Efficient Resource Allocation Research Articles

Can the Difference of the Transfer Trice of Industrial Land "Bidding, Auctions and Listings" Promote the Transformation and Upgrading of Regional Industries?

The purpose of this study is to explore the impact of the difference in the transfer price of industrial land on the regional industrial transformation and upgrading under the "bid and auction" system. Research methods: panel data fixed effect model. Research results: Improving the differentiation of industrial land transfer price is conducive to regional industrial transformation and upgrading, and this conclusion has passed a variety of robustness tests. Research conclusions: First, formulate and improve the promotion of industrial land transfer price differentiation strategy. The second is to establish and improve the enterprise entry and exit mechanism to improve the efficiency of industrial land resource allocation. The third is to further improve the secondary market trading system of industrial land, and actively explore the circulation mechanism of stock industrial land to ensure the reasonable flow and efficient use of industrial land resources.

Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks.

Vehicular clouds represent an appealing approach, leveraging vehicles' resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.

Walmart sales prediction based on random forest model and application of feature importance

Sales forecasting is crucial for efficient resource allocation and inventory management in retail. This study employs Random Forest to predict weekly sales for 45 Walmart stores, leveraging a diverse dataset with store-specific sales and external factors. Through meticulous preprocessing and model application, one achieves outstanding accuracy, with a Weighted Mean Absolute Error (WMAE) as low as 1.2030 and an impressive accuracy rate of 98.8%. Additionally, integrating feature importance ranking sheds light on influential variables in sales forecasting. This study provides a blueprint for developing precise and adaptable sales forecasting models, offering profound significance for the retail industry. It underscores the effectiveness of machine learning techniques, e.g., Random Forest and insightful feature engineering in achieving highly accurate predictions. By enhancing the industry's understanding of intricate sales dynamics, this research contributes to optimizing resource allocation, inventory management, and strategic planning. Ultimately, it drives operational efficiency and success in the dynamic landscape of the retail sector.

Evaluation of individual innovation and entrepreneurship effect based on linear space model and grey correlation

This study goes into the complexities of innovation and entrepreneurial skills by developing a detailed linear model and exploring the essential components that make up these talents. A multi-objective function model is presented to assess the effectiveness of using and distributing educational resources in this setting. For this assessment, the study uses the grey correlation method. Through a series of experimental simulations, the study demonstrates that the optimisation approach significantly improves the utilisation and allocation efficiency of educational resources committed to innovation and entrepreneurship by 18.72% and 20.98%, respectively. This results in a more balanced resource utilisation, which helps to enhance the allocation of educational resources. A major conclusion of this study is the correlation value of 0.3177 with ideal entrepreneurship, which indicates a high degree of excellence in innovation and entrepreneurship education reached across the population analysed..

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Unlocking the future of space resource management through satellite remote sensing and AI integration

This scientific study investigates how artificial intelligence (AI) and satellite remote sensing could work together to transform space resource management. As human activity in space continues to expand, the effective and sustainable utilization of celestial resources is of paramount importance. It begins by highlighting the evolution of space exploration and the growing interest in resource extraction beyond Earth's boundaries. It emphasizes the critical role of resource management for the success of future space missions.Advances in technology in recent times, including the implementation of in-situ resource utilization (ISRU) and self-governing mining systems, have significantly enhanced the possibility of extracting resources from celestial entities. While the economic feasibility of space mining is compelling, environmental concerns, sustainability, and intricate governance challenges necessitate careful examination.An approach for comprehending the complex field of space resource management is presented in this research. It delineates a clear roadmap of its content, offering a succinct summary of key objectives and sections, facilitating reader comprehension. The methodology includes a literature review of relevant research materials from Web of Science, Scopus, and Google Scholar sources. It investigates celestial bodies' resource potential, main challenges, and environmental implications.Preliminary contributions include one analysis of the potential applications of satellite remote sensing and AI technologies in mitigating these challenges, highlighting their pivotal roles in resource identification and management, proposing terrestrial solutions applicable to space resource management. The integration of AI into space resource management is discussed, emphasizing its potential to enhance safety protocols, resource allocation, and overall efficiency. Furthermore, it discusses international agreements and treaties governing space exploration and resource utilization while advocating for cooperation among space-faring nations and stakeholders. It concludes discussing the implications, benefits, challenges, areas requiring further attention, and future directions of integrating these technologies in space resource management.

