Allocation Approach Research Articles

Circular-Economy-Based Approach to Utilizing Cardboard in Sustainable Building Construction

Circular-economy-based sustainability approaches in construction are gaining wide acceptance due to the volume of waste generation and increasing demand for natural materials. Propelled by the recent timber shortage in Australia and the issues of waste management of cardboard, this study aims to analyse the possibilities of using cardboard as a construction material, based on its initial strength and multiple recycling options. A systematic review of research papers published in the last 40 years has been undertaken using a single keyword search to select the database. The review is presented in terms of the characteristics of the cardboard, dimensional stability, durability, structural strength, design, and analysis of cardboard. Recurring themes are evaluated using a latent Dirichlet allocation approach to identify the factors that ascertain the suitability of cardboard. Analysis reveals that despite certain constraints, such as water absorption and fire resistance, cardboard can be used as a replacement for timber by overcoming such limitations. This observation has benefits for the construction industry and the recycling industry. This study found that cardboard adheres to the circular economy principles, which should inspire policymakers. The paper concludes by highlighting the current circumstances and scientific challenges that impede the usage of cardboard in construction and recommends potential works needed to address these challenges for the benefit of practitioners and researchers.

Ranking and selection for pairwise comparison

AbstractIn many real‐world applications, designs can only be evaluated pairwise, relative to each other. Nevertheless, in the simulation literature, almost all the ranking and selection procedures are developed based on the individual performances of each design. This research considers the statistical ranking and selection problem when the design performance can only be simulated pairwise. We formulate this new problem using the optimal computing budget allocation approach and derive the asymptotic optimality condition based on some approximations. The numerical study indicates that our approach can reduce the number of simulations required to confidently identify the best design.

An improved hybrid approach for the simultaneous allocation of distributed generators and time varying loads in distribution systems

Distributed Generation (DG) studies are generally conducted considering a single type of DG units integrated with the Distribution System (DS). However, these studies may not evaluate optimum benefits offered by the DG integration. This paper presents a novel framework for individual and simultaneous allocation of different types of DG units in DS while considering varying load demand and probabilistic generation of DG units. A Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) based new hybrid method that utilizes a single controlling parameter is proposed for simultaneous allocation of DG units, aiming to minimize a multi-objective index. The strength of the proposed SSA-PSO hybrid approach is validated on seventeen benchmark functions and the performance of the novel framework for optimal allocation of DG units is validated through implementation on the 69-bus system. The results demonstrate that the proposed hybrid approach performs better as compared to other meta-heuristics techniques. Moreover, simultaneous allocation significantly reduces the multi-objective index as compared to individual DG units’ integration.

A Systematic Approach to Task Assignment and Production Planning in Disassembly with Employee Skills

An emerging shortage of resources fosters a development for strategies for the circularity of products and resources. Due to the different states of returned end-of-life products, the complexity for employees in disassembly increases. This work aims to provide an approach for an optimal allocation of disassembly tasks to individual employees and therefore enable a basis for planning and control in disassembly. At first, a task description is provided based on which a standardized time for operations is considered. Second, a link is created between the task description and the product state. Depending on the product state the time determined prior can be adjusted. Third, human skills are considered in manufacturing. It is assumed that within a production system there are different employees with different, developing skill sets. Based on specific skills, a task-to-person-is conducted. Using the information gathered, a Digital Twin (DT) that includes the human nature of the employees and their state is created to enable a simulation of tasks and thereby also a learning system for “first-time-seen” products. When facing complex tasks, the cognitive load and human's fatigue are decisive for performance and thereby the time required for execution. Completing these steps, a multistage concept is created that enables a more precise disassembly planning that can be shown in the case study on the example of components of electric vehicles.

Study of goal programming approach in resource allocation in acute care hospitals

In this study, the goal programming approach is discussed which is used in hospitals to allocate resources. Two linear goal-programming models are included in the process. One model fixes the ratio of different types of cases that doctors see each year, while the other translates case mix decisions into a set of practice changes for doctors. Decision-makers may use the models to find the optimal case mix that maximizes profitability for the institution while minimizing negative impacts on physician compensation and clinical workflow.

