Optimal Allocation Model Research Articles

生鲜农产品冷链集货车辆路径与货物配载联合优化研究

As a bridge connecting agricultural production and consumption, the circulation of agricultural products has the function of connecting supply and demand, guiding production and promoting consumption. However, the development of rural logistics in China is slow, and most logistics centers still rely on experience to plan the pick-up vehicle routings, resulting in long transport time and high cost. In order to improve the efficiency of pick-up and reduce transportation costs, a joint optimization model of cold-chain pick-up vehicle routing and cargo allocation for fresh agricultural products was proposed in this study. Soft time window constraint and three-dimensional loading constraints were considered, and the lowest pick-up cost was used as optimization goals in this model. In addition, adaptive large neighborhood search algorithm (ALNS) and heuristic depth-first search algorithm (HDFS) were combined to solve the model. A case study of Kunming International Flower Auction Center was conducted to compare the schemes of pick-up vehicle routing before and after optimization. Results demonstrate that the pick-up cost after optimization decreases by 9.6 %, the number of vehicles decreases by one, the total volume utilization rate of vehicles increases by 23 %, and the total load utilization rate of vehicles increases by 15 %. This study provides a model reference and solution method for enterprise operators to formulate schemes of pick-up vehicle routing quickly and reasonably.

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Collaborative optimization model and algorithm for airport capacity and traffic flow allocation.

How to efficiently utilize the existing airport capacity without physical expansion and considerable economic inputs to meet air traffic needs is one of the important tasks of air traffic management. To improve the efficiency of capacity utilization, it is necessary to find the actual airport capacity properly. In this work, taking Shuangliu International Airport as an example, a methodology for capacity estimation is proposed that combines the empirical method with an analytical approach that uses historical performance data from the airport to construct a capacity envelope to approximate the airport's actual capacity to the greatest extent, establishes a collaborative optimization model that reflects the inherent relations between airport capacity and arrival and departure traffic demand, adopts an improved optimization algorithm to solve the model, and generates an optimal flight allocation scheme. Priority ratio is introduced to dynamically adjust management preferences for arrival and departure traffic demand to further reveal the synergy mechanism between departure and arrival traffic flow demand and the airport capacity. The result shows that the Flight On-time Performance rate is lifted by 6% in the case study which proves the feasibility of the proposed method, demonstrating its value for maximizing airport capacity and traffic flow demand without requiring expansions on airport scales.

Transplant benefit-based offering of deceased donor livers in the United Kingdom

The National Liver Offering Scheme (NLOS) was introduced in the UK in 2018 to offer livers from deceased donors to patients on the national waiting list based, for most patients, on calculated transplant benefit. Before NLOS, livers were offered to transplant centres by geographic donor zones and, within centres, by estimated recipient need for a transplant. UK Transplant Registry data on patient registrations and transplants were analysed to build statistical models for survival on the list (M1) and survival post-transplantation (M2). A separate cohort of registrations - not seen by the models before - was analysed to simulate what liver allocation would have been under M1, M2 and a transplant benefit score (TBS) model (combining both M1 and M2), and to compare these allocations to what had been recorded in the UK Transplant Registry. The number of deaths on the waiting list and patient life years were used to compare the different simulation scenarios and to select the optimal allocation model. Registry data were monitored, pre- and post-NLOS, to understand the performance of the scheme. The TBS was identified as the optimal model to offer donation after brain death (DBD) livers to adult and large paediatric elective recipients. In the first 2 years of NLOS, 68% of DBD livers were offered using the TBS to this type of recipient. Monitoring data indicate that mortality on the waiting list post-NLOS significantly decreased compared with pre-NLOS (p <0.0001), and that patient survival post-listing was significantly greater post- compared to pre-NLOS (p= 0.005). In the first two years of NLOS offering, waiting list mortality fell while post-transplant survival was not negatively impacted, delivering on the scheme's objectives. The National Liver Offering Scheme (NLOS) was introduced in the UK in 2018 to increase transparency of the deceased donor liver offering process, maximise the overall survival of the waiting list population, and improve equity of access to liver transplantation. To our knowledge, it is the first scheme that offers organs based on statistical prediction of transplant benefit: the transplant benefit score. The results are important to the transplant community - from healthcare practitioners to patients - and demonstrate that, in the first two years of NLOS offering, waiting list mortality fell while post-transplant survival was not negatively impacted, thus delivering on the scheme's objectives. The scheme continues to be monitored to ensure that the transplant benefit score remains up-to-date and that signals that suggest the possible disadvantage of some patients are investigated.

