Task Execution Speed Research Articles

Implementation of Multi Extension in Blockchain-Based IoT Platform for Industrial IoT Devices

The rise of the Internet of Things (IoT) has led to the creation of technologies to improve human life. IoT involves integrating the Internet with the physical world, spanning applications like smart homes, industries, supply chains, academia, and more. By the end of 2020, around 212 billion IoT devices were globally deployed, presenting substantial opportunities for manufacturers and diverse applications. There have been numerous implementations of IoT across various fields, including Blockchain IoT (B-IoT), Artificial Intelligence of Things (AIoT), Digital Twin, and new communication protocols like the Matter protocol. We conducted a comprehensive testing of the blockchain (B-IoT) extension system on various bandwidths and scenarios, such as blockchain API execution time, speed, retention performance, and smart contract vulnerability testing. Our testing has been successful, and several messaging systems were used. Kafka was recommended to overcome the pending transaction problem caused by unprocessed messages. Our smart contract exhibited high severity. The Artificial Intelligence of Things extension, tested on real environments for person and vehicle counters, has shown successful results. Digital Twin, integrated into the IoT platform to perform and control 3D assets such as the postgraduate PENS building, has demonstrated efficient performance. Matter protocol achieved an average task execution speed of 0.48 tasks per second. Matter P2P communication was also successfully tested in this research by implementing the Access Control List (ACL) command.

ВОЗМОЖНОСТИ БОЛЬШИХ ЯЗЫКОВЫХ МОДЕЛЕЙ В КРЕАТИВНЫХ ЗАДАЧАХ БИЗНЕСА (НА ПРИМЕРЕ СОЗДАНИЯ КАТАЛОГА И КАРТОЧЕК ТОВАРОВ ДЛЯ ЭЛЕКТРОННОЙ КОММЕРЦИИ)

This article investigates the potential of large language models in creative business tasks, focusing on the creation of catalogues and product cards for e-commerce. It highlights the necessity for business development and the relevance of entering the e-commerce market, which requires clear planning and optimization, amidst challenges and contradictions. One such contradiction is ensuring the clickability of product cards and attracting the attention of the target audience based on financial, psychological, and other incentives. It emphasizes that creative business tasks are linked to innovative activities and prospects for attracting customer attention through unique offerings, promotion methods, popularization, etc. The article summarizes the possibilities, typical tasks, and prospects of applying large language models in creative business tasks, with particular attention to optimization potential, improving employee performance, and increasing task execution speed and accuracy. Based on the research findings, conceptual issues related to the primary introduction of products to the market, their continuous improvement, and the role of creating catalogues and product cards in business are systematized. The article justifies the feasibility of implementing large language models in e-commerce businesses.

Enhancing energy efficiency in cloud scaling: A DRL-based approach incorporating cooling power

The rapid growth of cloud computing significantly boosts energy usage, driven mainly by CPU operations and cooling. While cloud scaling efficiently allocates resources for changing workloads, current energy-driven methods often prioritize energy metrics combined with throughput, execution time, or SLA compliance, neglecting cooling power’s influence on energy consumption. To bridge this gap, we propose a deep reinforcement learning (DRL)-based autoscaler that considers cooling power as a critical factor for decision-making. Our approach employs DRL to dynamically adjust cloud resources, aiming to maximize energy efficiency and meet performance objectives. DRL, unlike RL, uses neural networks to handle the extensive state–action space in cloud scaling, overcoming the challenge of limited memory capacity for storing Q-values. In this study, we evaluate the performance of our proposed solution through a simulation-based experiment. We compare the performance of the proposed DRL-based autoscalers against an RL-based autoscaler. Our findings indicate that the DDQN-based autoscaler consistently outperforms other algorithms by maintaining optimal Power Usage Effectiveness (PUE) levels and improving task execution speed during high workloads. In contrast, the DQN-based autoscaler excels at sustaining optimal PUE levels during lower task loads, with a faster convergence rate at a scaling factor of 2 compared to scaling factor 1.

