Allocation Approach Research Articles

Review of task allocation for human-robot collaboration in assembly

ABSTRACT Due to the high cost pressure and the increasing variant diversity, the cooperation of humans and robots represents a promising technological solution to achieve higher flexibility and efficiency in assembly. It is therefore attracting significant interest from both researchers and practitioners. As a result, numerous reviews have been published addressing different aspects of human-robot collaboration, such as safety, interaction modalities, programming, and applications. However, in this paper, for the first time, the aspect of task allocation for collaborative assembly is methodologically examined through a systematic literature review. This paper presents the current state of the art in task allocation approaches, investigates the criteria for deciding on a suitable task assignment, and discusses challenges and future research needs. After filtering the 521 publications that resulted from the initial search process, 37 relevant publications were included in the analysis and grouped into a proposed classification consisting of two main categories, static and dynamic task allocation approaches. Based on the results of the literature review, this paper presents a reference model for human-robot collaborative assembly.

Genetic algorithm optimization based on manufacturing prediction for an efficient tolerance allocation approach

Genetic algorithm optimization based on manufacturing prediction for an efficient tolerance allocation approach

Memory Enabled Bumblebee-Based Dynamic Spectrum Access for Platooning Environments

In this paper, we propose a novel memory-enabled non-uniform sampling-based bumblebee foraging algorithm (MEB) designed for optimal channel selection in a distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed in a platoon operating environment. Given how bumblebee behavioral models are designed to support adaptation in complex and highly time-varying environments, these models can be employed by connected vehicles to enable their operation within a dynamically changing network topology and support their selection of optimal channels possessing low levels of congestion to achieve high throughput. As a result, the proposed VDSA-based optimal channel selection employs fundamental concepts from the bumblebee foraging model. In the proposed approach, the Channel Busy Ratio (CBR) of all channels is computed and stored in memory to be accessed by the MEB algorithm to make the necessary channel switching decisions. Two averaging techniques, Sliding Window Average (SWA) and Exponentially Weighted Moving Average (EWMA), are employed to leverage past samples and are evaluated against the no-memory case. Due to the high variability of the environment (e.g., high velocities, changing density of vehicles on the road), we propose to calculate the CBR by employing non-uniform channel sampling allocations as well as evaluate it using both simplified numerical and realistic Vehicle-to-Vehicle (V2V) computer simulations. The numerical simulation results show that gains in the probability of the best channel selection can be achieved relative to a uniform sampling allocation approach. By utilizing memory, we observe an additional increase in the channel selection performance. Similarly, we see an increase in the probability of successful reception when utilizing the bumblebee algorithm via a system-level simulator.

Three Processor Allocation Approaches towards EDF Scheduling for Performance Asymmetric Multiprocessors

With the rapid development of high-performance computing and parallel computing technology, by virtue of its cost-effectiveness, strong scalability, and easy programming, the multiprocessor system has gradually become the mainstream computing platform. Meanwhile, a growing number of researchers pay attention to the performance of multiprocessor systems, especially the task scheduling problem, which has an important impact on the system performance. Most of the current research works on task scheduling algorithms are based on the homogeneous computing environment. On the contrary, research works focusing on more complex performance asymmetric multiprocessor environments still remain rare. In this paper, we compare the effects of three earliest deadline first algorithms under different processor allocation strategies on performance asymmetric multiprocessors. We propose an efficient schedulability analysis for an allocation strategy that assigns high-priority tasks to the slowest idle processor. Experimental results show that the strategy of allocating processors with optimum speeds for high-priority tasks can schedule more task sets than the other two allocation strategies. The strategy that prioritizes the slowest processors for high-priority tasks has the smallest number of task migrations, and the strategy has the highest effective processor utilization.

