Average Utilization Rate Research Articles

Physical education teaching scheduling technology based on chaotic genetic algorithm

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

Analyzing Passenger Flows in an Airport Terminal: A Discrete Simulation Model

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario.

Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation

As electric vehicles (EVs) are cross-domain entities with dual attributes related to mobile loads and transportation, their travel modes and charging behaviors are stochastic and uncertain in time and space. Large-scale uncoordinated EV charging may cause problems such as the congestion of charging stations (CSs) and overloading of distribution networks (DNs). To address these challenges and the current research gap, and to optimize the spatial-temporal distribution of EV charging loads, a vehicle-road-network collaborative operation framework is constructed in this paper, and a two-stage spatial-temporal scheduling method for EV charging is proposed. In the first stage, real-time traffic and CSs information are introduced to improve Dijkstra's algorithm, and the value preferences of different types of users are considered to further improve the objective function of the algorithm as well. Thus, a dynamic personalized charging navigation model based on improved Dijkstra's algorithm is proposed to provide real-time guidance allowing users to choose their travel paths and CSs. In the second stage, we consider the charging demands of EV users and the operation status of the DN, and an orderly charging model is established to minimize the peak/valley load difference of the DN. Through comprehensive comparisons of experimental results, the variance in the average utilization rates of CSs and the peak valley difference for the DN are reduced by 80.77 % and 16.91 %, respectively. In addition, the time and economic costs of users can be reduced by 9 min and 7.9 RMB, respectively, thereby reducing travel and toll costs and increasing their willingness to participate in scheduling. The simulation experiment proves that the proposed method achieves multi-agent collaborative optimization of EV users, CSs, and DN.

An optimal configuration scheme of virtual capacity expansion transformers considering short-term emergency load capacity

Abstract As critical equipment for electrical energy transmission and voltage transformation, power transformers are fundamental to the power grid’s safe, reliable, and economical operation. As urbanization accelerates, the contradiction between the lag in constructing power transformers and the urgent need for short-term capacity expansion becomes increasingly prominent. Existing capacity expansion methods present challenges such as high costs and significant space requirements. In order to solve the problem, this paper proposes an optimized configuration scheme for virtual capacity expansion transformers, which considers the short-term emergency load capacity. Firstly, an annual average cost model of a virtual capacity expansion transformer is proposed. Then, the optimal configuration scheme is established with the highest average effective utilization rate of the transformer and the lowest comprehensive economic cost index as the optimization goal. Finally, a 220/110 kV urban power grid system is simulated to prove the advantages of the proposed scheme compared with the existing scheme.

Longitudinal analyses (2018-2022) of the COVID-19 impact on Swiss pediatric healthcare utilization

Abstract Background The impact of COVID-19 on healthcare utilization in the early pandemic is well documented. However, studies on changes in both primary and tertiary pediatric healthcare utilization that considered data beyond the first pandemic year is lacking. Methods National health insurance data (primary care) and pediatric emergency department data (tertiary care) from Jan. 2018 - June 2022 and on 0-18-year-olds were analyzed with interrupted time series. The first lockdown was used to differentiate between pre-pandemic and pandemic periods. Results Several healthcare services yielded significantly lower average utilization rates during the pandemic vs. the pre-pandemic period, particularly in the youngest age groups. In primary care, the relative utilization, compared to the pre-pandemic mean, dropped in regards to regular (0-5y: 0.760 [0.644-0.896]; 6-10y: 0.850 [0.742-0.974]), well-child (0-5y: 0.871 [0.765-0.992]), and urgent visits (0-5y: 0.638 [0.500-0.813]; 6-10y: 0.810 [0.665-0.985]), and for the vaccination against measles/mumps/rubella (MMR; 0-5y: 0.841 [0.729-0.971]). Similarly, in tertiary care, this pattern emerged for non-urgent (Zurich: 0.55 [0.482-0.627]; Geneva: 0.526 [0.435-0.636]; Ticino: 0.502 [0.436-0.578]) and urgent visits (Zurich: 0.573 [0.498-0.659]; Geneva: 0.487 [0.375-0.632]; Ticino: 0.676 [0.57-0.802]). The utilization rates of most services dropped markedly after the lockdown and recovered to some extent over the pandemic phase. However, no recovery was found for the MMR-vaccination. In primary care, higher average utilization rates during the pandemic were identified for telephone consultations (0-5y: 1.394 [1.218-1.594]; 6-10y: 1.547 [1.387-1.726]; 11-15y: 1.578 [1.430-1.741]; 16-18y: 1.831 [1.614-2.076]). Conclusions The pandemic’s effect on utilization seemed to have occurred temporarily, but it cannot be ruled out that some children have not fully caught up on preventative measures (well-child visits and MMR-vaccinations). Key messages • Pediatric healthcare utilization dropped considerably immediately after the lockdown, with the most pronounced decrease observed in the youngest children. • The heterogenous recovery over the pandemic period points to the necessity to continue to monitor utilization rates, catch-up visits, and vaccination statistics.

