Resource allocation and network evolution considering economics and robustness in manufacturing grid

Sun, G.; Chen, H., and Mao, Y., 2020. The efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 691–695. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of marine technology, the tasks of marine survey, exploration, navigation, transportation and development are becoming more and more serious. It is necessary to pay attention to the problem of planning resource allocation in coastal areas, so as to design a more reasonable resource allocation scheme. The traditional analysis model can only converge locally, resulting in the deviation of the analysis results of resource allocation efficiency. Therefore, the construction method of the efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm is proposed. According to the relationship between fuzzy operator and fuzzy scale, this method calculates the carrying capacity of coastal environmental resources. In light of the prediction results of the dynamic demand forecast function of emergency resources, it sets the evaluation weight of the efficiency of technological innovation resource allocation of coastal enterprises. Based on the global convergence characteristics of genetic algorithm, it realizes the construction of the efficiency analysis model of technological innovation resource allocation of coastal enterprises. The experimental results show that compared with the traditional method, the model based on genetic algorithm has stronger convergence and can achieve global data convergence. It can be seen that the analysis results of this model accord with the reality and can be applied to the analysis of resource allocation efficiency at this stage.

A topological model of mesh-network of LTE technology presented using hypergraphs and Koenig graphs has been offered. The capabilities of hypergraphs were used to describe the distribution of frequency resources and to assess the characteristics of the initial configurations of the mesh network. Koenig graphs were used for a comparative analysis of the results. This made it possible to describe all possible configurations of the mesh-network, as well as its elements represented in the form of vertices and edges of the hypergraph. A mathematical model of the frequency resource allocation in the LTE technology mesh-network. Its novelty lies in the description of the problem of the frequency resource allocation as subchannel allocation problem in taking into account the interference between the network stations.

In order to improve the statistical ability of online education allocation resources, a statistical model of online education allocation resources for the coordinated development of regional economy is proposed. Based on the principle of comparability and practicability in the selection of statistical indicators of online education allocation resources, this paper constructs a statistical indicator system of online education allocation resources, assigns the weight of statistical indicator system of online education allocation resources by using the specific steps of analytic hierarchy process, and calculates the allocation level of educational resources of each school in six administrative regions by using the weighted index and model, According to the results of the cluster analysis of the types of school resource allocation, by introducing the difference coefficient, the balance of the allocation of various types of school education resources in the region is determined. Through the establishment of the statistical model of online education resource allocation, the statistics of online education resource allocation is realized. The experimental results show that the statistical model of online education resource allocation for the coordinated development of regional economy has relatively high statistical ability in terms of statistical efficiency and student satisfaction.

Despite the importance of intrahousehold decision-making, economists have not always done well to understand what happens within the boundaries of the home. This is because mainstream economists have focused on the market economy, often ignoring the unpaid work done within the home. Economic models of intrahousehold resource allocation have extended beyond initial challenges to the unitary model of the household, demonstrating that many households do not obtain cooperative or efficient outcomes, at least in strictly economic terms. Models of household decision-making and resource allocation are silent on the processes. They consider how proxies for bargaining power affect the outcomes, but were not designed to tell us about the processes of how these decisions and allocations were reached. The advances in the intrahousehold allocation and decision-making literature have frequently occurred due to the relationships between the theory and the empirical work. Feminist theory and realities on the ground challenged early empirical work that treated households as though they were individuals.

The results of development of the mathematical model for time-frequency resource allocation in the downlink of the LTE technology using the Resource Allocation Type 1 are given. The proposed model is aimed at provision of the guaranteed quality of service for wireless network users by allocating the required downlink bandwidth to user equipment. The analysis of the proposed model for time-frequency resource allocation in the LTE technology from the viewpoint of providing the required bandwidths for user equipment in the downlink was conducted.

The results of development of a mathematical model for time-frequency resource allocation in the LTE technology downlink using Resource Allocation Type 1 are given. Using the proposed model allows taking into account the technological features of the LTE downlink (splitting the resource blocks into subsets, allocation of RB of only one subset to user equipment), as well as territorial remoteness of user equipment (type of modulation system and coding). During the analysis it was found that use of the proposed model is aimed at providing each user equipment with quality of service according to the bandwidth in the downlink with the possibility of access to additional (unguaranteed) bandwidth. The analysis of the proposed model for time-frequency resource allocation in the LTE technology from the viewpoint of providing the required bandwidths for user equipment in the downlink was conducted.