Measuring and Mitigating Group Inequalities In Resource Allocation

Resource allocation, an integral part of socio-economic governance, profoundly influences individual prosperity and has the potential to mitigate or exacerbate socioeconomic disparities. This paper addresses the challenge of equitably allocating finite resources among individuals by answering two fundamental questions: (1) how to accurately measure and test group disparities and (2) how to optimally distribute resources while ensuring group fairness. We propose the Group Beneficiary Disparity (GBD) metric – an evaluation tool engineered to systematically gauge inequalities in a binary beneficiary/non-beneficiary context. The GBD provides decision-makers and planners with a powerful tool to audit social programs and optimize policies from a lens of group equality. We argue that utilitarian decision-makers cannot fully eliminate group disparities even when operating under social welfare constraints. To address this issue, we propose a new resource allocation optimization model, called A-FARM (Asymptotically Fair Allocation of Resources Model), with asymptotic group fairness guarantees. A-FARM partitions individuals into distinct, non-overlapping units and distributes resources among these units based on a utility-based allocation mechanism. Finally, we evaluate the performance of our proposed algorithm using both simulated and real-world data. Our results demonstrate that, A-FARM enables decision-makers to (1) achieve maximum efficiency under group fairness constraints and (2) perform a fairness-efficiency trade-off.

A many-objective optimization approach for weight gain and animal welfare in rotational grazing of cattle

The “multidimensional” nature of the concept of welfare is reflected in the definition proposed by the World Organization for Animal Health (OIE), according to which an animal is in a satisfactory state of welfare when it is healthy, comfortable, and well-fed, can express its innate behavior, and does not suffer pain, fear, or distress. Many of these aspects, in the real context of a cattle farm, are not considered, and most of the farmers’ decisions are based on their experiences. In this proposal, we establish a many-objective optimization model for rotational grazing allocation based on six objectives that consider cattle weight gain and travel, as well as their welfare. The model is solved using the NSGA-III algorithm, and its performance is evaluated using a simulation study of 90 days of rotational grazing in which it is compared with the traditional grazing strategy. Average weight gains of up to 36.7 kg per animal are achieved at the end of the three months of simulated grazing using the proposed model. The results indicate that the allocation model generates an average weight gain that is statistically greater than that generated by the traditional rotation method but also guarantees improved animal welfare, the main contribution of our approach.

An optimization model of supplier selection and order allocation with transportation mode alternatives under carbon cap and trade policy

An optimization model of supplier selection and order allocation with transportation mode alternatives under carbon cap and trade policy

Point-to-Point-Based Optimization Method of Ballast Water Allocation for Revolving Floating Cranes with Experimental Verification

The Revolving Floating Crane (RFC) is a specialized engineering vessel crucial for offshore lifting operations, such as offshore platform construction and deep-water salvaging. It boasts impressive lifting capacity, good adaptability to various environmental conditions, and high operational efficiency. Conventionally, the safety and stability of RFC operations heavily depend on manual ballast water allocation, which is directly influenced by factors such as personnel status and sea conditions. These manual operations often result in reduced lifting efficiency, higher energy consumption, and compromised operational safety. In response, this paper introduces a ballast water-allocation approach based on the Point-to-Point (PTP) theory for the intelligent operation process of the RFC. The fundamental principles of the PTP theory are analyzed, and a method tailored to optimize ballast water allocation for RFC is proposed. Considering the unique characteristics of the ballast system and the specific requirements of lifting operations, an optimization model for PTP-based ballast water allocation is established. Numerical experiments are conducted to verify the efficacy and reliability of the proposed method. Comparing it to the conventional approaches, the results demonstrate a notable 17.75% reduction in energy consumption and an impressive 73.49% decrease in decision-making time, showcasing the superiority of the proposed approach. Finally, the engineering feasibility of the PTP-based optimization method for ballast water allocation is validated through actual lifting experiments, underscoring its potential to enhance RFC operations.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis

BackgroundA lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.MethodsA systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.ResultsA total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25–0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37–0.59), vaccines 0.47(95%CI 0.11–0.82), testing 0.38(95%CI 0.19–0.57), personal protective equipment (PPE) 0.38(95%CI 0.06–0.70), beds 0.34(95%CI 0.14–0.53), medicines and equipment for treatment 0.32(95%CI 0.12–0.51); Funnel plots and Egger’s test showed no publication bias, and sensitivity analysis suggested robust results.ConclusionWhen the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Evaluation Model of Human Resource Allocation Balance for Healthy Environment in Organizations Based on Big Data Driven and Internet of Things