Vaccine Logistics Management Information System at Public Health Facilities in Amhara Region, Ethiopia: A Performance Evaluation.

This study aimed to evaluate the performance of vaccine logistics management information systems at public health facilities in Amhara Region, Ethiopia. An institution-based concurrent explanatory mixed design was used at public health facilities in Amhara Region, Ethiopia. In total, 102 health facilities were selected using the multi-stage, stratified, random-sampling proportionate-to-size method. Data were collected using a structured data abstraction checklist from vaccine logistics management information system records. Additionally, experiences of Expanded Program for Immunization focal persons were gathered through interviewer-administered questionnaires. Among the 102 health facilities surveyed, 99 (97.1%) provided vaccination services during the study period. On the day of the visit, the average availability and utilization rates of 15 vaccine logistics management information system forms were 44.1% and 40.5%, respectively. The mean inventory accuracy for tracer vaccines stood at 58.3% ± 45.1%. The overall average accuracy percentages for urban and rural vaccine request form reports were 73.4% and 69.6%, respectively. The performance of vaccine logistics management information systems in these public health facilities was suboptimal. Key issues identified include inconsistent use of recording forms, shortages of reporting forms, lack of digitalization, poor data quality, and a shortage of qualified staff. These findings underscore the urgent need for standardization of the vaccine logistics management information system and enhancement of data quality.

Expedited program and utilization for anticancer drug approval in China and the United States

Objective: To systematically summarize and comparatively analyze the development, establishment and usage of oncology drugs speedy review approaches in China and in the United States between 2012 and 2021. Methods: Based on National Medical Products Administration (NMPA) and Food and Drug Administration (FDA) websites, the development and current status of the speedy review approaches were consulted and summarized. Approved oncology drugs in China and in the United States (87 in China, 118 in the United States) over the past decade were analyzed using chi-square test for group comparison. Results: Five speedy approaches have been established in China and in the United States, three of which are the same, priority review, conditional approval or accelerated approval and breakthrough therapy. The rest two are special review and approval, special examination and approval in China, and fast track and real-time oncology review in the United States. Compared to the United States, speedy review approaches in China set up late (1992 vs. 2005). The overall utilization rates of the oncology drugs speedy review approaches were similar between the China and United States (90.8% vs. 92.4%, P=0.800) in the previous 10 years, and priority review have highest utilization rates in both China and the United States without significant group difference (77.0% vs. 82.2%, P=0.381); relatively low utilization rates of conditional approval (31.0% vs. 44.9%, P=0.041) and breakthrough therapy (2.3% vs. 50.0%, P＜0.001) were seen in China. 52.9% of new drugs applied for special examination and approval in China and 40.7% of new drugs applied for fast track in the United States. Overall, the priority review both in China and the United States are stable, with a similar average annual utilization rate (84.8% vs. 83.7%); accelerated approval and breakthrough therapies in the United States fluctuate wildly, but the situation is tending towards stability in the last 3 years. Conclusions: Both China and the United States have established a relatively complete accelerated review system, with an overall utilization rate over 90%; China's accelerated review started late, although the overall utilization rate is close to that of the United States. The utilization rates of conditional approval and breakthrough therapy are still relatively low. Flexible usage of speedy review approaches, gaining regulatory recognition to use alternative endpoints, achieving real-time review and guidance are keys to accelerate new drug development in China.