The system of streaming data and complex events distributed processing is considered. A mathematical model for computational resource consumption and allocation in edge computing is offered. On the basis of mathematical model there was developed a multi-agent algorithm as a part of agent-based software architecture for distributed processing of streaming data, which had been developed by the authors previously. The efficiency of the developed algorithm has been investigated by means of simulation in AnyLogic. The resource allocation problem in edge computing was simulated and the algorithm demonstrated satisfying results both at static and dynamic regimes. Also, the perspectives of intellectual technologies using in resource allocation for edge computing are considered.

The purpose of Part 2 is to develop a model for resource allocation of state prevention funds to be distributed to its substate jurisdictions based on the relative need for prevention services measured in terms of composite risk-factor index (COMRISK) scores computed for each county. The risk factors are extracted from an extensive review of risk and protective factors addressed in the prevention literature. Based on twenty-two risk and protective factors identified, we were able to explain 71.3 percent of the total variation in student drug using behavior observed at the individual level. By aggregating individual COMRISK scores to the county level, we were able to determine aggregated COMRISK index scores at the county level. By determining the proportion of each county's share of the total statewide COMRISK and by weighting the latter proportion by the population size of each county, we have devised Prevention Needs Index (PNI) score based on the risks for each county. Finally, the county's share of PNI score as a proportion of the total statewide PNI score is computed. The latter proportion is then multiplied by the total amount of prevention resources available at the state. In this way, we were able to develop an alternative resource allocation model solely based on risk and protective factors for determining prevention needs of each county, independent of composite index score of drug use (COMDRUG) presented in Part 1. A comparison of three models for resource allocation has shown a significant amount of similarity of the total funds computed for each county. Accordingly, no preference is made among the resource allocation models suggested, although it is emphasized that the final decision concerning the level of funding must be made on the selection of the resource allocation algorithms rather than the suggested amount of funding computed for each county.

The construct of the market is an interesting new element in the discourse on higher education in Europe. It has generated serious initiatives in deregulating higher education, in developing performance-based models of resource allocation, in fostering inter-institutional competition and efficient management structures, and even in considering the 'privatisation' of higher education. These developments affect particularly the financing of higher education, where new models of resource generation and allocation, institutional steering and controlling, and accountability are being explored. Within this context, and with a view towards the future of higher education in Europe, four issues are being examined in more detail: formula funding, the mobilisation of external resources, the introduction of tuition fees, and the creation of private institutions of higher education.

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

A model for resource allocation in complex hierarchies

ALVEC: Auto-scaling by Lotka Volterra elastic cloud: A QoS aware non linear dynamical allocation model

This article develops a simple evolutionarily stable strategy (ESS) model of resource allocation in partially selfing plants, which incorporates reproductive and sex allocation into a single framework. The analysis shows that, if female fitness gain increases linearly with resource investment, total reproductive allocation is not affected by sex allocation, defined as the fraction of reproductive resources allocated to male function. All else being equal, the ESS total reproductive allocation increases with increasing selfing rate if the fitness of selfed progeny is more than half that of outcrossed progeny, while the ESS sex allocation is always a decreasing function of the selfing rate. Self‐fertilization is much more common in annual than in perennial plants, and this association has been commonly interpreted in terms of an effect of life history on mating system. The model in this article shows that self‐fertilization can itself cause the evolution of the annual habit. Incorporating the effects of pollen discounting may not have any influence on total reproductive allocation if female fitness gain is a linear function of resource investment, although the evolutionarily stable sex allocation is altered. Evolution of the selfing rate is found to be independent of reproductive and sex allocation under the mass‐action assumption that self‐ and outcross pollen are deposited simultaneously on receptive stigmas and compete for access to ovules.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

The healthcare industry has recently faced the issues of enhancing patient care, streamlining healthcare operations, and offering high-quality services at reasonable costs. These crucial issues include general healthcare administration, resource allocation, staffing, patient care priorities, and effective scheduling. Therefore, efficient staff scheduling, resource allocation, and patient assignments are required to address these challenges. To address these challenges, in this paper, we developed a mixed-integer linear programming (MILP) model employing the Gurobi optimization solver. The model includes staff assignments, patient assignments, resource allocations, and overtime hours to minimize healthcare expenditures and enhance patient care. We experimented with the robustness and flexibility of our model by implementing two distinct scenarios, each resulting in two unique optimal solutions. The first experimental procedure yielded an optimal solution with an objective value of 844.0, with an exact match between the best-bound score and the objective value, indicating a 0.0% solution gap. Similarly, the second one produced an optimal solution with an objective value of 539.0. The perfect match between this scenario’s best-bound score and objective value resulted in a 0.0% solution gap, further affirming the model’s reliability. The best-bound scores indicated no significant differences in these two procedures, demonstrating that the solutions were ideal within the allowed tolerances.