Abstract To improve the ability of balanced evaluation, dynamic allocation, level of output, and profitability of human resources, a balanced evaluation model of human resources allocation is proposed based on big data driven and the Internet of Things. An evaluation architecture model of human resource allocation balance in view of big data driven and the Internet of Things is established; a multithread big data driven configuration model and the Internet of Things are used to construct a table model of enterprise human resource optimal allocation in the form of reports; statistical regression analysis method is used to detect the risks and process parameters in the process of enterprise human resource optimal allocation; and employment elasticity theory analysis is taken to establish a resource factor analysis model of human resource balanced allocation under the constraint of economic growth mode. According to the changing trend of the scale and structure of labor resources, statistical regression analysis is adopted to make big data driven analysis in the process of balanced evaluation of human resources allocation, and balanced game control is adopted to analyze the relevant factors affecting human resources allocation to realize balanced scheduling of population flow and human resources allocation and automatic post adjustment. The empirical analysis results show that the system has a good balance in human resource allocation, the output efficiency among the various elements of human resource allocation is improved, and there is a high level of efficiency in human resource output.

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

Emergency resource allocation considering the heterogeneity of affected areas during the COVID-19 pandemic in China

The scientific allocation of emergency resources is crucial to ensure the success of COVID-19 relief operations. However, the heterogeneity of epidemic areas has an important impact on the allocation of emergency resources. Although it is a crucial topic, there has been limited research that considers the heterogeneity of affected areas in the emergency resource allocation. To bridge the gap, this study proposes a multi-period optimal allocation model of emergency resources considering the heterogeneity of affected areas, which aims to make the allocation of resources more equitable, efficient and economical. Then, a typical and representative case of emergency medical resource allocation in Hubei Province, China (where the epidemic occurred earlier and was seriously affected by COVID-19), was selected for a simulation study to verify the effectiveness and feasibility of the proposed model and method. The study finds that considering the heterogeneity such as disaster coefficient and demand urgency in different disaster stricken areas in emergency resource allocation can minimize the negative impact of resource shortfalls, especially in the early period of relief operations with insufficient resource supply. In addition, the proposed model can optimize multi-period emergency resource allocation by simultaneously considering time (efficiency criterion), cost (economic criterion), and loss(equity criterion), which is in line with the actual needs of emergency rescue to the COVID-19 epidemic. The results of this study can be effectively applied to the multi-period optimal allocation of emergency resources for large-scale public health emergencies, and providing insights for the government and relevant management departments to formulate emergency resource allocation policies and plans.

Joint Optimization of Urban Water Quantity and Quality Allocation in the Plain River Network Area

Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and quality is proposed. The model considers regional water resources, economic, social, and environmental requirements and uses the NSGA-II genetic algorithm for model solution. Furthermore, to evaluate and analyze the degree of spatial equilibrium of regional water resources and how it relates to economic factors, the study uses the spatial equilibrium theory of water resources and the Gini coefficient of water resources. Jingjiang, a city in Jiangsu Province characterized by a typical plain river network area, was selected as the study area. The results of the optimal allocation of water resources in Jingjiang City show that: (1) total water consumption and chemical oxygen demand (COD) emissions for the current planning period are within their respective limits. In addition, the implementation of the water conservation program has resulted in a 5% reduction in total water shortages and a reduction of COD emissions by 1276 tons, (2) the structure of the water supply in Jingjiang City has been optimized; more than 90% of Ⅳ~V surface water is used for agriculture, and the domestic water supply is mainly from transit water, which effectively ensures that high-quality water is used in the domestic water supply, (3) the spatial equilibrium coefficient of water resources per sub-area is between 0.33 and 0.74, indicating an unbalanced or almost unbalanced level. The application of a water conservation program has resulted in the improvement of the spatial equilibrium level of water resources in each sub-area, with an overall spatial equilibrium of 0.64, indicating a more balanced level; the degree of matching of water resources with population, GDP, and land area is at the matching level, (4) according to the Gini coefficient of the distribution of water resources, the plains river network area displays a better match between water resources and economic and social factors of each water receiving area, thanks to its unique geographical location and natural conditions. This study can serve as a decision-making reference for addressing the urban water quality water shortage problem in the plain river network area.

Assessment of using ponds as a nature-based solution on the optimal irrigation management of paddy fields

Abstract The water availability to supply agricultural water requirements plays an important role in the performance of agricultural products. Paddy fields depend heavily on water during cultivation, making this issue particularly important. In such cases, nature-based solutions, such as ponds, can be a suitable method to address water shortages for irrigation purposes. This study aims to develop a water allocation optimization model to evaluate the impact of two scenarios in ponds. In the scenario of increasing the ponds' depth, the hydraulic connection between the ponds and the groundwater level was taken into consideration. Based on this perspective, the scenario of increasing the depth resulted in a 0.33 million m3 increase in the volume of ponds, leading to an average percentage increase of 0.23% in the supply of irrigation requirements. The optimized model results in the pond dredging scenario showed a 2.03 million m3 increase in the volume of available water compared to the existing conditions, and a 1.70 million m3 increase compared to the pond deepening scenario. The results show that dredging ponds have a greater impact on increasing available water volume. It is also important to consider the hydraulic connection between ponds and groundwater levels in the pond-deepening approach.

