optimal Aquifer Remediation, Using Genetic Algorithms and MODFLOW

This paper investigates the optimal remediation process in an aquifer using Modflow 6 software and genetic algorithms. A theoretical confined aquifer has been polluted over a long period of time by unnoticed leakage in a pipeline conveying leachate from an adjacent landfill to a wastewater treatment plant. When the extended leakage and groundwater pollution are discovered, the optimal planning of the remediation strategy is investigated using the pump-and-treat method or/and hydrodynamic control of the pollution. The practical goal is to find the optimal locations and flow rates of two additional pumping wells, which will pump the polluted water or/and control pollution, protecting an existing drinking water pumping well, securing its fully operational mode even during the remediation process with the minimum possible cost, simply represented by the pumped water volume of the additional wells. The remediation process is considered complete when the maximum concentration in the aquifer drops below a certain limit. The Modflow software (handled by the Flopy Python package) simulates the flow field and advective–dispersive mass transport, and a genetic algorithm is used as the optimization tool. The coupled simulation–optimization model, Modflow-GA, complemented by a sophisticated post-processing results analysis, provides optimal and alternate sub-optimal remediation strategies for the decision makers to select from.