3D Feature-Based Sampled-Data Visual Tracking*

3D feature-based Visual Servoing (VS) on the one hand shows attractive peculiarities, on the other hand it suffers from drawbacks related to the existence of local minima, which may affect the convergence character of the VS control loop. Furthermore, the performance of the visual tracking module may constitute a bottleneck enforcing severe constraints on the workspace and visual task execution speed. In this paper we introduce a novel sampled-data model of the 3D feature-based VS, and, in order to avoid drawbacks due to local minima, we plan the target reference trajectory in the feature space with the aim to constraint the feature error dynamics to remain close to the desired equilibrium point. Then, we propose a novel feature generation based on the homography provided by a template matching algorithm based on the Zero mean Normalized Cross Correlation (ZNCC) and the design of a visual tracking scheme by resorting to the Extended Kalman Filter (EKF) and Lyapunov direct method, which explicitly takes into account the camera velocity limits, while ensuring stability.

An Accessible, Open-Source Dexterity Test: Evaluating the Grasping and Dexterous Manipulation Capabilities of Humans and Robots.

Evaluating the dexterity of human and robotic hands through appropriate benchmarks, scores, and metrics is of paramount importance for determining how skillful humans are and for designing and developing new bioinspired or even biomimetic end-effectors (e.g., robotic grippers and hands). Dexterity tests have been used in industrial and medical settings to assess how dexterous the hands of workers and surgeons are as well as in robotic rehabilitation settings to determine the improvement or deterioration of the hand function after a stroke or a surgery. In robotics, having a comprehensive dexterity test can allow us to evaluate and compare grippers and hands irrespectively of their design characteristics. However, there is a lack of well defined metrics, benchmarks, and tests that quantify robot dexterity. Previous work has focused on a number of widely accepted functional tests that are used for the evaluation of manual dexterity and human hand function improvement post injury. Each of these tests focuses on a different set of specific tasks and objects. Deriving from these tests, this work proposes a new modular, affordable, accessible, open-source dexterity test for both humans and robots. This test evaluates the grasping and manipulation capabilities by combining the features and best practices of the aforementioned tests, as well as new task categories specifically designed to evaluate dexterous manipulation capabilities. The dexterity test and the accompanying benchmarks allow us to determine the overall hand function recovery and dexterity of robotic end-effectors with ease. More precisely, a dexterity score that ranges from 0 (simplistic, non-dexterous system) to 1 (human-like system) is calculated using the weighted sum of the accuracy and task execution speed subscores. It should also be noted that the dexterity of a robotic system can be evaluated assessing the efficiency of either the robotic hardware, or the robotic perception system, or both. The test and the benchmarks proposed in the study have been validated using extensive human and robot trials. The human trials have been used to determine the baseline scores for the evaluation system. The results show that the time required to complete the tasks reduces significantly with trials indicating a clear learning curve in mastering the dexterous manipulation capabilities associated with the imposed tasks. Finally, the time required to complete the tasks with restricted tactile feedback is significantly higher indicating its importance.

Project Implementation of Corporate Information Systems (ERP and MES) as a Guarantee for Increasing the Operational Efficiency of the Enterprise

This article delves into the intricacies of implementing corporate information systems, such as Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES). It has been established that the implementation of ERP and MES ensures increased operational efficiency of the enterprise by automating various processes. Additionally, the MES system controls multiple production stages, from raw material input to finished product shipment, thereby reducing errors and increasing task execution speed. ERP and MES also facilitate data and information consistency between different departments, providing all levels of management access to the same company information, streamlining the decision-making process. Furthermore, ERP allows for the collection of data on finance, personnel, logistics, and production in one system, making the planning and decision-making process simpler. The MES system can be used to determine task execution time and distribute it among workers based on their skills and abilities. Lastly, ERP and MES enable companies to prioritize tasks, control their execution, automate processes, improve task execution quality and speed, and ensure data and information consistency between different departments. The article discusses the importance of corporate information systems (CIS) in modern companies with a priority of maintaining a competitive position. The key components of such a system are enterprise resource planning (ERP) and manufacturing execution systems (MES), which ensure the integrity, accuracy, and speed of data exchange between all company units. Implementing ERP and MES is a guarantee of increasing the company's operational efficiency in complex production conditions. CIS help companies ensure data and information unity between different departments, enabling quick and efficient decision-making processes.

