Collaborative Allocation Research Articles

Data-driven collaborative healthcare resource allocation in pandemics

Severe shortages of healthcare resources are major challenges in pandemics, especially in their early stages. To improve emergency management efficiency, this paper proposes a novel rolling predict-then-optimize framework that includes three interactive modules, i.e., data-driven demand prediction, healthcare resource allocation, and parameter rolling update. Such a framework uses historical data to dynamically update the control parameters of the proposed Net-SEIHRD model, which predicts the healthcare needs of each region by jointly considering government interventions and cross-regional travel behaviors. Based on the forecasted healthcare resource demand in real-time, an optimization model is then formulated to realize coordinated resource allocation across multiple regions by minimizing the total generalized cost. To facilitate model solving, the proposed mixed integer nonlinear programming model is converted into an equivalent mixed integer linear model by using some linearization techniques. Finally, the proposed method is applied to the SARS-CoV-2 emergency response and collaborative allocation of healthcare resources in Shanghai, China. The results show that the proposed prediction model can effectively predict the peak and scale of the spread of the virus. Compared with the traditional LM and SEIHR models, the prediction accuracy of the Net-SEIHRD model is improved by 10.76% and 24.11%, respectively. Moreover, coordinated relief activities across regions, such as patient transfer and drug-sharing can improve the efficiency of pandemic control and save social costs.

Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective

The growth of global data is increasing exponentially, leading to a greater demand for computing power. To address this requirement, expanding computing power from the cloud to the edge is essential. However, this transformation presents two significant challenges: how to share computing resources more efficiently and how to optimize resource allocation. To tackle these challenges, we propose a three-layer Computing Power Network (CPN) framework that focuses on implementing the collaborative allocation of computing nodes and user tasks. We formulate the resource allocation problem in CPN as a double auction game and use an experience-weighted attraction algorithm that enables participants to adjust bidding strategies based on environmental interactions. We implemented a prototype of our proposed CPN framework and conducted extensive experiments to verify our algorithm’s convergence and evaluate the benefits obtained by buyers (users) and sellers (computing nodes) from the perspective of transaction prices, rewards, and average pricing. The comprehensive experimental results demonstrate the effectiveness of our proposed method. Compared with state-of-the-art pricing strategies, our approach achieves a 20% increase in convergence speed and an 88% increase in overall returns. Furthermore, it also exhibits a 2.5% increase in deal prices and a substantial 83% rise in the income of individual users. These outcomes convincingly prove the superiority of our method in achieving better convergence, improving overall returns, and benefiting both buyers and sellers in the CPN resource auction market.

Reliability-centered availability collaborative optimization allocation approach for machine tools

The availability of computer numerical control (CNC) machine tools is a comprehensive reflection of reliability and maintainability. To ensure the availability requirements of products, it is necessary to allocate the availability indicators of the entire machine to all units. Traditional methods based on reliability allocation or maintainability allocation ignore the coupling relationships among them, which cannot comprehensively improve the availability level of machine tools. Therefore, in this paper, a availability collaborative optimization allocation model for CNC machine tools is proposed to maximize the availability under the optimal combination of failure rate and repair rate. Firstly, a two-stage reliability allocation model based on different allocation mechanisms is established by taking the Function-Motion-Action (FMA) decomposition tree as the allocation path. In the first stage, the allocation weights are determined based on global sensitivity analysis. In the second stage, the reliability optimization allocation model based on Copula function is established. Then, with reliability as the center, an availability collaborative optimization allocation model constrained by maintainability is constructed, and the non-dominated sorting genetic algorithm II (NSGA-II) algorithm is used to solve the model. Finally, a CNC gear milling machine is taken as an example to verify the rationality and effectiveness of the method on product availability requirements under collaborative allocation.

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

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.

Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model

To further enhance fairness in the allocation of en-route space–time resources in the collaborative trajectory selection program, a study on the plan preferences between air traffic control (ATC) and airlines in the selection process of the final plan is conducted based on the initial resource allocation plans, considering the roles of airlines in resources allocation decisions. By using Stackelberg game theory, a game model is established for the roles played by ATC and airlines in the process of selecting plans. Then, combining the overall consideration of ATC for all affected flights, the preferences of airlines for initial allocation plans are obtained, and the option range of selectable plans is narrowed down to determine the optimal allocation plan. The results of the example analysis show that the proposed model and method can effectively select the optimal allocation plan from the six initial allocation plans, select the trajectories and entry slots in the congestion areas for airlines that better meet the operation demand, and provide the decision basis with more preferences for ATC to select the final allocation plan. When ATC prefers the lowest overall delay cost, the delay cost of the selected optimal allocation plan is 267.7 min, which is 23.84% lower than the traditional RBS algorithm; when considering the preferences of the main base airline in East China, the delay cost of the selected optimal allocation plan is 287.7 min, which is 18.15% lower than the traditional RBS algorithm.

Collaborative allocation model and balanced interaction strategy of multi flexible resources in the new power system based on Stackelberg game theory

For realizing environment protection and carbon emission reduction strategy, the dominant position of renewable energy in the future power system has been clarified. Planning economic and efficient flexible resource system is the basis to balance multi fluctuations caused by large amount of renewable energy integration and ensure the safe operation of the whole power system. Considering that the flexibility adjustment demands of different links of the power system are not independent, but influence and restrict each other, this paper focuses on how to collaboratively allocate multi-flexible resources and achieve corresponding balancing interaction strategy. Firstly, a flexible resource system is composed through a fuzzy TODIM improved PROMETHEE-II model. Secondly, a multi-flexible resource collaborative allocation optimization model is proposed, which considers the Stackelberg game relationship of flexibility adjustment demand and ability in different links of the power system under varied fluctuation scenarios. The scientificity and effectiveness of the proposed model are verified by a practical case, and key incentive factors and specific development suggestions for improving flexible resources application efficiency are identified simultaneously. Since the collaborative planning of flexible resource system have not been deeply discussed by scholars yet, thus, this research contributes to the literature and expand the knowledge.

Discovering optimal resource allocations for what-if scenarios using data-driven simulation

IntroductionData-driven simulation allows the discovery of process simulation models from event logs. The generated model can be used to simulate changes in the process configuration and to evaluate the expected performance of the processes before they are executed. Currently, these what-if scenarios are defined and assessed manually by the analysts. Besides the complexity of finding a suitable scenario for a desired performance, existing approaches simulate scenarios based on flow and data patterns leaving aside a resource-based analysis. Resources are critical on the process performance since they carry out costs, time, and quality.MethodsThis paper proposes a method to automate the discovery of optimal resource allocations to improve the performance of simulated what-if scenarios. We describe a model for individual resource allocation only to activities they fit. Then, we present how what-if scenarios are generated based on preference and collaboration allocation policies. The optimal resource allocations are discovered based on a user-defined multi-objective optimization function.Results and discussionThis method is integrated with a simulation environment to compare the trade-off in the performance of what-if scenarios when changing allocation policies. An experimental evaluation of multiple real-life and synthetic event logs shows that optimal resource allocations improve the simulation performance.

A Collaborative Governance Strategy for Power Quality in AC/DC Distribution Networks Considering the Coupling Characteristics of Both Sides.

The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis.

Simulation of high-dimensional collaborative distribution of disaster materials with time window constraints

In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response; Build SPEA2 + SDE hybrid algorithm, solve the model and output the optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed.

A Distributed Collaborative Allocation Method of Reconnaissance and Strike Tasks for Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm’s capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments.

A Multi-Regional Collaborative Optimization Model of Emergency Medical Materials for Responding to COVID-19

