Allocation Model Research Articles

Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data

In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.

An improved IPA approach driven by big data and its application to customer satisfaction research of energy-saving appliance

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers.

Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm

This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.

Optimal resource allocation in hierarchical organizations under uncertainty: An interval-valued n-person cooperative game approach

In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n-person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n-person cooperative games, an auxiliary nonlinear programming model and method are developed to solve the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods.

Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.

Task allocation algorithm for distributed large data stream group computing in the era of digital intelligence

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain.

Construction of water resource allocation model based on green supply chain theory

Construction of water resource allocation model based on green supply chain theory

Exploration and Practice of Personnel Allocation Model for County-Level Power Supply Enterprises' Rural Electric Personnel

Exploration and Practice of Personnel Allocation Model for County-Level Power Supply Enterprises' Rural Electric Personnel

CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests

Abstract. The Climate-responsive Land Allocation model with carbon Storage and Harvests (CLASH) is a global, biophysical land-use model that can be embedded into integrated assessment models (IAMs). CLASH represents vegetation growth, terrestrial carbon stocks, and production from agriculture and forestry for different land uses in a changing climate. Connecting CLASH to an IAM would allow the consideration of terrestrial carbon stocks, agriculture and forestry in global climate policy analyses. All terrestrial ecosystems and their carbon dynamics are comprehensively described at a coarse resolution. Special emphasis is placed on representing the world's forests. Vegetation growth, soil carbon stocks, agricultural yields and natural disturbance frequencies react to changing climatic conditions, emulating the dynamic global vegetation model LPJ-GUESS. Land is divided into 10 biomes with six land-use classes (including forests and agricultural classes). Secondary forests are age structured. The timing of forest harvests affects forest carbon stocks, and, hence, carbon storage per forest area can be increased through forest management. In addition to secondary forests, CLASH also includes primary ecosystems, cropland and pastures. The comprehensive inclusion of all land-use classes and their main functions allows representing the global land-use competition. In this article, we present, calibrate and validate the model; demonstrate its use; and discuss how it can be integrated into IAMs.

Research on Wireless Multimedia Big Data Transmission Model Based on Non-linear Algebraic Equation with the Adaptive Scheduling Algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital vision system is a multi-plane information analysis method based on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital reading devices have become an essential part of people's daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the trend is to improve the speed and reliability of computer systems. In this research, we provide a nonlinear model of optimal allocation employing an ant colony management strategy, a genetic operator, and an adaptive scheduling algorithm based on topology (dB) to discover the best scheduling scheme and test its feasibility via simulation. It proposes a viable idea for tackling the practical wireless multimedia massive data transmission model problem by applying the method to the real-time processing process in other sectors with high importance and resilience.

Stroke-volume-allocation model enabling wearable sensors for vascular age and cardiovascular disease assessment

Frequent and unobtrusive monitoring of cardiovascular conditions with consumer electronics is a widely pursued goal, since it provides the most economic and effective way of preventing and managing cardiovascular diseases (CVDs) ─ the leading causes of death worldwide. However, most current wearable and flexible devices can only support the measurement of one or two types of vital signs, such as heart rate and blood oxygen level, due to the lack of physiological models to link the measured signals to cardiovascular conditions. Here, we report a stroke-volume allocation (SVA) model to quantify the cushioning function of arteries and empower nearly all existing cardiac sensors with new functions, including arterial stiffness evaluation, dynamic blood pressure tracking and classification of CVD-related heart damage. Large-scale clinical data testing involving a hybrid dataset taken from 6 hospitals/research institutes (9 open databases and 4 self-built databases from 878 subjects in total) and diverse measurement approaches was carried out to validate the SVA model. The results show that the SVA-based parameters correlate well with the gold-standard measurements in arterial stiffness and blood pressure and outperform the commonly used vital sign (e.g., blood pressure) alone in detecting abnormalities in cardiovascular systems.

Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

Sentiment Analysis Method of Epidemic-related Microblog Based on Hesitation Theory

The COVID-19 pandemic in 2020 brought an unprecedented global crisis. After two years of control efforts, life gradually returned to the pre-pandemic state, but localized outbreaks continued to occur. Toward the end of 2022, COVID-19 resurged in China, leading to another disruption of people’s lives and work. Many pieces of information on social media reflected people’s views and emotions toward the second outbreak, which showed distinct differences compared to the first outbreak in 2020. To explore people’s emotional attitudes toward the pandemic at different stages and the underlying reasons, this study collected microblog data from November 2022 to January 2023 and from January to June 2020, encompassing Chinese reactions to the COVID-19 pandemic. Based on hesitancy and the Fuzzy Intuition theory, we proposed a hypothesis: hesitancy can be integrated into machine learning models to select suitable corpora for training, which not only improves accuracy but also enhances model efficiency. Based on this hypothesis, we designed a hesitancy-integrated model. The experimental results demonstrated the model’s positive performance on a self-constructed database. By applying this model to analyze people’s attitudes toward the pandemic, we obtained their sentiments in different months. We found that the most negative emotions appeared at the beginning of the pandemic, followed by emotional fluctuations influenced by social events, ultimately showing an overall positive trend. Combining word cloud techniques and the Latent Dirichlet Allocation (LDA) model effectively helped explore the reasons behind the changes in pandemic attitude.