Optimization of Initial Carbon Emission Allowance Allocation in Industrial Industries: A Case Study of Zhejiang Province, China

The construction of carbon market is one of the important means for China to reach the goal of carbon peaking and carbon neutrality, and the allocation of initial carbon emission right quota is the key link in carbon market transaction. At present, the historical method is the commonly used allocation method of initial carbon emission rights in China, and the use of this method is unfair and inefficient. Therefore, this paper proposes an allocation optimization model, selects a number of indicators, and optimizes the allocation of initial carbon emission rights based on the hierarchical analysis method and entropy weight method. Example analysis shows that the allocation optimization model helps to share the carbon emission responsibility and improve the allocation efficiency, realizes the optimal allocation of initial carbon emission rights among industrial sectors, and provides support for the emission reduction of industrial sectors.

Construction of an intelligent optimization model for water resource allocation under drought conditions

Abstract In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m3, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.

A Generalized Tail Mean-Variance Model for Optimal Capital Allocation

A Generalized Tail Mean-Variance Model for Optimal Capital Allocation

Research and Practice on Strategic Planning and Resource Allocation Optimisation Model of Off-site Research Institute

Abstract Off-site research institutes serve as crucial platforms for universities to facilitate the translation and application of their scientific research outcomes. Additionally, these institutes act as vital intermediaries for local governments to harmonize scientific inputs with economic development and play a key role in the industrial transformation and elevation of regional science and innovation levels. Drawing on the triple helix theory, this paper outlines strategic planning for off-site research institutes and develops a multi-objective linear programming model aimed at optimizing resource allocation. This model focuses on enhancing both the efficiency of resource utilization and the efficiency of resource allocation at these institutes. To address the issue of local minima commonly encountered in optimization algorithms, this study employs a simulated annealing algorithm to refine the performance of the particle swarm optimization algorithm. The resulting hybrid algorithm termed the simulated annealing particle swarm algorithm, is applied to solve the proposed model and investigate the determinants of optimal resource allocation. The findings indicate a significant improvement in resource allocation efficiency, with the coefficient for heterogeneous research institutes decreasing from an average of 0.84 in 2020 to 0.68. This optimization has led to a more effective and rational distribution of resources, better meeting the needs of the institutes. Furthermore, the analysis reveals that financial support and talent introduction and development account for approximately 69.7% of the variance in the optimized development of resource allocation at these institutes. The study provides actionable insights that could guide the optimal development of off-site research institutes, offering valuable references for future applications.

Research on Intelligent Equipment Procurement Strategy and Optimized Resource Allocation Based on Big Data

Abstract The intelligent equipment manufacturing cycle is long, resource consumption is large, in the manufacturing process often needs for different production stages to take multiple batches of resource procurement strategy and resource optimization configuration. Based on this situation, this paper proposes an intelligent equipment procurement strategy and resource optimization design. The problem description is established, the variables and constraints are introduced, and the construction of the objective optimization model is completed. The corresponding optimization model is solved using the improved Gray Wolf algorithm and the improved differential evolution algorithm. After the training and optimization of the improved gray wolf algorithm, the total cost of intelligent equipment procurement is 180150 yuan, and the procurement strategy is to select the preferred supplier at the corresponding point in time, according to which the procurement strategy can be a realistic intelligent equipment procurement task. In addition, the average deviation of resource optimization allocation based on the improved differential algorithm is only 0.35%, which is better than the performance of the traditional differential algorithm and genetic algorithm, confirming the feasibility of this paper’s algorithm in intelligent equipment resource allocation optimization model aiming at cost optimization.

Digital Human Resources Optimization Allocation Model and Algorithm of Artificial Intelligence Technology

From the perspective of optimal allocation of enterprise human resources, in view of the challenges brought by changes in enterprise organizational structure and changes in job requirements in the digital environment to human resource allocation, the article aims to improve the competitiveness of enterprises, combines the characteristics of enterprise organizational structure and job demand, and analyzes the optimal allocation of human resources. The article builds a digital human resources optimization allocation model based on artificial intelligence technology, and designs an artificial intelligence-based digital human resources optimization allocation algorithm. The results show that the resource utilization rate of the digital human resources optimization allocation model based on artificial intelligence technology used in the experiment can reach up to 79.2%.