Wi‐Fi 6‐based home area network for demand response in smart grid

SummaryThe past decade has brought prolific developments to power systems that range from large‐scale renewable integration to digital twins. Modern power systems consist of highly variable generation clusters. Hence, monitoring and prediction of power generation has become a vital part of today's power systems. On the other hand, detailed real‐time information of power usage is also a key parameter for the control algorithms used for the demand side management. With this information flow, power systems become smart grids which offer improved service and enable many advanced operations. In this regard, the home area network (HAN), which collects the power consumption/generation of household devices, plays a key role. So far, a number of technologies such as ZigBee, Z‐Wave, and Power Line Communication (PLC) have been considered for the HAN. Yet, widely used wireless local area network (WLAN) technology, Wi‐Fi, was not identified as a prominent candidate due to the limitations in its latency and reliability. However, with the introduction of the IEEE 802.11ax standard, Wi‐Fi 6 was developed, and it demonstrates guaranteed latency and reliability for its users. Thus, with its widespread deployment in households, Wi‐Fi is becoming a natural candidate for HAN. In this article, we present a comprehensive HAN protocol based on Wi‐Fi 6 which can achieve guaranteed latency and reliability for HAN users. The results show that when an efficient resource allocation mechanism is in place, using Wi‐Fi for HAN does not adversely affect the other Wi‐Fi users in the normal WLAN.

Data-driven flexibility evaluation methodology for community integrated energy system in uncertain environments

Flexibility operation and evaluation have become central and critical for grid integration of high percentage of renewable energy generations (REGs). However, the interactions between multiple energy forms in community integrated energy system (CIES) and the uncertainty of REG significantly complicate the quantification of flexibility. To this end, a novel two-stage data-driven flexibility evaluation methodology is proposed for CIES. The first stage performs optimal scheduling of multiple flexible resources, where the flexible thermal comfort requirements of users in integrated response demand are considered by introducing a predicted mean vote index. The second stage refines the flexibility supply model for flexible resources, and carves out the system flexibility supply domain by enveloping the net load. To overcome the uncertainty of REG, a data-driven scenario analysis approach is developed, which fully captures the renewable output characteristics under different climate environments and avoids complicated explicit feature model construction. Furthermore, a set of evaluation metrics is proposed from the aspect of direction, amount, and frequency to quantify the flexibility. The effectiveness and accuracy of the proposed methodology are verified by applying it to a modified CIES located in North China. The sensitivity analysis of multiple time scales and flexible resources to operational flexibility and economy of CIES is presented, which provides guidelines for the efficient allocation of flexible resources and expand planning of CIES.

Socioecological justice in urban street greenery based on green view index-A case study within the Fuzhou Third Ring Road

Urban green space equity relates to the efficient allocation of natural resources and the equalization of public service facilities. Street (road) greenery provides substantial ecological, social and cultural benefits. Thus, in this study, a subdistrict-level evaluation framework for the fairness of the spatial distribution of street greenery was proposed, taking a case study within the Third Ring Road of Fuzhou City in Fujian, China. Street view images may capture the green information in a vertical dimension for the indirect representation of people’s perspective on the ground. The green view index, which was estimated based on Baidu Street View images, was employed to represent the urban street greenery, and the results were combined using deep learning technology. The Gini coefficient, share index and location entropy were used as evaluation indicators for the fairness of the spatial distribution of the street green view index. Furthermore, this framework combined socioeconomic data and population census data to explore the correlation among socioeconomic status, age, and evaluation index at the subdistrict level. In addition, we analyzed street greenery distribution inequalities from the perspective of socioecological justice. The results showed that in Fuzhou, there is a significant correlation among the Gini coefficient, green view index and socioeconomic status. In addition, subdistricts with a lower green view index have a less equitable street greenery distribution, people with low socioeconomic status may suffer from green injustice, and seniors have a lower accessibility to street green space than people with the average social status. Our analytical approach is applicable for other cities, and the findings are useful for greenery spatial planning processes and evaluating construction effects.

