Traditional Allocation Methods Research Articles

Allocation of carbon emission responsibility among Chinese cities guided by economic welfare gains: Case study based on multi-regional input-output analysis

Ensuring equitable allocation of carbon emission responsibilities at the city level is crucial for effectively mitigating climate change. However, traditional allocation methods like production-based accounting (PBA) and consumption-based accounting (CBA) encounter double-counting problems at this scale. This study for the first time addressed the ignored externality of carbon emissions among bilateral trading partners at city scale, achieving equitable carbon emission responsibility reallocations using the economic welfare gain-based (EWG) method. Examining 313 cities in China using a multi-regional input-output model, the highest carbon emission responsibilities are identified in Baotou at 400.7 Mt., followed by Yunnan (189.9 Mt) and Dalian (166.7 Mt). In this case, the EWG method reallocates producer and consumer responsibilities in bilateral trades. Among the 313 cities, emission responsibilities from 198 are situated between PBA and CBA, avoiding one-sided assignment of emissions based solely on either principle. A comparative analysis of allocation methods in China further indicates a predominance of production responsibility, mirroring a similar trend between EWG and PBA; a significant portion of producer responsibilities are identified within EWG. The results of this work may offer a fairer and economically sound basis for allocating emission responsibilities among cities, thereby facilitating more effective emission reduction strategies.

Estimation of cost reduction by tolerance optimization

Tolerance allocation is a design task with a strong potential impact on manufacturing choices. In practice, however, it is often carried out with simple heuristics rather than with an optimization approach like those available in research literature. One reason could be the difficulty in predicting the economic benefits resulting from optimization. To allow for such considerations, the paper proposes a procedure to estimate the cost reduction that optimization allows compared to three traditional allocation methods (equal tolerances, precision factor, proportional to nominal). The chosen optimization method is based on the closed-form solution of a problem of cost minimization with a stackup constraint, using the extended reciprocal power cost-tolerance function. Compared to other methods, it provides analytical expressions of both the allocated tolerances and the associated costs. When applied to specific cases, these help recognize the conditions in which optimization allows a significant reduction in manufacturing costs. The results show that this occurs when the features of the same dimension chain have very different properties regarding a set of design variables with particular influence on the amount of machining required.

Reliability Allocation in Complex Systems Using Fuzzy Logic Modules

Reliability allocation is critical in the design of complex engineering systems. However, traditional allocation methods rely on precise failure data and have difficulty handling epistemic uncertainty. This paper proposes using fuzzy logic modules for reliability allocation in complex systems. Fuzzy logic can represent imprecise data using membership functions and incorporate expert knowledge through fuzzy rules. The methodology involves developing fuzzy logic modules for each subsystem to allocate reliability based on fuzzy input variables like failure rate and criticality. The outputs are aggregated to obtain the system level allocation. A case study demonstrates the approach and compares results against traditional methods. The fuzzy logic modules are shown to optimize reliability allocation under epistemic uncertainty. This paper demonstrates the advantages of using fuzzy logic for reliability allocation in complex systems with limited failure data. The methodology provides a new tool for reliability engineers to handle imprecise information and optimize designs.

Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm.

Emergency shelter allocation planning technology for large-scale evacuation based on quantum genetic algorithm.

Shelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods. This article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints. Results show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter.

Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods

Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods

Research on reliability allocation strategy for gear transmission system of high-speed train

High-speed train industry is in the stage of rapid development, and the reliability allocation strategy is helpful to improve the reliability of the system and reduce the economic cost of components. In this paper, the reliability allocation of gearbox transmission system of highspeed train is studied. Combining AGREE allocation method and reliability allocation method based on cost function, the reliability allocation of high-speed train gear transmission system is carried out. Considering the quantity, importance, cost and complexity of various parts of high-speed train gear transmission system, the feasibility factor of improving reliability based on the cost function allocation method was improved to acquire the improved cost function, and the reliability optimization allocation model based on the improved cost function was established. Compared with AGREE allocation method and the traditional allocation method based on cost function, the improved optimization allocation strategy proposed in this paper is more suitable for high-speed train gear transmission system.

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

Application Research on the Maintainability Allocation Method of a Certain Shooter Seat

In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.