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for computing resources. At the same time, the overall power consumption of data centers has been rising beyond 1&#x0025; of worldwide electricity consumption. The usage of renewable energy in data centers contributes to decreasing their carbon footprint and overall electricity costs. Several green-energy-aware resource allocation approaches have been studied recently. None of them takes advantage of the joint migration of <i>jobs</i> and <i>energy</i> in green data centers to increase energy efficiency. This paper presents an optimization approach for energy-efficient resource allocation in mini data centers. The observed momentum around edge computing makes the design of geographically distributed mini data centers highly desirable. Our solution exploits both virtual machines (VMs) and energy migrations between green compute nodes in mini data centers. These nodes have energy harvesting, storage, and transport capabilities. They enable the migration of VMs and energy across different nodes. Compared to VM allocation alone, joint-optimization of VM and energy allocation reduces utility electricity consumption by up to 22&#x0025;. This reduction can reach up to 28.5&#x0025; for the same system when integrating less energy-efficient servers. The gains are demonstrated using simulation and a Mixed Integer Linear Programming formulation for the resource allocation problem. Furthermore, we show how our solution contributes to sustaining the energy consumption of old-generation and less efficient servers in mini data centers.

Capacity Allocation of Hybrid Power System with Hot Dry Rock Geothermal Energy, Thermal Storage, and PV Based on Game Approaches

This study utilizes hot dry rock (HDR) geothermal energy, which is not affected by climate, to address the capacity allocation of photovoltaic (PV) -storage hybrid power systems (HPSs) in frigid plateau regions. The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage (TS) plant. An HPS consisting of an HDR plant, a TS plant, and a PV plant is proposed. Game approaches are introduced to establish the game pattern model of the proposed HPS as the players. The annualized income of each player is used as the payoff function. Furthermore, non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS. Finally, the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin, Qinghai, China as a case study. The results show that in the proposed non-cooperative game approach, the players focus only on the individual payoff and neglect the overall system optimality. The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player. Thereby, the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity (EFC) of PV plants, which in turn indirectly guarantees the reliability of grid operation.

Battle Royale Optimization-Based Resource Scheduling Scheme for Cloud Computing Environment

Cloud computing (CC) is developing as a powerful and flexible computational structure for providing ubiquitous service to users. It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment. The variation of software and hardware resources were combined and composed as a resource pool. The software no more resided in the single hardware environment, it can be executed on the schedule of resource pools to optimize resource consumption. Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation. This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment (BRORSS-CCE) technique. The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan. In the BRORSS-CCE technique, the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival. The BRORSS-CCE technique can be employed to balance the load, distribute resources based on demand and assure services to all requests. The experimental validation of the BRORSS-CCE technique is tested under distinct aspects. The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.

Sexual mixing patterns in men who have sex with men: network approaches for smart resource allocation.

Age-based sexual mixing patterns in men who have sex with men (MSM) can greatly inform strategic allocation of intervention resources to subsets of the population for the purpose of preventing the greatest number of new HIV infections. Egocentric network data collected from MSM participating in annual HIV sentinel surveillance surveys were used to assess age-dependent mixing and to explore its epidemiological implications on the risk of HIV transmission risk (among those HIV-infected) and HIV acquisition risk (among those not infected). Mixing in this sample of 1605 Chinese MSM is relatively age assortative (the average of values expressing the degree of preferential mixing were 2.01 in diagonal cells vs 0.87 in off-diagonal cells). Expected numbers of HIV acquisition were highest in the 20-24years age group; those for HIV transmissions were highest among 25-29year olds. The risk of both acquisition and transmission was highest in age groups that immediately follow the most commonly reported ages of sexual debut in this population (i.e. age 20). These findings suggest that combination prevention resources should be targeted at younger MSM who are at higher risk of both transmission and acquisition. Programs may also do well to target even younger age groups who have not yet debuted in order to establish prevention effects before risky sexual behaviours begin. More research on optimal strategies to access these harder-to-reach subsets of the MSM population is needed. Findings also support ongoing efforts for public health practitioners to collect network data in key populations to support more empirically driven strategies to target prevention resources.

Energy-Efficient Sparsity-Driven Speech Enhancement in Wireless Acoustic Sensor Networks

Wireless acoustic sensor network (WASN) has shown a superiority over conventional microphone arrays in many aspects. There exists an important tradeoff between the performance and power consumption, as usually the sensors are power driven with a limited amount of battery resource. Given a prescribed performance bound, in literature sensor selection (SS) and rate allocation (RA) methods can be leveraged to optimize the energy efficiency. In this work, we propose a joint rate allocation and sensor selection (RASS) approach to simultaneously optimize the sensor subset and rate distribution, which is formulated by minimizing the total transmission power in terms of selection and bit-rate variables and constraining the residual noise power. It can be shown that under a set of linear constraints on beamforming, the linearly-constrained minimum variance (LCMV) beamformer is the optimal noise reduction filter. Based on this, the RASS reduces to a mixed semi-definite and bilinear programming problem, which is then solved using a two-step algorithm. As the selection and bit-rate unknowns are bilinear, we first consider to optimize their product, resulting in an upper bound of RASS. Then, we use McCormick envelopes to relax the bilinear constraint, resulting in a linear program. The final selection and bit-rate solutions are obtained by posterior randomized rounding. It can be shown that SS and RA are special cases of the proposed RASS. Numerical results using simulated WASNs validate the power efficiency of the proposed method as well as the robustness against dynamic factors.