Optimizing the Regulation of Nursing in the Asian Context: Development and Application of a Legal Framework

PurposeIt is essential to regulate nursing to protect the population's health. As regulation constantly changes in response to societal trends, periodic reviews of nursing regulations become imperative. Therefore, we developed a legal framework by extracting essential elements for nursing regulation and explored its potential application. MethodsThis study consisted of two parts. First, the legal framework for nursing regulation was developed through reviewing literature that mentions the content that can be included in nursing regulations, and through a content validity assessment by five experts. Second, this legal framework was applied to the nursing laws of China, Hong Kong, Japan, and Taiwan to confirm the suitability of the framework. ResultsIn the first part of the study, the legal framework for nursing regulation consisting of seven categories (purpose, definitions, standards for practice, license acquisition and registration to practice, regulatory body, protection of the legal authority of nurses, offences/penalties and disciplinary procedures) and 17 items was developed. As a result of applying this framework to nursing laws in four countries, the average utilization rate for all 17 items was 68.4%. The matching scores between the framework and the law were over 60% for all four laws. ConclusionRegulations, especially in the form of legislation, must be carefully considered because laws involve enforcement and potential penalties. This study is significant for identifying essential nursing regulation elements and offering a practical reference tool, expected to be widely utilized in future nursing policy and regulatory research.

A high-precision and near-zero residue laser-controlled gel propellant

Micro-nano satellites, with their advantages of small size, low power consumption, short development cycles, and capability for formation flying and networking, play a significant role in scientific research, national defense, and commercial applications. However, currently developed micro-nano satellites globally possess very limited maneuverability and lack effective attitude and orbit propulsion control. Solid propellants can offer some advantages such as high energy density and low cost, and they are extensively used in micro-and nano satellite propulsion systems. However, most of these propellants exhibit poor controllability and produce massive amounts of residual by-products. Laser-controlled gel propellants have been confirmed to be the most effective solution to address these issues in this study. The present study exhibits the preparation of ADN-based laser-controlled gel propellants using the freeze–thaw method and their controllable combustion characteristics under laser irradiation. The results show that the ignition delay time of gel propellants decreases from 93.0 ms to 34.6 ms, the extinction delay time is stable at around 2 ms, and the average burning rate increases from 0.6 mm/s to 1.5 mm/s under continuous laser irradiation with the power density ranging from 0.6 to 1.3 W/mm2. The average utilization rate of gel propellant is as high as 99.2 %. Upon testing, the residual solid matter was identified as unreacted propellant that had not been irradiated by the laser. Laser-controlled gel propellants represent a new type of energetic material that can be modulated to control its ignition, extinction, and burning rate. These capabilities overcome the challenges associated with the inability of traditional chemical propellants to extinguish and restart, thereby fulfilling the propulsion system requirements of micro-nano satellites.

Transition from machine intelligence to knowledge intelligence: A multi-agent simulation approach to technology transfer

Abstract The traditional machine intelligence system lacks deep understanding and reasoning ability. This study took the automatic driving system in multi-agent as an example to bring higher-level intelligence and decision-making ability to automatic driving through knowledge intelligence. It obtained real-world geographic information data from OpenStreetMap, preprocessed the data, and built a virtual environment. The inception model was used to identify information in environmental images, and the knowledge information of traffic regulations, road signs, and traffic accidents was expressed to build a knowledge map. The knowledge related to automatic driving was integrated, and automatic driving training was carried out through the reward mechanism and the deep Q-network (DQN) model. About 13 kinds of traffic situations were set up in the virtual environment, and the traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agent were compared. The results show that the average number of accidents in 100,000 km of traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agents was 3 and 1.4, and the average number of violations in 100,000 km was 4.3 and 1.8, respectively. The average graphics processing unit (GPU) utilization rate of knowledge fusion autonomous driving in 13 virtual environments was 75.9%, and the average peak GPU utilization rate was 96.1%. Knowledge fusion of autonomous driving multi-agents can effectively improve the safety of autonomous driving and enable autonomous driving multi-agents to have a higher level of decision-making ability.