Water pollutant discharge permit allocation based on DEA and non-cooperative game theory

Against the background of the ecological civilization system reform in the new era, the appropriate allocation of water pollutant discharge permits is an important policy for controlling the amount of wastewater discharge. Traditional allocation methods have disadvantages, such as high additional costs, an unfair allocation scheme, and market distortion. In the present study, a fixed-cost allocation model based on data envelopment analysis (DEA) and the Nash non-cooperative game theory is employed to allocate water pollutant discharge permits of totally 31 provinces in China from 2008 to 2017. The allocation scheme considers environmental efficiency. The results demonstrate regional differences in the allocation of water pollutant discharge permits. The eastern region has abundant allocations. The northeastern and central regions have insufficient allocations. Besides, the western region has a significant shortage of allocations. It indicates the higher the utilization efficiency of the water pollutant discharge permits, the higher the region's sustainable development is. Based on the analysis, we propose guidelines for industrial wastewater discharge reduction.

Research on the Reliability Allocation Method of Smart Meters Based on DEA and DBN

Reliability allocation can reasonably determine the reliability index of each unit in the system to ensure product quality in design, manufacturing, testing and acceptance. In the design process of the smart meter, the preliminary reliability allocation results may be unreasonable, so in the middle and later stages of the design stage, the reliability needs to be reallocated. The traditional allocation method has some limitations, such as strong subjectivity, large amount of calculation and too much reliance on expert judgment. In order to solve these problems, this paper presents a multi-method fusion method of reliability allocation. First, this paper uses the goal-oriented methodology (GO methodology) to integrate dynamic Bayesian networks (DBNs) to predict the reliability of smart meters. Second, a data envelopment analysis (DEA) reliability allocation model is established, the posterior probability obtained by DBN reasoning together with the failure rate and structural complexity of each unit are used as the output indicators of this model. Finally, the reliability allocation weight is calculated by using the efficiency value obtained from the DEA reliability allocation model. The validity and accuracy of this method is verified by an accelerated life test. This provides a new idea for reliability reallocation of smart meters.

Study on Spectrum Allocation and Optimization of Wireless Communication Networks Based on SFOA

Wireless communication network spectrum is a limited resource. With the rapid increase of mobile communication services in recent years, traditional spectrum allocation methods are only based on a fixed spectrum allocation strategy, which often results in uneven and wasteful resource allocation. Therefore, spectrum allocation and the optimization problem need to be solved urgently. The application of semantic mobile computing in the Internet of Things and the research of emerging bionic models provide new ideas for this problem. In order to solve the problem of spectrum optimization and allocation, this paper proposes an optimization algorithm that simulates fisherman fishing to reasonably arrange the allocation and optimization of wireless network spectrum. This paper selects SFOA and the other two algorithms, designs experimental functions to perform calculations separately, obtains relevant data indicators, and uses comparative analysis to analyze. The analysis shows that in terms of performance, the success rate of SFOA is higher than that of PSO, and the success rate of the two function calculations has reached 100%. In the signal‐to‐noise ratio analysis, when the signal‐to‐noise ratio is ‐4 dB, the throughput of GPSO reaches the maximum value of 0.17, the throughput of PSO reaches the maximum value of 0.56, and the throughput of SFOA reaches 1, which shows that SFOA is adopted. The stability and accuracy of the algorithm are higher than the other two algorithms, and in the case of high signal‐to‐noise ratio, the advantages of the SFOA algorithm are also more obvious. This shows that the use of this algorithm will be very helpful for spectrum allocation and optimization. Because SFOA has high stability and accuracy, through reasonable adjustment and improvement, it can make good use of spectrum allocation and optimization. Chinese wireless communication network and the development of Internet of Things technology are of great significance.