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches

The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.

Open government data and resource allocation efficiency: evidence from China

ABSTRACT Data are among the most valuable resources. This paper, utilizing the China Tax Survey Database from 2009 to 2016, investigates the impact of Open Government Data (hereafter, OGD) on the efficiency of resource allocation among firms within the Chinese context. The findings indicate that OGD significantly reduces the deviation in firms’ factor inputs, thereby enhancing the efficiency of resource allocation. This is primarily achieved through the optimization of government-business relationships, the reduction of rent-seeking costs, and the decrease in information asymmetry. Given the inclusive nature of OGD, it has a more pronounced positive impact on disadvantaged firms and cities. Furthermore, while OGD directly improves representative firm’s TFP, its greater economic impact lies in elevating aggregate TFP by enhancing resource allocation efficiency. In the current global economy, highly dependent on data-driven processes, the conclusions of this paper provide a novel perspective on the role of OGD in enhancing productivity. This has significant implications for accelerating the implementation of open data policies and enhancing economic efficiency.

OPTIMIZING FMCG SUPPLY CHAIN DYNAMICS: A NOVEL FRAMEWORK FOR INTEGRATING OPERATIONAL EFFICIENCY AND CUSTOMER SATISFACTION

This paper proposes a groundbreaking theoretical model specifically designed for the FMCG sector, focusing on optimizing supply chain dynamics to balance operational efficiency with customer satisfaction. Drawing on hands-on strategies and problem-solving abilities, the framework aims to offer practical solutions for common challenges faced by FMCG operations managers, such as inventory management, demand forecasting, and efficient resource allocation. The fast-moving consumer goods (FMCG) industry is characterized by intense competition, short product life cycles, and rapidly changing consumer preferences. In this challenging environment, optimizing supply chain dynamics is crucial for FMCG companies to achieve operational efficiency and enhance customer satisfaction. This concept paper presents a novel framework for integrating operational efficiency and customer satisfaction in FMCG supply chains, aiming to provide insights and strategies for FMCG companies to improve their supply chain performance. The framework emphasizes the importance of aligning supply chain strategies with customer needs and preferences. By focusing on key areas such as inventory management, distribution network efficiency, and technology integration, FMCG companies can improve their supply chain agility and responsiveness. Additionally, the framework highlights the importance of leveraging data analytics and technology to enhance supply chain visibility and decision-making. Implementing the framework requires collaboration among stakeholders, including suppliers, manufacturers, distributors, and retailers. By working together to optimize supply chain processes and enhance customer service, FMCG companies can gain a competitive advantage in the marketplace. Overall, the framework offers a comprehensive approach to optimizing FMCG supply chain dynamics, with the ultimate goal of improving operational efficiency, reducing costs, and enhancing customer satisfaction.
 Keywords: FMCG, Supply Chain, Customers, Framework.

The Impact of Digital Transformation on the High-quality Development of the Manufacturing Industry

The digital transformation of manufacturing industry facilitates enterprises to access and utilize data information to improve the efficiency of resource allocation, expand product and service business models, and accumulate human capital stock to empower the high- quality development of manufacturing industry by creating a large number of databases. This paper adopts panel data of 30 provinces and cities in China from 2011-2020, and uses system GMM to verify the correlation between two variables. It also introduces environmental regulation as a threshold variable to analyze the threshold effect. The results of the study indicated that: firstly, the association between digital transformation of manufacturing industry and high-quality development of manufacturing industry shows a U-shaped relationship, secondly, the digitalization of manufacturing industry exhibits different impact effects on the four dimensions of high-quality development of manufacturing industry, and lastly, only when the environmental regulation is within a certain level, it can have a driving effect on high-quality development of manufacturing industry. Therefore, it is important to grasp the strategic opportunities of digital economy, strengthen the integration of digital economy and traditional industries, accelerate the digital transformation of manufacturing enterprises, and improve the high-quality development of China's manufacturing industry.

