Optimal Planning Research Articles

Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.

Integrated optimization of crude oil procurement planning and blending scheduling for property stabilization

Crude oil procurement and blending are key processes in refinery production. When solving the integrated optimization problem of simple oil procurement and blending, the following issues are mainly considered: how to establish an integrated optimization model of crude oil procurement and blending process under different time scales to minimize the cost of procurement under the premise of ensuring the stability of the properties of blended crude oil. The paper proposes an integrated optimization model, takes cost minimization as the model's objective function, and provides stability through the yield and property constraints of blended crude oil. Then, the paper describes the procurement process by an event-based representation and the blending process by a continuous event representation and proposes a hybrid event-time-based representation to describe the model. Finally, this paper verifies the model's effectiveness in solving real production problems through case simulations.

Defining Strategic Incentives as the Basis for the Company's Long-term Success

Successful business foresees effective disposal and management of scarce resources, which is related to the proper planning of the company's activities, the development of a coherent plan of decisions and actions that should be the basis for achieving the company's goal. The formation of the company's goal depends on the determination of strategic directions and the incentives that shape certain strategic behavior of the firm. The paper "Determining strategic incentives as the basis of the company's long-term success" is dedicated to this issue. The paper discusses the importance of business strategy development and determining factors. Interdependence of organizational structure and business strategy, as well as consistent ways of developing strategic decisions are analyzed. The paper discusses the importance of business strategy development and determining factors. Interdependence of organizational structure and business strategy, as well are analyzed consistent ways of developing strategic decisions. There is emphasized that a successful business is the effective disposal and placement of scarce resources, which is related to the correct planning of the company's activities, the development of a consistent plan of decisions and actions that should be the basis for achieving the company's goal. It is concluded that the public purpose of the company specifies its status, determines the efforts of all organizational levels to achieve the main strategic goal for which the organization was created. Development of business strategy and correct formation of strategy allows the company to: • to develop a concrete business plan; • to define specific business goals; • on the basis of the developed optimal action plan, to focus on increasing efficiency; • to establish a control mechanism of achievable goals and results. Keywords: Company mission and vision, business strategy, organizational structure; strategy incentives;

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.

Multi-dimensional management of smart distribution networks: Comparative analysis of box and polyhedral methods for modeling uncertainties

By using advanced automation services, distribution systems have been transformed from passive networks to smart and active infrastructures. In this regard, due to the complex interaction of different entities, operators have faced multifaceted challenges in securely exploiting the electric power system. However, optimal planning and operation of smart distribution networks have made the reach of a more stable and flexible electricity network possible. To this end, this study proposes a new operational model of effectively integrating demand response and battery energy storage systems (BESS) to reduce the power exchange with the sub-distribution substation. To optimize the BESS charging and discharging cycles, the degradation limit is considered for its optimal operation in the electric system. Herein, locally controllable generators are taken into account to support reactive power as well as assist with the flattening of the voltage profile. This paper considers emission cost in the performance of local controllable generators (LCG), which adds a critical layer of optimization to distribution network management. This enables the system to balance grid stability, affordability, and environmental responsibility. Moreover, box and polyhedral methods have been investigated and compared for effective and uncertain-aware optimization of the system. Findings show that the polyhedral method provides more robust and confident results than the box method.

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.

Feasibility and constraints of Bragg peak FLASH proton therapy treatment planning.

FLASH proton therapy (FLASH-PT) requires ultra-high dose rate (≥40 Gy/s) protons to be delivered in a short timescale whilst conforming to a patient-specific target. This study investigates the feasibility and constraints of Bragg peak FLASH-PT treatment planning, and compares the in silico results produced to plans for intensity modulated proton therapy (IMPT). Bragg peak FLASH-PT and IMPT treatment plans were generated for bone (n=3), brain (n=3), and lung (n=4) targets using the MIROpt research treatment planning system and the Conformal FLASH library developed by Applications SA from the open-source version of UCLouvain. FLASH-PT beams were simulated using monoenergetic spot-scanned protons traversing through a conformal energy modulator, a range shifter, and an aperture. A dose rate constraint of ≥ 40 Gy/s was included in each FLASH-PT plan optimisation. Space limitations in the FLASH-PT adapted beam nozzle imposed a maximum target width constraint, excluding 4 cases from the study. FLASH-PT plans did not satisfy the imposed target dose constraints (D95% ≥ 95% and D2%≤ 105%) but achieved clinically acceptable doses to organs at risk (OARs). IMPT plans adhered to all target and OAR dose constraints. FLASH-PT plans showed a reduction in both target homogeneity (p < 0.001) and dose conformity (non-significant) compared to IMPT. Without accounting for a sparing effect, IMPT plans were superior in target coverage, dose conformity, target homogeneity, and OAR sparing compared to FLASH-PT. Further research is warranted in treatment planning optimisation and beam delivery for clinical implementation of Bragg peak FLASH-PT.

Integrated optimization of production planning and electric trucks charging and discharging scheduling

The majority of supply chains in any manufacturing industry rely on heavy trucks for their flow of material from suppliers to end-users. Transportation by these heavy trucks causes greenhouse gas emissions detrimental to the environment. To cope with challenges caused by environmental issues and energy crises, replacing heavy trucks with electric trucks seems to be a viable solution. They can be considered as an energy resource that supports grid-to-vehicle and vehicle-to-grid, enabling efficient energy management for smart manufacturing systems and supply chains. Proper scheduling of charging and discharging activities of electric trucks also supports the penetration of renewable energy sources with volatile energy generation. This paper encourages sustainable practices and proposes a mixed integer programming model for the simultaneous optimization of production planning and transportation scheduling of electric trucks in a smart manufacturing system. The uncertainty sources considered in this system include power generation of renewable energy, transportation patterns, and customer demand for products. The results show that utilizing electric trucks in smart manufacturing with real-time pricing, save up to 11% of the total cost compared to traditional manufacturing systems without electric trucks and renewable energies. This integration also increases the efficient utilization of renewable energies such as wind and solar powers. The savings decrease to 4% and 8% under flat and time-of-use electricity pricing strategies. The results also highlight the complexity of setting utilization level of electric trucks and renewable energy sources into well-implemented sustainability standards.

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.

Intercity customized passenger transportation service plan optimization design with spatial-temporal accessibility based on BIRCH-VNS

Intercity customized passenger transportation service plan optimization design with spatial-temporal accessibility based on BIRCH-VNS

Design and study of mine silo drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy

Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2–7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section—water level—rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.