Comparison of sampling effort allocation strategies in a stratified random survey with multiple objectives

Stratified random survey is commonly used to estimate abundance indices of fish populations in multispecies survey, providing reliable data for stock assessment and fisheries management. In some cases, however, the sample size is relatively small because of the limitation of survey cost or other factors. The allocation methods of sampling efforts among strata in stratified random surveys with small sample size may need adjustment compared with traditional approaches. In this study, two sampling stations were allocated to each stratum first and then the remaining sampling units were allocated among strata using five traditional allocation methods. In order to distinguish them from traditional methods, we called them adjusted methods in this study. A simulation study was conducted to compare the performances of different allocation strategies of sampling efforts in a stratified random survey for estimating abundance indices of multiple target species. Relative estimation error (REE) and relative bias (RB) were used to measure the precision and accuracy of estimates of abundance indices under different allocation schemes of sampling efforts in the multispecies survey. The performances of different allocation schemes in estimating abundance indices varied greatly for different species over different seasons. The adjusted Neyman allocation scheme could significantly reduce the REE and RB of estimates of abundance index for single species survey. For multiple species surveys, the adjusted average-Neyman allocation method, the adjusted Yate allocation method, the adjusted proportional allocation method and current allocation method had relatively high accuracy and precision of estimates of abundance indices for four species in terms of the totalREE and totalRB. Though the adjusted average-Neyman allocation scheme did not always have the best performance, it was the optimal one considering the accuracy and precision of estimates of abundance indices for all species simultaneously. The allocation of sampling efforts among strata in stratified random surveys targeting for estimating abundance indices of multiple species should comprehensively consider the variance of abundance of different species in stratum and the seasonal changes.

Micromirror array allocation algorithm based on deconvolution.

Freeform illumination is one of the necessary techniques in 28 nm technology nodes and beyond. The micromirror array (MMA) has been widely used in lithography freeform illumination systems due to its programmability and high free degree. The MMA allocation algorithm is the key to generate the target freeform illumination source. Its computational speed and precision affect the generation speed and precision of the target illumination source as well as the process window size of the generated illumination pupil directly. In this paper, an MMA allocation method based on deconvolution is proposed. The target freeform illumination source can be obtained directly with the deconvolution and quantization processes. Without the iterative optimization process, the computational speed of the proposed method is much faster than that of the traditional method. The numerical simulation results show that the difference between the target source and the MMA source generated using the proposed method is less than 0.2%. Compared with the process window loss of the target source, the process window loss of the MMA source generated by the proposed deconvolution method is less than 0.5%. Compared with the traditional allocation method, the runtime of the proposed method is less than 0.05 s and has improved by 1463 times.

Traffic assignment model for combined mode with travel condition constraints

Combined-mode traffic assignment is one of the key links for multi-modal transportation planning. In order to quantitatively evaluate the implementation effect of combined-mode transportation and explore its assignment mechanism, a combined-mode traffic assignment model with travel constraints is proposed. First, the practical factors of combined mode in real world are discussed and integrated into the analysis of multi-modal network transformation as related constraints. Second, the physical transportation network is translated to equivalent super network (SN) and state-augmented network (SAN) based on the graph theory. Moreover, the network size constrained by the actual conditions is conducive to further analysis of combined-mode trips. Third, a tri-level combined-mode traffic assignment model is formulated based on the simulation platform of SAN. The first level of the model is to address the combined-mode choice, the second level is to transfer choice on SAN, and the third level is to characterize auto travelers’ route choice behavior. By analyzing the impedance in multi-level network, an MSA algorithm solving Nest-Logit model is proposed. Finally, numerical example is performed to validate the model. The method proposed in this paper considers the travel similarity, reduces the network scale, conforms to the travel logic and makes up for the shortcomings of traditional traffic allocation methods.

An Innovative Control Allocation Framework for a Novel Tiltrotor Aircraft

Control allocation is still a challenge for tiltrotor aircraft due to effectors’ redundancy, dramatic nonlinearities and cross-coupling. Besides this, the external disturbance caused by the unique configuration of tiltrotor such as larger wing area and tiltable mechanism puts forward extra security requirement on control allocation method. Thus, this paper proposes an innovative control allocation framework from the perspective of flight safety to address this problem. Firstly, an effector distribution management scheme based on remaining control ability index (RCAI) is developed to reduce the effector saturation and ensure flight safety. Secondly, the energy consumption models for two classes of effectors: thrust vectoring and aerodynamic surfaces are introduced respectively for the subsequent optimization problem mathematically. Then, the null space transition is used to find the minimum-energy control input of effectors. Finally, key parameters of energy consumption model are identified by experiments. The real-time hardware-in-loop simulation platform is adopted to demonstrate the effectiveness of the proposed control allocation framework. Experimental results show that the energy consumption of proposed method can reduce by about 63.5% comparing to traditional control allocation method.