A study of delays in indian road construction projects under different contracts

Road construction projects are complex and dynamic management processes that face many challenges, leading to delays, mainly owing to cost overruns and schedule extensions during the construction phase. Forensic analysis of past project data was examined, and construction project performance was reviewed. This paper presents an analysis of delays in Indian road construction projects under different contract modes. Each contract mode has distinct contractual relationships, risk allocation, and projection-building approaches that influence project execution. To identify the cause of the delay, allocating responsibilities among all project participants' duties and roles under the contract obligation is crucial. Numerous factors can lead to delays, including weather conditions, unforeseen ground conditions, design changes, permit issues, and contractor performance. These delays have a significant impact on project timelines, budgets, and overall project success. This study aims to explore the major factors resulting in project delays and their potential improvements.

Hierarchical Planar Platoon Control Toward Tracking Safety for Distributed-Driven Vehicles

This paper deals with the planar platoon control problem of distributed-driven electric vehicles considering modeling deviations and driving disturbances. The follower vehicle is arranged to track the leader vehicle on curved roads with high tracking accuracy and driving security. A hierarchical integrated control framework is proposed based on disturbance-rejection robust model predictive control (DRRMPC) strategy and optimal control allocation (OCA) approach. The upper layer is a feedforward control layer, where a linear time-varying (LTV) model predictive controller (MPC) produces constrained predictive control towards a nominal model. The time-interval-based reference states are obtained from a “shadow following” trajectory generator through vehicle-to-vehicle (V2V) communication. In the middle layer, the feedback control consisting of a robust controller (RC) and the disturbance compensation out of an extended state observer (ESO), take effect to reconfigure the LTV vehicle model into the nominal model with an off-line calculated constraint tube. The bottom layer performs optimal control allocation among decoupled motor torques and wheel angles towards minimal tire force utilization. Hardware-in-the-Loop (HIL) experiment validates the effectiveness and practicability of the proposed platoon control strategy.

DATA POISON DETECTION USING MACHINE LEARNING

Distributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. In this paper, we classify DML into basic-DML and semi-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. We firstly put forward a novel data poison detection scheme for basic-DML, which utilizes a cross-learning mechanism to find out the poisoned data. We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. Then, for semi-DML, we present an improved data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed. Simulation results show that the proposed scheme can significantly improve the accuracy of the final model by up to 20% for support vector machine and 60% for logistic regression in the basic-DML scenario. Moreover, in the semi-DML scenario, the improved data poison detection scheme with optimal resource allocation can decrease the wasted resources for 20-100%.

Performance Analysis of Support Vector Machine Learning Based Carrier Aggregation Resource scheduling in 5G Mobile Communication

The 5G cellular system will enable heterogeneous networks that incorporate 5G, 4G, Wi-Fi, and other telecommunication connectivity. This will result in huge changes in existing infrastructure and architecture network. Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine Type Communication (mMTC) are some of the 5G's basic application cases. The variety of heterogeneous networks makes it challenging to manage, network traffic, inter-cell, and intra-cell interfaces. Due to diverse interfaces and data transmission rates, network traffic in heterogeneous networks can be difficult to manage and control. Advanced techniques including Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) are used in the 5G Network to overcome such issues. In this paper, a Support Vector Machine (SVM) learning-based resource allocation approach for heterogeneous networks is designed. A machine learning method is proposed to enhance the performance of 5G NR mobile networks. This method is used to upgrade the throughput of the overall networks. The SVM learning function detects by splitting network data of channel state information (CSI). The considered channel state data of two variables as labels with X1 and X2 and trained using SVM-LINEAR, SVM-RBF, and SVM-SIGMOID. The proposed SVM classifier by splitting data for training 80% and testing 20%. A Static Error rate is set as a threshold limit at 10% (0.10) of BLER (Block Error Rate). If the error rate passed the threshold, then update Carrier Aggregation with aggregated component else set carrier aggregation with ordinary carrier component values to Zero. The proposed approach further compares with the round robin (RR), best channel quality indicator (BCQI), fractional frequency reuse (FFR), MU-MIMO, and beamforming resource scheduling techniques. The innovative results indicated that the proposed approach enhanced spectral efficiency, average cell throughput, edge throughput peak throughput, and fairness index.