Medical materials are the most important, fundamental resources necessary for emergency relief of major infectious disease disasters. The scientific and optimal allocation of emergency medical materials is the key to reducing casualties and losses in epidemic regions, and to improving the effectiveness and efficiency of rescue operations. In response to the cross-border characteristics of major infectious diseases, the imbalance of material storage, and the differences of supply across regions, a multi-objective optimization model for a multi-regional collaborative allocation of emergency medical materials was developed. Then, an improved adaptive genetic algorithm (IAGA) was designed and applied to solve the proposed model. Finally, a case study of the collaborative response to the COVID-19 epidemic in the Yangtze River Delta of China was conducted for model verification. The results show that collaborative allocation can improve the material satisfaction rate at demand points, especially under peak demand pressure during the early stage of the response, and can meet all material needs at all demand points in the shortest possible amount of time. The proposed model can achieve the effective integration and mutual sharing of emergency materials across regions, and improve the efficiency of emergency material utilization and rescue efforts. The material allocation scheme considers the difference coefficients in different regions, which is conducive to enhancing the flexibility of decision-making and the practical applicability of collaborative allocation operations. A comparative analysis of the algorithms shows that the proposed IAGA is an effective method for managing large-scale multi-regional emergency material allocation optimization problems, as it has higher solving efficiency, better convergence, and stronger stability.

Meta-action-oriented collaborative allocation optimization for accuracy-related key quality characteristics of CNC machine tools

Meta-action-oriented collaborative allocation optimization for accuracy-related key quality characteristics of CNC machine tools

Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization

This study addresses two key issues, ie, the “cold-start problem” in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives. These two issues have not yet been well addressed in data-driven emergency medical resource allocation systems. A decision support prediction-then-optimization framework combing deep learning and optimization is developed to address these two issues. Two transfer learning based convolutional neural network models are built for epidemic transmission predictions in the initial and the subsequent outbreak regions using transfer learning to deal with the “cold-start problem”. A prediction-driven collaborative emergency medical resource allocation model is built to address the issue of collaborative decisions by simultaneously considering the inter- and intra-echelon resource flows in a multi-echelon system and considering the efficiency and fairness as the objective functions. A case study of the COVID-19 pandemic shows that combining transfer learning and convolutional neural networks can improve the performances of epidemic transmission predictions, and good predictions can improve both the efficiency and fairness of emergency medical resource allocation decisions. Moreover, the computational results show that the prediction errors are asymmetrically amplified in the optimization stage, and the shortage of the resource reserve quantity mediates the asymmetrical amplification effect.

Contributions of climatic and social factors to two water use efficiency indicators in the Yellow River

AbstractTracing the spatiotemporal tendency and driving forces of water use efficiency (WUE) is of great significance for understanding the carbon‐water balance, especially in large river basins. The lack of differential comparisons regarding the tendencies and driving forces of WUE indicators between the upper and lower reaches of rivers often affects the collaborative allocation and utilization of water resources at the basin scale, especially social factors such as population and economy distributions. Using remote sensing data and hierarchical decomposition methods, we identified the spatiotemporal distribution of the WUE of ecosystems (WUEe) and the WUE of canopies (WUEc) in the Yellow River Basin from 2003 to 2017 as well as the contribution rates of climatic and social factors to WUEe and WUEc. Our results show that WUEe had a different distribution pattern than WUEc. An increase in WUEe was accompanied by a decrease in WUEc, and these changes varied among the upper, middle and lower reaches. WUEc was most negatively correlated with pressure (| r | = 0.65), while WUEe was most positively correlated with the precipitation rate (| r | = 0.68). The contribution rates of climatic factors to both WUEc and WUEe in the upper reaches were higher than 95%. The middle reaches were mainly driven by climatic factors, with contribution rates higher than 90%. In the lower reaches, the contribution rates of social factors to WUEe were higher than 30%. These differentiated contributions indicated the importance of considering social factors in the lower reaches than in the upper reaches when managing WUEe under the dominance of climatic factors on WUEc.