Analyzing the features of service robotics development and distribution during the coronavirus pandemic

Subject. The work analyzes the features of service robotics development and distribution during the coronavirus pandemic, using the case of delivery drones and UVC disinfection robots. Objectives. The aim is to identify the main trends in the development of certain types of service robots during the COVID-19 period, analyze technical and economic characteristics, assess assistive technologies for the future development of robotics. Methods. The study draws on methods of correlation and regression analysis. To assess the development trends of certain types of service robots, we apply website data of manufacturers of the equipment under consideration. Results. We unveil major trends in the development and production of certain types of service robots during the fight against coronavirus, using the case of unmanned aerial vehicles and disinfection robots. The development of delivery drones is aimed at solving the problems of flight efficiency, increasing the flight time, reducing the energy use and noise, and ensuring the safety of delivered goods. Medical robotics developers focus on streamlining the management interface, increasing the speed of task execution, enhancing the reporting abilities. Conclusions. Our analysis shows that the coronavirus pandemic affected the speed of development and implementation of robots in medicine. The market is shifting towards the production of UV-disinfection robots. The most important technical and economic characteristics of the main types of disinfection robots are the surface treatment cycle to achieve the maximum disinfecting effect, maximum operating time (without recharging), charging time, wavelength of radiation, weight of device.

Information-centric Edge Computing: A Survey

Edge Computing (EC) and Named Data Networking (NDN) are two emerging technologies that are considered as the most representative technologies for the future Internet. NDN directly forwards an application layer name in the network layer, mitigating the requirements of an individual and complex network addressing schemes for devices. The intermediate nodes and/or routers in NDN architecture cache the content inside their memory according to the caching policy to fulfill similar requests in the future within a short time. EC, on the other hand, offers computation, software services, data, and storage that are close to end-users compared to cloud computing, which results in lower delay and high speed of task execution. In the 5G communication scenarios and beyond, ultra-low latency, efficient mobility management, and security are fundamental requirements. Thus, the synergy of NDN with EC appears as a promising candidate and has attracted extensive attention from academia in the recent past. Therefore, in this survey paper, we discuss various techniques that integrate EC with Information Centric Networking (ICN)/NDN and fulfill the aforementioned requirements. We also shed light on the limitations and open research challenges in the field of NDN and EC for the research community.

SMOSA: Spider monkey optimization‐based scheduling algorithm for heterogeneous Hadoop

SummaryHadoop is a typical framework for processing big data. Task scheduling algorithms have a significant impact on the processing performance of Hadoop clusters. Existing scheduling algorithms of Hadoop fail to consider the performance differences between nodes in heterogeneous Hadoop clusters, causing problems such as uneven task allocation and low resource utilization. Aiming to solve this problem, we propose a spider monkey optimization‐based scheduling algorithm (SMOSA) for heterogeneous Hadoop. First, the cluster heartbeat mechanism is used to obtain information such as memories and CPUs of nodes to comprehensively consider the actual load capacity of each node. Then, the spider monkey optimization algorithm is adopted to find the optimal mapping relationship between tasks and resources by taking the task completion time as the objective function and updating the position of the spider monkey. Finally, we calculate the remaining rate of node hardware resources, and according to the task type, the node with the higher remaining rate of resource will give priority to the task. Data are compressed for I/O type tasks to reduce disk operations and increase the speed of task execution. Experimental results show that, compared with existing scheduling algorithms, the SMOSA can effectively reduce task execution time and can significantly improve scheduling efficiency and task execution speed especially in heterogeneous Hadoop clusters. For different types of tasks, the execution time can be reduced by up to 19%.