Efficient resource allocation in heterogeneous clouds: genetic water evaporation optimization for task scheduling

Efficient resource allocation in heterogeneous clouds: genetic water evaporation optimization for task scheduling

Process Mining in Sepsis Treatment Management: A Step-by-Step Approach to Discovery

This article explores the management of sepsis treatment processes, a prominent contributor to hospital morbidity and mortality on a global scale. The research delves into the application of process mining techniques during the discovery phase, utilizing anonymized data from Mannhardt and Blinde (2017). The impetus for this investigation stems from the imperative to pinpoint inefficiencies within sepsis treatment protocols, with the overarching goal of enhancing care quality while curbing expenses. A primary challenge lies in the need for a comprehensive overview of hospital processes for sepsis management. The study employs discovery algorithms such as Alpha Miner, Heuristic Mining, and DFG (Direct Flow Chart) to address this challenge. By leveraging these methodologies, the research endeavors to foster efficient allocation of costs and resources, elevate the standard of patient care, and foster operational efficacy within public health institutions.

Innovation Strategies and Technical Efficiency

In the innovation literature, little attention has been paid to technical efficiency as a measure of performance, despite the fact that technical efficiency is key to explaining firm productivity. In this study we analyze the influence of internal R&D, external R&D and R&D cooperation on the technical efficiency of the firm. The data used come from the Panel de Innovación Tecnológica 2016 for Spanish companies, from whose database we have selected the set of innovative manufacturing firms. To estimate the coefficients of interest we have constructed a knowledge production function, estimated using Stochastic Frontier Analysis (SFA) and the Heckman correction. The results indicate that both internal R&D and R&D cooperation have a positive and significant influence on technical efficiency, while external R&D has a significant negative relationship. Knowing this type of information in advance is very important for business managers and policy makers, since it facilitates decision-making on innovation strategies, as well as the formulation of public policies to support innovation leading to an efficient allocation of public resources.

Bank competition, government interest in green initiatives and carbon emissions reduction: An empirical analysis using city-level data from China

Finance plays a pivotal role in the transition to a sustainable economic development model. In China, the banking sector, a key component of its financial landscape, operates within an increasingly competitive environment, exerting profound effects on the efficient allocation of resources. This paper employs instrumental variables regression and text analysis techniques to meticulously examine the impact of bank competition on carbon emissions and elucidate the underlying mechanisms. We also investigate the synergistic influence of government engagement in green initiatives and bank competition on carbon emissions reduction, drawing insights from data encompassing Chinese cities during the years 2006 to 2019. Our empirical analysis unveils a significant carbon emissions reduction effect resulting from bank competition. This effect is primarily driven by the facilitation of green innovation and the promotion of manufacturing servitization. Notably, this impact is more pronounced in cities that are not dependent on resource extraction and those situated outside regional financial centers. Additionally, the level of government interest in green initiatives amplifies the carbon reduction effect of bank competition. This paper contributes to the enhancement of the banking market structure, aligning it with eco-friendly development goals.

Patient flow modeling and simulation to study HAI incidence in an Emergency Department

Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital—which hospital units patients visit and where they spend the most time since their admission into the hospital—can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022–June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

Pollution emission right trading policy and green total factor productivity

ABSTRACT Improving the quality of the ecological environment and the efficiency of green economy has become the new demands of China’s economic development. This paper takes the 2007 sulphur dioxide (SO2) emission trading pilot policy in China as a quasi-natural experiment, measures the green total factor productivity of 280 Chinese cities, and analyses the impact of emission right trading policy on urban green total factor productivity (GTFP). The results show that emission right trading policy has a significant role in improving urban GTFP. This effect differs at region, city level and economic circle. Emission right trading policy improves urban GTFP by promoting green technological innovation, improving resource allocation efficiency and optimising industrial structure. This paper shows a new perspective on cross regional and cross city emissions trading cooperation, providing policy enlightenment for deepening China’s environmental governance and promoting green and high-quality economic development.