A random access channel resources allocation approach to control machine‐to‐machine communication congestion over LTE‐advanced networks

SummaryThe Internet of Things (IoT) is becoming a reality, and one of the core elements to make this reality come true is machine‐to‐machine (M2M) communication. With the fast‐growing rate in which devices are being deployed, communication among them has become crucial to support the development of IoT applications. The Long‐Term Evolution‐Advanced (LTE‐A) is a potential access network for these M2M devices. However, the LTE‐A inherited characteristics from older cellular network standards, which were designed for human‐to‐human (H2H) and human‐to‐machine (H2M) communication. Accordingly, supporting M2M communication poses some challenges to LTE‐A, with a highlight of the congestion and overload problems in the radio access network (RAN) during the random access channel (RACH) procedure. Such a problem arises due to the large number of devices sending access‐request messages to the network and the limited number of resources to satisfy this new demand. In this paper, we introduce a solution to mitigate the impact of M2M communication in the LTE‐A network. Precisely, we model how to divide the random access resources into different types of devices as a bankruptcy problem. Then, we propose two solutions: (i) A game theory approach to formulate the bankruptcy problem as a transferable utility game, and (ii) an axiomatic method where some elements are considered for judging the amount of resources each class should receive. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses, and priority respect among the different classes of devices.

An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

SummaryEfficient utilization of resources is a challenging task in cloud computing due to heterogeneity of resources and fluctuating resource demands of multifarious applications. This article presents an autonomic resource allocation approach for energy efficient utilization of resources while satisfying QoS needs of the end‐users. The proposed approach is based on antlion optimization and makes use of resource utilization history for autonomic allocation and efficient use of resources. It can operate in one of the three different modes namely, energy‐saving, performance, and balanced mode depending on the projected workload of an application. The performance analysis of the proposed approach is carried out in CloudSim simulator under different workload conditions.

A comparison of first-come-first-served and multidimensional heuristic approaches for asset allocation of floor cleaning machines

This paper presents a comparison of three binary heuristic algorithms - Particle Swarm Optimization (BPSO), Differential Evolution (BDE), and a Genetic Algorithm (GA) - under two asset allocation approaches to obtain optimal deployment combinations of assets for a cleaning company. When a company requires planning for the best asset allocation, the common practice is to rely on the experience of managers to estimate what combination of assets is appropriate to deploy to fulfill the different cleaning tasks that are carried out simultaneously in different locations. This work considers a given asset inventory of 168 machines composed of 29 different types of cleaners obtained online from three vendors. The comparison is made for three scenarios with 5, 10, and 15 tasks. Each task has a desired cleaning area and maximum cleaning time that are taken as constraints while minimizing the number of selected machines to be allocated for each task. The allocation approaches are inspired by the First-Come-First-Served (FCFS) approach and the Multidimensional Knapsack Problem (MKP). In total, 30 trials were computed for each scenario where the best, worst, average, and standard deviation were obtained. The results indicate that the combination of BDE and the FCFS approach provides the best solutions by having the lowest number of assets selected for each scenario. Additionally, it has the lowest standard deviation in cleaning area and time, while having a low execution time compared to the other approaches and heuristic algorithms.

An Empirical Study on Workers' Preferences in Human–Robot Task Assignment in Industrial Assembly Systems

Collaborative industrial robotic arms (cobots) are integrated industrial assembly systems relieving their human coworkers from monotonous tasks and achieving productivity gains. The question of task allocation arises in the organization of these human–robot interactions. State of the art shows static, compensatory task allocation approaches in current assembly systems and flexible, adaptive task sharing (ATS) approaches in human factors research. The latter should exploit the economic and ergonomic advantages of cobot usage. Previous research results did not provide a clear insight into whether industrial workers prefer static or adaptive task allocation and which tasks workers do prefer to assign to cobots. Therefore, we set up a cobot demonstrator with a realistic industrial assembly use case and did a user study with experienced workers from the shop floor (n = 25). The aim of the user study is to provide a systematic understanding and evaluation of workers' preferences in a practical context of human–robot interaction (HRI) in assembly. Our main findings are that participants preferred the ATS concept to a predetermined task allocation and reported increased satisfaction with the allocation. Results show that participants are more likely to give manual tasks to the cobot in contrast to cognitive tasks. It shows that workers do not entrust all tasks to robots, but like to take over cognitive tasks by themselves. This work contributes to the design of human-centered HRI in industrial assembly systems.