Ship Emergency Material Allocation Model Based on NSGA-II Algorithm

The process of ship material resupply is studied to illustrate the importance of the material resupply process; the common emergency material scheduling problem is compared with the material resupply problem to analyze the similarities and differences; analogous to the part of the emergency material scheduling problem to design the material resupply algorithm, and finally, a material resupply mission is taken as an example to use this paper's algorithm for solving the model and verifying the feasibility of the algorithm.

Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language

The purpose of the study is to solve an extreme mathematical problem—semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the latent Dirichlet allocation model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the latent Dirichlet allocation model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.

Optimal fissioning strategies of social insects with respect to colony dynamics and nest founding probability

Social insects demonstrate two fundamentally different modes of reproduction, independent colony foundation (ICF) by single fertilized queens or dependent colony foundation (DCF) by fissioning of existing colonies into two or several new colonies (swarms). In some species, both reproductive modes occur in parallel. The benefits and disadvantages of DCF vs. ICF have been widely discussed and been subject to empirical studies, but a formal theoretical treatment of the topic is still incomplete. Taking honey bees as example, we provide a resource allocation model of colony dynamics to analyze the ecological conditions under which DCF may be favored over ICF. Using mathematical and numerical methods, we show that it critically depends on the survivorship function linking swarm size to the probability of swarm establishment whether ICF or DCF results in a higher output of surviving new colonies. Because building larger swarms requires larger inter-swarm time intervals, DCF can only be a better strategy if this disadvantage is over-compensated for by a strong size-dependent swarm survivorship and survival of single queens is very low. Colony growth rate has no effect on this decision and the impact of maximum possible colony size is negligible. Further, there is a discontinuity in the optimal swarm size, so that either a swarm size of 1 (ICF) is the best strategy, or emitting swarms of considerable size (DCF). Consequently, a direct evolutionary transition from ICF to DCF appears unlikely and may have been triggered by selective pressures promoting movement of complete nests or distributing single colonies over several nests (polydomy).

Deep reinforcement learning-based joint optimization model for vehicular task offloading and resource allocation

Deep reinforcement learning-based joint optimization model for vehicular task offloading and resource allocation

Dynamic selection and order allocation of resilient suppliers based on improved fuzzy multi-criteria decision method

ABSTRACT A complex supply chain can improve the economic benefits of an enterprise to a certain extent, whereas its vulnerability also occurs. How to select resilient suppliers to improve enterprise risk resistance has become hot issue in supply chain management. Furthermore, due to the complexity of the supply chain and the instability of the market, the supplier selection in different periods and customer demands is dynamic. We use the best-worst approach to model the initial selection of suppliers taking into account risk factors. Based on this result, our study applies the fuzzy multi-criteria decision method to establish a multi-objective model that aims to solve the problem of dynamic supplier selection considering resilient criteria. We apply the improved wolf pack algorithm to measure the optimal order allocation strategy between profit and pollution. The supplier selection and order allocation of new energy vehicle manufacturing companies are used as a case study for verification. The results show the multi-objective model of supplier selection considering risk and resilience factors is feasible, and the dual-objective model of supplier selection and order allocation considering different demands in different periods are effective. Compared with the original algorithm, the improved Wolf pack algorithm has better comprehensive performance.

Classification and Prediction of Food Safety Policy Tools in China Based on Machine Learning

Governments use policy interventions to mitigate food safety risks. Despite its crucial role, empirical studies evaluating the effectiveness of China's food safety policy tools are scarce. Drawing on a dataset encompassing 11,236 food safety policy texts from 2005 to 2021 and the incidence of problematic food products in the Eastern, Central, and Western regions of China, this study employs Latent Dirichlet Allocation (LDA) and eXtreme Gradient Boosting (XGBoost) models to facilitate the classification of policy tools and forecast the effectiveness of policy combinations. The study reveals that (1) local governments have gradually become an important supplementary maker of food safety policies, and have issued an increasing number of policy tools year by year. (2) Mandatory policy tools are predominant in number and have the highest legal hierarchy and authority levels, followed successively by guiding policy and voluntary policy tools. (3) Mandatory policy tools demonstrated the most effective intervention results, followed successively by guiding policy and voluntary policy tools. (4) The forecast analysis reveals that combinations of policies within high-growth frameworks and those driven by mandatory regulations emerge as the most effective. Therefore, the balance of policy tools in terms of type, effectiveness, and quantity, as well as their applicability in different situations, should all be taken into account.

Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)

Abstract. Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the Soil Enzyme Steady Allocation Model (SESAM) at the decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM-degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize the total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. These findings increase our confidence in conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. Further, based on these findings we formulated the constrained enzyme hypothesis. This hypothesis provides a complementary explanation why some substrates in soil are preserved over decades, although they are often decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights, and new hypotheses. This is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric C–N–P dynamic models that are aimed to study decadal processes beyond the ecosystem scale.