A ‘Speed—Difficulty—Accuracy’ Model Following a General Trajectory Motor Task with Spatial Constraints: An Information-Based Model

Accuracy in executing a motor task, i.e., in following a given trajectory under geometrical constraints, is of great interest in work operations as well as in biomechanics applications. In the framework of the Fitts’ law research on motor tasks, experimental studies usually refer to simple trajectories which are of low interest in practical applications. Furthermore, available models lack predicting accuracy in executing motor tasks since do not systematically investigate effects of both speed and task difficulty (index of difficulty (ID)). In this paper, the authors propose a ‘Speed-ID-Accuracy’ model aiming at overcoming abovementioned limits. The model is of general validity as is based on an information-based formulation of a trajectory ID; the model proposed put into relation accuracy in task execution with a general trajectory and with the speed of task execution. Modeling accuracy, defined as standard deviation of the endpoint position, is carried out by regressing data available in the literature. The model proposed proves to be more accurate than the classical ‘Speed-Accuracy’ model in fitting available data. Such a result has been found in both numerical cases relating to ‘tunnel’ and ‘circular’ traveling tasks. Limits of data from field experiments are stressed out and future research field of investigations in work environment and biomechanics are figured out.

Assessing the impact of information and communication technologies on the performance of microfinance institutions in Niger

This paper assesses the impact of information and communication technologies (ICTs) on the performance of microfinance institutions (MFIs) in Niger, West Africa. MFIs play a pivotal role in improving financial inclusion in Niger because the majority of the country’s poor live in rural areas, with only limited and costly access to formal financial services. Using an unbalanced panel of 23 MFIs spanning 2005–2013, single-step generalized moments method (GMM) estimations are run to appraise whether ICT investments improve the financial and the social performance of MFIs. The results show a positive relationship between investments in ICTs and MFIs’ financial performance. Investing more in technologies enables managers to reduce the frequency of operational errors, increase the speed of task execution, decrease operating costs, and increase the likelihood of higher financial profits. The findings also reveal a positive effect of institutional affiliation on the financial performance of MFIs. Namely, MFIs affiliated with a network and investing in ICTs tend to perform better. The impact of ICT investments on the social performance of MFIs is rather weak. From a policy perspective, developing ICT infrastructure can yield substantial performance dividends and should remain a top developmental priority in Niger.

Methods and analytical tools for assessing tactical situation in military operations using potential approach and sensor data fusion

The paper presents designed quantitative methods and algorithms implemented in specialized mobile mCOP application for combat decision support. Military operations require sophisticated planning and execution in order to minimize risks and potential losses while fulfilling mission objectives in constrained time. To support such features the decision makers design methods of evaluating threats and tactical scenarios. The outcomes of such calculations provide mission alternatives called variants or Courses of Actions. To determine which possible CoA is matching the scenario and in the end is the most feasible, a set of quantitative methods can be used. The evaluation process considers both tactical and topographical characteristics of the scenario. The location, orientation and composition of forces, combat potentials, performed tasks, as well as terrain and battlespace environment features are the parameters considered in the algorithms, which in result provide threat levels, attrition rates, potential losses, objectives timelines etc. To assess tactical situation we implemented, a set of combat potential evaluation methods, which using doctrinal patterns, can be easily employed in mobile software. The advantage of equipping each decision maker at the tactical level with sophisticated methods provides means of increasing the speed of task execution, provides more accurate decisions but most of all supports instant reporting from combat units. Due to the development of hardware and software platforms, smartphones offer are capable of running complex algorithms for tactical support for individual soldiers and low level commanders support. Since they are capable to utilize tactical data (forces location, composition, tasks) in dynamic mobile military networks, accessible anywhere during mission, and provide provides rich functional support for situation awareness and decision superiority. These two key factors of 21st century military operations influence the efficiency of recognition, identification and targeting during combat missions. Military support tools providing rich tactical and their analytical capabilities, can serve as recon data hubs, but most of all can support and simplify complex analytical tasks for commanders. The tasks mainly include topographical and tactical orientation within the battlespace. Developed software platform mCOP, demonstrates all research findings, providing personalized combat-oriented distributed mobile system, supporting blue force tracking capabilities, reconnaissance data fusion as well as threat level evaluation for functionalities for management of military and crisis management scenarios. Presented research demonstrates and evaluates the proves the usefulness of deploying mobile applications for combat support, situation awareness development, and the delivery of augmented reality based threat level analytical data to extend the capabilities and properties of constructed methods and of software tools applied for supporting military and border protection operations.