Allocation of power in NOMA based 6G-enabled internet of things using multi-objective based genetic algorithm

Abstract Sixth generation (6G)-enabled internet of things (IoT) requires significant spectrum resources to deliver spectrum availability for massive IoT’s nodes. But the existing orthogonal multiple access limits the full utilization of limited spectrum resources. The non-orthogonal multiple access (NOMA) exploits the potential of power domain to improve the connectivity for 6G-enabled IoT. An efficient quality of service (QoS) aware power allocation approach is required to enhance the spectral efficiency and energy of NOMA based 6G-enabled IoT nodes. The multi-objective genetic algorithm (MOGA) is used to resolve the non-convex problem by considering the successive interference cancellation (SIC), QoS, and transmission power. Extensive experiments are drawn by using the Monte Carlo simulation to evaluate the significant improvement of the proposed model. Experimental results indicate that the proposed power allocation model provides good performance of the NOMA based IoT network.

A Service-Oriented Energy Efficient Resource Allocation Approach for Wireless Communications of the Tunnel Construction

Tunnel construction is a dynamic excavation process that requires a variety of construction machinery and workers to cooperate according to standard procedures. However, due to insufficient information sharing and inefficient construction situation awareness, the operation efficiency cannot be largely enhanced, and the safety of workers cannot be guaranteed. To deal with the problem, it is imperative to establish a wireless communication system for construction machinery and workers. In this paper, we propose a wireless communication architecture for tunnel construction to carry multiple services including video, voice and sensor data with different quality-of-service (QoS) provision. A resource block (RB) allocation and power control scheme is developed for uplink transmission, where the long-term energy consumption is minimized and the transmission performance is optimized. As a discrete time-constrained stochastic optimization problem, it can be modeled as a constrained Markov decision process (CMDP). Furthermore, the dueling deep Q-network (Dueling-DQN) is utilized to obtain the scheduling policy based on the wireless fading characteristics of tunnels and network queues state. Simulation results demonstrate that the proposed scheme achieves better performance in a variety number of user equipment (UE) scenarios. The scheme gives priority to VoIP and sensor services in the limited bandwidth, which can provide efficient information sharing for tunnel construction.

Resource Allocation in 5G Platoon Communication: Modeling, Analysis and Optimization

New features brought by 5 G New Radio V2X (NR-V2X) support multiple vehicular communication types (unicast, groupcast, and broadcast) to coexist in road scenarios. However, the current standard does not specify the resource scheduling approach for groupcast to support such a new feature, which may severely degrade its packet delivery performance impacted by other communication types. In this paper, we investigate the scheduling and resource allocation approaches for a groupcast-based application, vehicle platooning, under the environment characterizing this new feature. We first analyze two baseline resource allocation approaches, i.e., Semi-persistent Scheduling (SPS) stated in the 3 rd generation partnership project (3GPP) and Random Selection (RS), based on the metric of packet collision probability. Subsequently, considering the issues from baseline approaches, we develop an Improved Random Selection (IRS) scheme to decrease the collision probability. We further propose to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results show that IRS significantly mitigates the packet collision probability compared with the baselines. Meanwhile, DDPG outperforms IRS in terms of the packet collision probability as well as average scheduled delay and is more robust to the change in the environment.

Water-constrained green development framework based on economically-allocable water resources