A new tolerance allocation approach based on decision tree and Monte Carlo simulation

The rise of artificial intelligence techniques provides new opportunities in the robust design and tolerancing fields. These technological growths require adapting new practices. In this paper, a new tolerance allocation approach based on supervised machine learning (decision tree) and Monte Carlo simulation is proposed. The most important branch of the decision tree which is generated based on the results of the Monte Carlo simulation, is analysed and the best tolerances to be allocated are defined in terms of quality/cost trade-off. The effectiveness of the proposed approach is demonstrated and discussed through a non-linear problem: a Jansen linkage study.

Knowledge-based flexible resource allocation optimisation strategy for multi-tenant radio access network slicing in 5G and B5G

Formalisation of the network slice as a resource allocation unit is considered a promising aspect that enables scalable and flexible resource allocation among many tenants in 5G and beyond 5G (B5G) communication networks. However, the user traffic has to be passed through the central administration for processing, which leads to latency problems. To solve this problem, recent research works have suggested fixed central-to-edge resource allocation ratios as per the service type. However, this approach leads to over-provisioning of some resources. This paper provides a flexible resource allocation approach for 5G slice networks operating in a heterogeneous environment with multiple tenants and tiers. A radial basis-neural network is used to convert abstract specifications of simulation activities into precise resource needs, and then a genetic algorithm-based flexible multi-resource allocation scheme is proposed, where a versatile optimisation framework is used. The results show that the proposed approach outperforms such existing schemes.

Risk Budgeting: A Tactical Asset Allocation Approach for Retirement Reserve Funds in Morocco

Risk Budgeting: A Tactical Asset Allocation Approach for Retirement Reserve Funds in Morocco

Deep Reinforcement Learning Based Resource Allocation Approach for Wireless Networks Considering Network Slicing Paradigm

In this paper, we present an approach for resource scheduling in wireless networks based on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) Reinforcement Learning (RL) algorithm. More specifically, we propose a joint power and Scheduling Block (SB) allocation algorithm for networks with NS. The reinforcement learning algorithm applied to the resource allocation problem is formulated using state transitions regarding the system dynamics. We also present an algorithm, namely Network Slicing based on Reinforcement Learning (NSRL) that combines the proposed reinforcement learning based resource allocation with an approach based on reservation and sharing of resources among the slices where each RL agent acts in one slice. Simulations are carried out considering User Equipments (UEs) within a small cell coverage area - (Small Cells) with different Modulation and Coding Schemes (MCS) standardized by the 3rd Generation Partnership Project (3GPP) based on a simplified NS scenario with fifth generation wireless network (5G) characteristics. In the simulations, two slices are considered for the UEs: one considering Ultra-reliable and Low Latency Communications (URLLC) and other related to enhanced Mobile Broadband (eMBB) services. Simulation results show that the NSRL algorithm efficiently allocates power and SBs, outperforming other algorithms in the literature.

Assessing the cost-effectiveness of annual COVID-19 booster vaccination in South Korea using a transmission dynamic model.

We evaluated the cost-effectiveness of South Korea's planned annual coronavirus disease 2019 (COVID-19) booster campaign scheduled for October 2023. An age-structured mathematical model was used to analyze the public impacts and cost-effectiveness of vaccination across three vaccination strategies: uniform allocation and prioritizing those over 65 or those over 50 years old. We calculated the incremental cost per quality-adjusted life year (QALY) from both healthcare and societal perspectives. The maximum vaccine cost for cost-effectiveness was also identified. Our analysis highlights the cost-effectiveness of South Korea's annual COVID-19 vaccination program in mitigating health and economic impacts. The most cost-effective strategy is uniform vaccine allocation, offering the lowest incremental cost-effectiveness ratio (ICER) at US$ 25,787/QALY. However, with a relatively high attack rate, the strategy prioritizing individuals over 65 years emerges as more cost-effective, lowering the ICER to US$ 13,785/QALY. Prioritizing those over 50 was less cost-effective. All strategies were cost-saving from a societal perspective, with cost-effectiveness being more sensitive to vaccine price than to its effectiveness. Our results imply a potential strategy shift in current vaccination plan, with uniform vaccine distribution being more cost-effective than prioritizing older adults. Early estimation of viral transmissibility and vaccine effectiveness is crucial in determining the most cost-effective vaccine allocation approach.

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks, and hence as technological solutions for future generations, from this perspective. As a result, combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment. The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication. Coverage maximization, power control, and enhanced connection quality are the three steps of the proposed model. To satisfy the desired signal-to-noise ratio, the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural, suburban, and urban macro deployment scenarios of the ITU-R M.2135 standard (SNR).