The Optimization of Resource Allocation Based on Process Mining

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Through a review on literature about optimal allocation of higher education resources in China in recent years, the author summarizes the status quo and trends of research on higher education resource allocation both at home and abroad. Especially, the author sorts out existing literature at home and makes an inductive analysis in the following aspects: the concept of optimal allocation of higher education resources, the method of resource allocation, existing issues of resource allocation and corresponding research on countermeasures, which lays a foundation for writing relevant articles for subsequent researchers.

The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge computing. Cloud-edge-end collaborative computing leverages cross-layer task offloading to alleviate edge node resource contention and improve task scheduling efficiency. However, existing methods generally neglect the joint optimization of task offloading, resource allocation, and priority adaptation, making it difficult to balance task execution and resource utilization under resource-constrained and competitive conditions. To address this, this paper proposes a two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA). In the first stage, an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm integrated with a Priority-Gated Attention Module (PGAM) enhances the robustness and accuracy of offloading strategies under dynamic priorities; in the second stage, the resource allocation problem is formulated as a single-objective convex optimization task and solved globally using the Lagrangian dual method. Simulation results show that DPTORA significantly outperforms existing multi-agent reinforcement learning baselines in terms of task latency, energy consumption, and the task completion rate.

To support any services in all-IP networks, it is necessary to allocate both computing resource and network resource (bandwidth) simultaneously and effectively. The authors proposed the joint resource allocation method for multiple resource types, assuming the non-delay resource allocation. Although the proposed method can achieve an efficient use of resources, it may result in an unfair use of resources in which resources may be monopolized by a specific service. Therefore, the authors proposed the fair joint multiple resource allocation method which tries to equalize the total amount of resource allocated to each service. This paper proposes to enhance the fair joint resource allocation method, assuming three or more services and unbalanced generation traffic. It is demonstrated by basic simulation evaluations that the enhanced fair joint resource allocation method could enable fair resource allocation among multiple services without a large decline in resource efficiency.

In current networks, large flows often co-exist with small flows and consume a significant proportion of the network bandwidth. This imposes a challenge on network resource management. Various hybrid switching architectures have been proposed in recent years aiming to address this challenge and improve efficiency and resource utilization. However, there is still a need for means for design and dimensioning of resources among multiple switching planes. In this work, we first argue that the resource allocation in hybrid switching systems is fact the interaction between three components: resource allocation, traffic partitioning and performance (or cost) constraints. We then introduce a framework called Blocking-LOss Curve (BLOC) to address the resource allocation problem in hybrid switching systems. BLOC can capture the behavior of hybrid switching systems under different resource allocation strategies. This framework also allows us to identify the feasible region for resource allocation, within which performance metrics like the packet loss and request blocking requirements can be satisfied. An optimal resource allocation strategy may be found based on the feasible region. We also discuss the application of BLOC in hybrid switching Data Center networks.

PurposeHuman resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining.Design/methodology/approachA systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected.FindingsHuman resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies.Originality/valueThis study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors’ knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

recipitation shortage and the consequent loss of several water resources, as well as the population growth, are the most important problems in arid and semi-arid regions like Iran. The providence of basic tools for optimal water resources management is considered as one of the main solutions to this problem. Since the agricultural sector is the main user of water resources, the present study presented a model based on an artificial neural network method for optimal allocation of water resources in the agricultural sector during the statistical period of 2007-2016. The objective function was determined for each product in the agricultural sector as well as product performance, each product revenues, and cultivated area of the demand function. Maximization of the objective function (to maximize economic profits) and optimal allocation of water resources were; then, conducted by using the neural network. The results of the application of the artificial neural network method to the problem of optimal water allocation showed that, in this section, higher revenues could be obtained through economic policies as well as changing the pattern of cultivation. Furthermore, the results revealed that about 44 percent of the optimal allocation revenues of water resources ($115 billion) were improved between the agricultural sectors, compared to the current situation, by applying a coefficient of 0.9 compared to two coefficients of 0.75.

Construction and application of a refined model for the optimal allocation of water resources — Taking Guantao County, China as an example

A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications

In recent years, foreign research on the optimal allocation of supply chain resources and operational coordination in the digital economy environment has also made more and more progress, and the current status of domestic research is in the development stage, and supply chain management in the context of the digital economy has become another research hotspot.In this regard, this paper studies the application of Hamiltonian circuit in supply chain resource allocation from three aspects, such as product pricing strategy, supply chain management optimization and consumer behavior, and constructs the optimal resource allocation model according to the steps of resource allocation establishment.Through the Hamiltonian loop algorithm, the revenue function of cross-border ecommerce supply chain services is obtained.Using the Hamiltonian loop algorithm, the optimal price model of supply chain logistics is simulated and simulated experiments are carried out, and the optimal logistics and distribution services will be priced at about 225 under the high competition situation with competition coefficient 0.6 k . Under the optimal allocation of supply chain resources, analyzing the user's satisfaction, retail customers' satisfaction with the supply of goods in 2021 is 81.806 points, higher than the province's 0.913 points, and the experimental results show that the resource allocation model can meet the needs of the customers, making the supply and demand of the product resources more balanced, which argues the scientific nature and reliability of this study.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

Optimizing sepsis care through heuristics methods in process mining: A trajectory analysis

For a multiuser cognitive OFDM network, most existing adaptive resource allocation methods focus solely on fixed data-rate requirement (QoS guaranteed) or variable data-rate (no QoS guaranteed) conditions without any fairness consideration. In this paper, we investigate the resource allocation problem in a heterogeneous cognitive network with different QoS requirements. By decomposing the problem as a convex optimization, an optimal time-sharing resource allocation method is proposed to maximize the throughput of cognitive network under both subcarrier interference temperature limits and heterogeneous data-rate requirements. Finally, the performance of our proposed resource allocation algorithm is investigated by numerical results.

In this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

The conventional resource allocation method for civil aviation security post competency course reform is based on post elements, and the allocated resources are more considering the post level, which can not meet the adaptability demand of resource allocation. Therefore, a resource allocation method based on big data multimedia technology is designed for the reform of civil aviation security post competency course. We determine the set of big data cluster elements for resource allocation of security inspection job competency course, and deeply understand the connotation of civil aviation security inspection job competency, so as to adjust the resource allocation strategy at any time. Based on the big data multimedia technology, we construct a resource allocation model for the reform of security inspection job competency course, collect data related to security inspection job competency, and comprehensively assess the job competency. The optimal parameters of resource allocation for the reform of civil aviation security post competency course are adjusted to adapt to multiple course reform scenarios by adjusting the type, quantity and quality of course resources, the skill level, learning speed and willingness to learn of the security personnel, as well as the duration, frequency and mode of the training courses. Comparative experiments are used to verify that the method is more effective in resource allocation and can be applied in real life.