Data-driven analysis of optimal repositioning policy in bike sharing systems

In this study, we analyze the impact of optimal repositioning policy on the efficiency of bike sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of a sharing system. We further extend this model to consider demand uncertainty under a stochastic setting. The proposed model is then implemented on the real-world bike-sharing data of New York City, Toronto, and Vancouver. After finding the optimal repositioning policies, we analyze the effect of repositioning on the profit, fulfilled demand, number of required vehicles, and utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of a sharing system under these performance measures. In particular, our analyses show that 32% more profit can be obtained and up to 41% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size by up to 61% and increase the average utilization rate of the vehicles by up to 21% by employing repositioning.

The adaptability and irrigation constraints analysis of the WOFOST model for grain production in the Songhua River Basin.

The Songhua River Basin, a vital grain-producing area in China, faces challenges due to the uneven distribution of water resources and the intensive water demands of agriculture. To enhance agricultural development and effectively manage water scarcity, it is essential to identify the water-saving potential of major staple crops - corn, wheat, and rice. This study enhances the World Food Studies (WOFOST) model by refining the day of year for the developmental vegetative stage (DVS), thereby improving the representation of phenological stages for spring maize, spring wheat, and rice within the model. This refinement offers a detailed analysis of the potential and rainfed yields. The results from the modified WOFOST model show promising simulation outcomes for the biomass and yield of maize, wheat, and rice, with Nash-Sutcliffe efficiency (NS) and index of agreement (IoA) values all exceeding 0.7. An analysis of photothermal potential yields (Yp) and rainfed yields (Yr) revealed minimal differences in yields for spring maize and rice across various rainfall frequencies. Specifically, the average photothermal utilization rates (LTs) are 93.57% for maize and 85.25% for rice. In contrast, the rainfed yield for wheat is lower than its photothermal yield, with an LT of 43.66%. These findings suggest that in the Songhua River Basin, maize and rice offer greater potential for water conservation compared to wheat. It is recommended to judiciously reduce irrigation during the growing seasons of spring maize and rice to help alleviate agricultural water use pressures. © 2024 Society of Chemical Industry.

Training mode of university dance performers based on sequential quadratic programming algorithm

Dance performance is an art form, which needs to cultivate students’ dance skills, artistic accomplishment and stage performance ability. Sequential quadratic programming algorithm is an optimization algorithm that can be used to solve complex optimization problems. In this paper, Sequential Quadratic Programming (SQP) is applied to explore the training mode of dance performers in colleges to help dance performers develop the optimal training plan and program. Aiming at the problems existing in the training mode of dance performance talents in colleges, this paper put forward an optimization method based on SQP algorithm, and implemented its optimization scheme in actual colleges. In the planning of dance performance talent training mode, particle swarm optimization (PSO) is used to optimize SQP algorithm, so that it can have higher planning efficiency. This paper studied the goal and index system of the training of dance performance talents in colleges, generated a personalized training program, and further improved the scientific and practical effectiveness of the training mode. This paper investigated the current situation of dance performance talent training in several dance schools in a certain province of China. The survey data include practical curriculum planning, teachers’ teaching philosophy and teaching content. Combined with SQP algorithm, the teaching and training program is optimized. After evaluation, it can be concluded that the SQP algorithm optimized by PSO shows good stability and accuracy. It can calculate the optimal solution of the cultivation scheme, and when calculating the optimal solution, the running time of the Central Processing Unit (CPU) was only 5.6 s, which can further improve the efficiency of the planning. Finally, through the satisfaction and resource utilization test, it can be found that the number of people who are very satisfied with the teaching content of the dance performance talent training program optimized by SQP increased from 38.4% to 52.4%. After optimization, the average utilization rate of teaching resources reached 88.1%. It can be concluded that SQP algorithm can provide scientific basis for dance education institutions to improve the training mode of dance talents. This can help dance education institutions better improve the training mode, and improve the overall quality and dance skills of dance students.

Optimizing tool life in SiCp/Al composites milling with acoustic emission analysis: A comprehensive monitoring and implementation strategy

The machining of SiCp/Al composites is hampered by tool wear monitoring challenges. SiCp/Al composites are used in industries like aerospace where tool wear can lead to workpiece loss, necessitating real-time monitoring. Acoustic emission (AE) sources during machining are complex due to SiC particles. This paper attempts to isolate AE frequencies for plastic deformation and silicon carbide particle behavior through scratch experiments at different depths. The relationship between signal energy distribution and tool wear during milling is analyzed using the wavelet packet method. The frequency band correlating best to tool wear is identified outside the scratch experiment's frequency range. A monitoring strategy is proposed based on this, and the effect of machining parameters on AE monitoring is analyzed, with refinements to the monitoring model. The proposed strategy's effectiveness has been validated through experimental trials, resulting in an average tool utilization rate of 85 % during milling.