Variations in glenohumeral movement control when implementing an auditory feedback system: A pilot study

Introduction: Human motor control requires a learning process and it can be trained by means of various sensory feedback sources.Objective: To determine variations in glenohumeral movement control by learning in young adults exposed to an auditory feedback system while they perform object translation tasks classified by difficulty level.Materials and methods: The study involved 45 volunteers of both sexes (22 women), aged between 18 and 32 years. Glenohumeral movement control was measured by means of the root mean square (RMS) of the accelerometry signal, while task execution speed (TES) was measured using an accelerometer during the execution of the task according to its difficulty (easy, moderate and hard) in four stages of randomized intervention (control, pre-exposure, exposure-with auditory feedback, and post-exposure).Results: Statistically significant differences (p<0.001) were found between the pre-exposure and exposure stages and between pre-exposure and post-exposure stages. A significant increase (p <0.001) in TES was identified between the pre-exposure and exposure stages for tasks classified as easy and hard, respectively.Conclusion: The use of an auditory feedback system in young adults without pathologies enhanced learning and glenohumeral movement control without reducing TES. This effect was maintained after the feedback, so the use of this type of feedback system in healthy individuals could result in a useful strategy for the training of motor control of the shoulder.

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task

BACKGROUND: Linking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large-scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near-threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states. METHODS: We here investigated whether the prestimulus activation and within-network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match-to-sample) mental rotation task, employing a sparse event-related functional magnetic resonance imaging (fMRI) design. RESULTS: We found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials. CONCLUSION: These results indicate that pre-existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind-wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance.

Optimal task execution speed setting and lower bound for delay and energy minimization

The current technology trend reveals that static power consumption is growing at a faster rate than dynamic power consumption. In this paper, energy-efficient task scheduling is studied when static power consumption is a significant part of energy consumption which cannot be ignored. The problems of scheduling a set of independent sequential tasks on identical processors so that the schedule length is minimized for a given energy consumption constraint or the energy consumption is minimized for a given schedule length constraint are investigated. For a given schedule, the optimal task execution speed setting for delay and energy minimization is found analytically. Lower bounds for the minimum schedule length of a set of tasks with a given energy consumption constraint and the minimum energy consumption of a set of tasks with a given schedule length constraint are established. Our lower bounds are applicable to sequential or parallel, and independent or precedence constrained tasks, on processors with discrete or continuous speed levels, and bounded or unbounded speed ranges. The significance of these lower bounds is that they can be used to evaluate the performance of any heuristic algorithms when compared with optimal algorithms. Experimental study on the performance of list scheduling algorithms is performed and it is shown that their performance is very close to the optimal. To the best of the author’s knowledge, this is the first paper that provides such analytical results for energy-efficient task scheduling with both dynamic and static power consumptions.

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task.

BackgroundLinking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large‐scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near‐threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states.MethodsWe here investigated whether the prestimulus activation and within‐network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match‐to‐sample) mental rotation task, employing a sparse event‐related functional magnetic resonance imaging (fMRI) design.ResultsWe found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials.ConclusionThese results indicate that pre‐existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind‐wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance.

Can sleep quality and burnout affect the job performance of shift-work nurses? A hospital cross-sectional study.