Water as a main driver for sustainable development (SD) should be optimally allocated to different users to support economic, social, and environmental functions. Traditional approaches are not able to account for all the mentioned functions simultaneously, therefore a change in the allocation approaches is necessary. This paper proposes a new framework for inter-sectoral water allocation called “water-constrained green development” (WCGD) to better meet the SD goals. The framework optimally allocates economically-allocable water (EAW), which is the total available water resources left after subtracting the amount of water required for drinking, sanitation, and environment (DSE), to different job classes. It was tested in Sistan Region- a low-developed area in southeast of Iran- which stands on agriculture. In the recent years, because of water crisis, intensity of dust problem, lack of sustained occupation, and immigration, the region’s rate of population growth has been negative. Also, due to decrease of Helmand River inflow, Hamoun wetland, being the major source of food and shelter for the Sistan’s residents, has been degraded. Therefore, Sistan Region needs to take a new development route. The shares of occupation and gross domestic product (GDP) in the agricultural sector of Sistan are respectively 29.1 and 14.8%, whereas they are on average 1 and 7% in Iran. Application of the proposed framework in Sistan Region under three scenarios of available EAW resources showed that the optimal reallocation of water among 15 job classes can improve job availability and GDP of the region currently suffering from poor economy and employment conditions. Based on the optimal job pattern obtained, the share of GDP of Sistan’s agricultural sector drops to 7.1% while the shares of industrial and service sectors increase respectively from 9.7 and 75.4% to 13.7 and 79.2%, which are close to those of the country averages. Also, under the WCGD-based optimal solution, 68, 14, and 18% of people will respectively be employed in service, industry, and agriculture sectors. Additionally, the total available jobs and GDP will increase by 8.9 and 51.1%, respectively, leading to improved socio-economic well-being of the region’s people and protection of its environmental resources.

A Novel Hybrid Optimization Approach for Optimal Allocation of Distributed Generation and Distribution Static Compensator with Network Reconfiguration in Consideration of Electric Vehicle Charging Station

Electric vehicle charging station (EVCS) infrastructure development is a crucial part of the sustainable growth of the electric vehicle (EV) industry. However, the installation of EVCS may negatively affect the performance of the distribution system. This article presents a novel hybrid optimization approach to solve the optimal allocation problem of EVCS with network reconfiguration in the presence of distributed generation (DG) and distribution static compensator (DSTATCOM), with the objectives of reducing the voltage deviation, real power loss, and investment costs of DGs and DSTATCOMs while improving the voltage stability of the distribution network. A hybrid of African vulture optimization and pattern search (HAVOPS) algorithm is proposed to solve this problem. The impacts of EVCSs are examined for the 33-bus and 136-bus systems with five different cases of DG and DSTATCOM placement with reconfiguration of the network. The efficacy and performance of the HAVOPS algorithm are validated by comparison to the results of the African vulture optimization algorithm, marine predators’ algorithm, grey wolf optimizer, artificial gorilla troops optimizer, and genetic algorithm. According to the findings, the HAVOPS algorithm is superior to the other methods in terms of the merit of the solutions it produces.

Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning

Abstract: Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resources, such as frequency bands and power levels, to different devices and services to maximize some performance metric, such asthroughput or energy efficiency. The main challenge in applying DRL to radio resource allocation is designing an appropriate reward function that incentivizes the agent to improve the performance metric while avoiding undesirable behavior. Additionally, the radio resource allocation problem is complex, requiring the agent to consider many variables and constraints, such as channel conditions, interference, and QoS requirements. To address this, researchers have proposed various techniques such as hierarchical RL, multi-agent RL, and curriculum learning. Despite the challenges, DRL has shown promising results inradio resource allocation for 5G networks. It has outperformed traditional methods in some scenarios, especially when network conditions are dynamic and unpredictable. However, further research is necessary to explore the scalability and robustness of DRL-based approaches in practical 5G networks. In this method we suggest an algorithm for voice and data carriers in sub-6 GHz and millimeter wave (mmWave) frequencies respectively. The mmWave ranges between 30GHz to 300GHz

Enhancing diabetes care for the most vulnerable in the 21st century: Interim findings of the National Advisory Panel on Care Home Diabetes (NAPCHD).

Older adults with diabetes may carry a substantial health burden in Western ageing societies, occupy more than one in four beds in care homes, and are a highly vulnerable group who often require complex nursing and medical care. The global pandemic (COVID-19) had its epicentre in care homes and revealed many shortfalls in diabetes care resulting in hospital admissions and considerable mortality and comorbid illness. The purpose of this work was to develop a national Strategic Document of Diabetes Care for Care Homes which would bring about worthwhile, sustainable and effective quality diabetes care improvements, and address the shortfalls in care provided. A large diverse and multidisciplinary group of stakeholders (NAPCHD) defined 11 areas of interest where recommendations were needed and using a subgroup allocation approach were set tasks to produce a set of primary recommendations. Each subgroup was given 5 starter questions to begin their work and a format to provide responses. During the initial phase, 16 key findings were identified. Overall, after a period of 18 months, 49 primary recommendations were made, and 7 major conclusions were drawn from these. A model of community and integrated diabetes care for care home residents with diabetes was proposed, and a series of 5 'quick-wins' were created to begin implementation of some of the recommendations that would not require significant funding. The work of the NAPCHD is ongoing but we hope that this current resource will help leaders to make these required changes happen.

Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies’ social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers’ work and family, and (e) users’ action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week’, ‘safety first’, and ‘construction team’, (b) the largest clusters were ‘safety training’, ‘occupational health and safety administration’, and ‘health and safety environment’, (c) the most active users were ‘Parallel Consultancy Ltd.’, ‘Pike Consulting Group’, and ‘Global Training Canada’, and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.

An Reliability Allocation and Optimization Genetic Algorithm Approach to (ARPA) Network

As communication infrastructures become more complicated, the telecommunications industry is expanding, which is why there is interest in creating an integrated broadband service for digital networking technologies like the Advanced Research Projects Agency Network (ARPANET). The topological design of the (ARPANET) has become a significant study topic because to the evolving traffic patterns and new technology employed in ARPANETs. The majority of researchers have addressed the issue of topological design and have put forth solutions that depend on expensive interchange-based equipment. We suggested a practical ARPA network model in this study. Network optimization is a component of ARPA Network design. To improve the ARPA network, we put forth a GA-based approach, and we also used the exponential cost function to determine how much of each component of the system should be allocated. According to study findings, Enhanced GA generates significantly better answers than Simple GA.

A stochastic geometric programming approach for power allocation in wireless networks

A stochastic geometric programming approach for power allocation in wireless networks

Aggravated air pollution and health burden due to traffic congestion in urban China

Abstract. Vehicle emissions are regarded as a primary contributor to air pollution and related adverse health impacts. Heavy traffic congestion increases traffic flow and thus produces more O3 precursor emissions, leading to more adverse air quality issues. Although the development of a vehicle emission inventory has received great concern and continuous efforts, limitations still exist. For example, real-time diurnal variations and increases in emission rates due to traffic congestion are not well understood. In this study, we developed a new temporal allocation approach in transportation emissions to investigate the impact on air quality and health burden due to traffic congestion in China in 2020. Both real-time congestion-level data and emission correction factors were considered in the approach. Results show that traffic congestion aggravates air pollution and health burden across China, especially in the urban clusters such as the North China Plain and Sichuan Basin. In these regions, the average annual increases in fine particulate matter (PM2.5) and ozone (O3) could be up to 3.5 µg m−3 and 1.1 ppb, respectively. The excess PM2.5 and O3 attributed to the traffic congestion also induce an additional 20 000 and 5000 premature deaths in China, respectively. In major cities, the increased rate of premature mortality caused by traffic congestion may reach 17.5 %. Therefore, more effective and comprehensive vehicle emission control policies or better planning of the road network should be established to reduce traffic congestion and improve air quality in China.

Phasor measurement unit application-based fault allocation and fault classification

<span>This paper makes a contribution to the field of fault location finding in a new way that helps in the improvement of grid reliability. This paper proposes a study-based approach for fault allocation and fault type classification that uses the study of voltage and current frequency during the abnormal condition. Although, ideally frequency of voltage and current are the same in the abnormal condition they may differ from each other. This difference in frequency is separately measured by the phasor measurement unit (PMU) block at MATLAB/Simulink platform. The PMU (PLL-based, positive-sequence) block is inspired by the IEEE Std C37.118.1-2011. In this approach, we measure the line voltage and current frequency variation with the help of installed PMU after this we present this measurement in characteristics form with the help of the scoping tool in MATLAB/Simulink and study them one by one, and proposed a conclusion for fault location identification and fault type classification. The proposed approach is able to identify the source side and load side fault location and also able to classify faults into two categories namely symmetrical and asymmetrical. The proposed approach is tested on two MATLAB/Simulink models and observed satisfactory.</span>