Load Balancing Algorithm of API Gateway Based on Microservice Architecture for a Smart City

Abstract As the entrance of the computer systems, the API gateway is an indispensable part of the microservice architecture. To realize the load balancing of API gateway, this paper studies the load balancing algorithm of the API gateway based on the microservice architecture. In doing this, we analyze the microservice architecture level from the data layer, the basic layer, and other levels, take the container cloud as the carrier of the microservice architecture, combine it with the client and API gateway, and design the API gateway based on the microservice architecture. We then judge whether the microservice identifier in the request source of the API gateway client is included in the API gateway routing table and determine the service cluster to which the microservice belongs according to the microservice identifier. After retrieving the qualified backend microservice container list according to the service cluster information, it adopts a load balancing algorithm based on dynamic weight, takes central processing unit (CPU) utilization and memory utilization as parameters to evaluate the resource load of microservers, uses an extreme gradient lifting model to predict CPU utilization and memory utilization, calculates the weight of microservers based on the prediction results, selects the microserver with the highest weight value to make API gateway service requests, and initiates API gateway service calls to specific backend microservice containers, thereby completing the load balancing of the API gateway. The experimental results show that the average load balancing degree of the algorithm is about 95 %, the average network resource utilization rate is as high as 89 %, and the algorithm execution time is short.

POTENTIAL AND LEVEL UTILIZATION OF SKIPJACK (Katsuwonus pelamis) IN THE BUNGUS OCEAN FISHING PORT WEST SUMATERA PROVINCE

The skipjack (Katsuwonus pelamis) in Bungus Ocean Fishing Port has been evaluated for its sustainable potential using the surplus production approach method. The findings indicate a strong alignment with the Fox approach model. The estimated sustainable potential for skipjack is 471 tonnes per year, with an ideal effort of 45 trips annually. The analysis indicates a moderate utilization status, with an average skipjack utilization rate of 62.76%. It is evident that around 37.24% of its maximum sustainable potential, equivalent to 154.74 tonnes per year, is still unexplored. In addition, the catch rate of skipjack is relatively high at 96.14%, indicating that there is still some untapped potential, with 3.86% of the optimal fishing effort or two trips per year remaining unused. As a result, it is recommended that a total allowable catch of 341 tons be set in order to ensure the long-term sustainability of skipjack harvesting in the area.

Provider perceptions of barriers to biosimilar utilization in community oncology practices

BackgroundBiosimilars reduce the burden of cost on patients and payers, and so doing, increase access to life-saving care. However, biosimilar uptake in the US has been inconsistent. ObjectivesThis study assessed provider perceptions of barriers to biosimilar use and their relationships to utilization rates in a large, national oncology network and examined if perceptions differed by demographic and practice characteristics. MethodsA 28-item survey was administered to 400 network physicians, pharmacists, nurses, and administrators, spanning 25 provider groups, and measured 1) barriers to use categorized into 4 subscales—payer-related, provider-related, operational, and patient-related, using a Likert scale ranging from Never (1) to Always (5); and 2) demographic and practice characteristics. Utilization rates were assessed using aggregated patient-level drug administration data found in the electronic health record system. Descriptive and inferential statistics were used to describe responses and assess relationships between variables. ResultsA total of 46 responses were analyzed, with a response rate of 11.5%. Most respondents were female (55.6%), physicians (52.2%), with over 6 years of experience (67%). A majority worked in practices participating in the Oncology Care Model (86.7%) and received continuing education on biosimilars (84.8%). Overall scale score was moderately low (mean=2.31), indicating low levels of perceived barriers. The lowest subscale score was operational barriers (mean=2.21), while payer-related barriers was the highest (mean=2.78). Perceptions of barriers did not differ based on demographic and practice characteristics. The average biosimilar utilization rate was 66.2%, with practices in the West administering biosimilars most frequently (71.8%). Utilization was not impacted by perceptions of barriers. ConclusionPerceived barriers to biosimilar utilization were not common and not associated with utilization. Infrequent impediments to utilization may be associated with network-wide emphasis on continuing education and a value-based care environment. Future research should consider other practice- and patient-level factors that may impact biosimilar utilization.