Resources Collaborative Allocation Method of UAV Dense Formation Support Network

Resources Collaborative Allocation Method of UAV Dense Formation Support Network

Container Terminal Berth and Yard Collaborative Allocation for Import and Export Synchronous Operations by Computational Logistics

Container terminal operators have practical and clear requirements on improving operational efficiency and service level to address running challenges. The resource allocation and collaborative scheduling of quayside berth and storage yard are vital issues in the optimization of container terminal handling systems (CTHSs). The allocation of berth in quayside and blocks on storage yard directly affects the running efficiency of CTHS. Consequently, we focus on the berth and yard collaborative allocation for import and export synchronous operations (BYCA-IESOs) and propose the BYCA-IESO research model based on synchronous or asynchronous integrated scheduling strategy by computational logistics. The BYCA-IESO model is a mixed integer quadratic programming model to minimize multiple time consumption of calling liners and the total horizontal transferring distances of yard trailers at container terminals. Furthermore, we design other two allocation policies for comparison on the BYCA-IESO, which are both multistage scheduling strategy profiles. CPLEX 20.1.0 is applied to implement and solve the model and tactics of BYCA-IESO. Accordingly, the feasibility, validity, and relative merits of the models and strategies are obtained and verified by analyzing and comparing multiple numerical experimental results.

Collaborative Allocation and Optimization of Path Planning for Static and Mobile Sensors in Hybrid Sensor Networks for Environment Monitoring and Anomaly Search.

In this study, a novel collaborative method is developed to optimize hybrid sensor networks (HSN) for environmental monitoring and anomaly search tasks. A weighted Gaussian coverage method hs been designed for static sensor allocation, and the Active Monitoring and Anomaly Search System method is adapted to mobile sensor path planning. To validate the network performance, a simulation environment has been developed for fire search and detection with dynamic temperature field and non-uniform fire probability distribution. The performance metrics adopted are the detection time lag, source localization uncertainty, and state estimation error. Computational experiments are conducted to evaluate the performance of HSNs. The results demonstrate that the optimal collaborative deployment strategy allocates static sensors at high-risk locations and directs mobile sensors to patrol the remaining low-risk areas. The results also identify the conditions under which HSNs significantly outperform either only static or only mobile sensor networks in terms of the monitoring performance metrics.

Collaborative Allocation of Energy Consumption, Air Pollutants and CO2 Emissions in China

Energy consumption is an important source of the emissions of CO2 and air pollutants such as SO2 and NOX. Reducing energy consumption can realize the simultaneous reduction of air pollutants and CO2 emissions to a certain extent. This study examines the collaborative allocation of energy consumption and the emissions of SO2, NOX and CO2 in China. In contrast to previous studies, this paper proposes an improved centralized DEA model that takes into account the correlation between energy consumption and air environmental emissions, the economic development demand and the energy resource endowment of different provinces. The initial allocation scheme is obtained based on the principle of equity. Then, the initial allocation results are brought into the improved centralized DEA model to maximize the expected output. The empirical analysis of projected data for 2025 shows that the looser the restrictions of energy consumption, the greater the optimal economic output. When the energy consumption of each province is allowed to fluctuate within the range of 85% to 115% of the initial quota, the total GDP is the largest and 20.62% higher than the initial GDP. The optimal allocation scheme is more equitable than the initial scheme and realizes absolute interpersonal equity and economic equity. Eighteen provinces bear the pressures of energy saving, emission reduction or GDP growth, with average pressure indexes of 11.46%, 16.85% and 40.62%, respectively. The pressures on the major regions involved in the “Belt and Road”, Beijing-Tianjin-Hebei region and Yangtze River Economic Belt national strategies will thus be reduced significantly; the maximum pressures on energy saving, emission reduction and GDP growth are 10.03%, 12.17% and 29.84%, respectively. China can take a series of measures to promote regional coordinated development and improve the realization of optimal allocation schemes, including establishing unified resource asset trading platforms, improving the methods of regional cooperation, building effective transportation and logistics transport networks to weaken the barriers among regions and implementing differentiated regional policies and regional interest coordination mechanisms.

The Method of Collaborative Allocation of Slots and Trajectories Based on Route Resource

In order to solve the problem of cooperative allocation of route resources in the case of congestion or bad weather, this paper proposes a two-stage time slot coordinated allocation model. The two phases will be considered separately from the air traffic control department and the airlines, with the lowest total delay cost of the affected flights and the average flight delay cost between the airlines tend to be the same. In order to verify the model, this paper intends to use the greedy algorithm to solve. The validity of the model is tested by the simulation data of the route, so as to achieve the goal of balancing multiple flow constrained areas and improve the operation efficiency of the route.