Low-carbon power dispatch with wind power based on carbon trading mechanism

Low-carbon power dispatching with wind power has been attracting increasing attention recently, whereas it also encounters some difficulties under carbon trading mechanism, such as the resistance to carbon emission reduction, the fairness of carbon trading, and the disturbance of wind power uncertainty. For harmonizing these contradictions, this paper presents a stochastic dynamic economic dispatch model focusing on wind power uncertainty and carbon emission rights simultaneously, which tries to minimize total electrical energy costs under uncertainty, and to meet the specific requirement of carbon emission reductions. Moreover, several traditional allocation methods for initial carbon emission rights emphasizing the equality are introduced, and the uncertainty characterizations of wind power are discussed through the scenario-generation technique. The simulation results eventually demonstrate that the operating cost for non-cooperative mode cannot be optimized as the ratio of carbon emission reductions rises; carbon trading mechanism is beneficial for lower cost under the pressure of carbon emission reduction; carbon price ranges that suit both carbon trading parties could be deduced. Synthesizing wind power uncertainty and carbon emission rights, this study is helpful for allocating the load demand of wind power integrated system to each generating unit more scientifically and reasonably based on carbon trading mechanism.

Loss Allocation in Distribution Networks Based on Aumann–Shapley

This paper outlines a procedure for loss allocation in both radial and meshed distribution networks with distributed generation that could be regulated in various ways. The method is analytically developed based on the theory of electrical circuits combined with game theory based on Aumann–Shapley, which guarantees both the electrical principles and the fair axioms of game theory. The proposed method obtains unitary participation coefficients for each network user based on the currents demanded/injected by each user and the network topology. The proposed allocation method based on Aumann–Shapley has been compared with other traditional allocation methods, is adaptable to distribution networks, and shows great potential and ease of implementation. Moreover, it can be applied to any kind of distribution network (radial or meshed) with distributed energy resources.

Influence of allocation methods on the LC-CO2 emission of an agrivoltaic system

Agrivoltaic systems are multi-output systems where both solar power and crops are produced on the same land. Unlike other land-based photovoltaics (PV), the agrivoltaic PV modules are ground mounted between crops at some height with a certain tilt. Alternatively, PV modules replace part of a greenhouse or are partially set either below or above a covering material. The system could become an important mitigation option for climate change. However, power generation by PV reduces sunlight transmittance and therefore reduces agricultural yield. An allocation method that will address the potential interference of PV with crops is required for life cycle assessment (LCA) to evaluate greenhouse gas mitigation. This study aims to develop a new allocation method (i.e., solar allocation) and compare the LCA results of the new and traditional allocation methods (i.e., system expansion and economic allocation). The partition rate of the solar allocation is derived from the ratio of the active area covered by PV to the greenhouse surface area and light transmittance. These methods were applied to an agrivoltaic tomato production system using protected horticulture with the introduction of organic photovoltaics as a case-study of a system in Japan.The allocation methods considered in the present study could serve as potential methods in assessing life cycle−CO2 emissions. Above all, the solar allocation method can be used for many crops that will be influenced by PVs. Further improvement of the allocation method is required in cases where crop growth is less influenced by PVs (e.g., shadow-tolerant crops or transparent PV).

Transmission Cost Allocation Based on Data Envelopment Analysis and Cooperative Game Method

Transmission cost allocation is a traditional but challenging problem in electric power industries. Many factors can affect the transmission cost allocation. But most traditional allocation methods focus on just one point. The Data Envelopment Analysis (DEA) approach has the characteristic of multi-criterion. It can overcome the drawbacks of traditional allocation methods. This paper combines the game theory and DEA approach to solve the problem of transmission cost allocation. In order to take various key points of power transmission into account, three criterions are proposed. These criterions reflect the effect of load demand, transmission line usage and marginal cost on transmission cost allocation. Nucleolus and Shapley value are used to solve the DEA game programming. For measuring the fairness of the transmission cost allocation methods, this paper proposes an algorithm to evaluate the satisfaction of users with the different allocation methods. The DEA game approach is applied to IEEE 14-bus system. The results which are compared with that of other three traditional allocation methods demonstrate the applicability of the proposed method.