The end-of-life power battery recycling & remanufacturing center location-adjustment problem considering battery capacity and quantity uncertainty

The booming electric vehicle market has led to an increasing number of end-of-life power batteries. In order to reduce environmental pollution and promote the realization of circular economy, how to fully and effectively recycle the end-of-life power batteries has become an urgent challenge to be solved today. The recycling & remanufacturing center is an extremely important and key facility in the recycling process of used batteries, which ensures that the recycled batteries can be handled in a standardized manner under the conditions of professional facilities. In reality, different adjustment options for existing recycling & remanufacturing centers have a huge impact on the planning of new sites. This paper proposes a mixed-integer linear programming model for the siting problem of battery recycling & remanufacturing centers considering site location-adjustment. The model allows for demolition, renewal, and new construction options in planning for recycling & remanufacturing centers. By adjusting existing sites, this paper provides an efficient allocation of resources under the condition of meeting the demand for recycling of used batteries. Next, under the new model proposed in this paper, the uncertainty of the quantity and capacity of recycled used batteries is considered. By establishing different capacity conditions of batteries under multiple scenarios, a robust model was developed to determine the number and location of recycling & remanufacturing centers, which promotes sustainable development, reduces environmental pollution and effectively copes with the risk of the future quantity of used batteries exceeding expectations. In the final results of the case analysis, our proposed model considering the existing sites adjustment reduces the cost by 3.14% compared to the traditional model, and the average site utilization rate is 15.38% higher than the traditional model. The results show that the model has an effective effect in reducing costs, allocating resources, and improving efficiency, which could provide important support for decision-making in the recycling of used power batteries.

Modeling and simulation analysis of optimal layout scheme of aviation logistics park based on genetic algorithm

AbstractAs an important component of modern logistics industry, aviation logistics parks address the problems of unreasonable layout and low efficiency in traditional planning and construction methods. The study first constructed a grid model for optimizing the layout of functional areas in the aviation logistics park, and selected the layout scheme with the highest comprehensive correlation as the optimal planning. At the same time, three important model parameters were determined: prediction of cargo throughput, size of functional areas in the park, and comprehensive correlation values between functional areas. Subsequently, in response to the optimization problem of functional area layout in aviation logistics parks, a cosine adaptive genetic algorithm based on adaptive reversal operation was introduced to solve the model. According to the findings, the research mention algorithm has an average utilization rate of up to 69.64% in the international freight zone, which is a 12.38% improvement over the conventional genetic algorithm. Additionally, it has an average domestic cargo area utilization rate of 67.93%, 11.24% greater than the genetic algorithm. This demonstrated that the functional area layout scheme of the aviation logistics park produced by examining the suggested algorithm is extremely viable and offers new ideas and techniques for the planning and designing of aviation logistics parks.

Dynamic feedback algorithm based on spatial corner fitness for solving the three-dimensional multiple bin-size bin packing problem

To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize container space utilization rate and cargo load balance, minimize container usage costs, and incorporates some practical constraints. On this basis, we propose a novel dynamic feedback algorithm based on spatial corner fitness (SCF_DFA) to solve this model, which consists of three stages. Specifically, Stage 1 employs a heuristic algorithm based on spatial corner fitness to optimize the search of the remaining spaces. Stage 2 employs a container type selection algorithm to dynamically adjust and optimize container types. Stage 3 uses an improved genetic algorithm to improve the quality of the solutions of the first two stages. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances, and apply it to solve the real-life instances for the 3D-MBSBPP. The results show that the proposed algorithm can make the average container space utilization rate reach 85.38%, which is 1.48% higher than that of baseline method, while the loading results obtained are more balanced, indicating the advantages of the SCF_DFA in solving 3D-MBSBPP. Furthermore, we conduct ablation experiments to confirm the effectiveness of each component within the algorithm.