The aim of this study was to investigate any possible relationship between sleep disorders, burnout and job performance in a shift-work population of nurses. Sleep disorders and burnout can affect the job performance of nurses in terms of efficiency, productivity, task execution speed and supervision, which can be compromised when work shifts are organized on a 24-hour schedule and when the shift itself is irregular. A cross-sectional observational study was conducted from August 2014 - January 2015 on a sample of 315 shift-work nurses across 39 wards in seven central Italian hospitals. The Pittsburgh Sleep Quality Index was used to detect the presence of sleep disorders, the Copenhagen Burnout Inventory was used to detect the presence of any possible type of burnout and the Job Performance Scale was used to measure job performance. Data analysis was mainly based on a multivariate logistic regression to identify variables significantly associated with investigated outcomes. On shift-work nurses' sleep quality and burnout correlated positively. The female gender and personal burnout were significantly associated with impaired sleep quality, while working in the psychiatric setting, working a long cycle shift pattern and experiencing daytime dysfunction were significantly associated with burnout. A significant negative association between patient-related burnout and job performance was observed. Specific characteristics of shift-work nurses can directly affect sleep quality and burnout and indirectly job performance. This evidence offers healthcare administrators opportunities to intervene with measures to promote nurse's health, well-being and safety.

Improving Precision in Navigating Laparoscopic Surgery Instruments toward a Planar Target Using Haptic and Visual Feedback

The poor ergonomics of laparoscopic surgery are a widely recognized source of difficulty for surgeons, leading to sub-optimal surgeon performance and sometimes injury to the patient. The main causes for this are lost and distorted perception of interaction forces and instrument position. The latter, due to losses in visual and kinaesthetic depth perception and modified hand-eye coordination, can prevent precise navigation of instruments towards surgical targets or away from sensitive anatomic structures. This situation prompts us to explore methods for efficiently assisting the surgeon during instrument navigation. Here, we present experiments aimed at providing insights into the effectiveness of haptic (tactile and kinaesthetic), visual and combined feedback in assisting the navigation of a laparoscopic instrument tip towards a surgical target. Subjects placed in front of a laparoscopic trainer were tasked with following various instrument tip trajectories within a target plane while minimizing both deviations and task execution times. Feedback on the level of deviation was provided alternately through visual on-screen cues, tactile cues provided by vibration motors and / or kinaesthetic cues provided by a haptic interface co-manipulating the surgical instrument. Evaluations of these forms of feedback over two series of experiments implicating a total of 35 subjects (34 non-surgeon novices, 1 surgeon intern with experience in laparoscopy) show positive impacts of providing such feedback on precision in instrument navigation. Visual, tactile and combined cues lead to increased precision in navigation (up to 25\% increase in time spent on target, and 32\% reduction in deviation amplitudes), but usually at the cost of reduced task execution speed. However, the use of kinaesthetic feedback through soft virtual fixtures provided in a co-manipulated robot-assisted surgery set-up both significantly improved precision (32\% increase in time spent on target, and 70\% reduction in deviation amplitudes) and task execution speed (30\% reduction in task completion times). The combination of visual and tactile feedback was shown to be helpful in correcting larger deviations from the target. These preliminary results are promising for implementation of low-cost tactile or combined visual and tactile feedback in applications to conventional laparoscopic instrument navigation, as well as to robot-assisted laparoscopic surgery.

Research of Hadoop Task Speculative Execution Based on Process Migration

Current Hadoop platform task speculative execution can’t guarantee the backup task execution speed. This paper proposes a Hadoop speculative execution mechanism based on process migration. The backup task initializes the Task state with ExecutionState object which is formed according to the original task status. Thus, the backup task can continue the task instead of restarting a new task, this makes sure the backup task is more efficient than the original task. The results of running jobs on the hadoop platform prove that task speculative execution based on progress migration improves the efficiency greatly.

Gossip algorithms for heterogeneous multi-vehicle routing problems

In this paper we address a class of heterogeneous multi-vehicle task assignment and routing problems. We propose two distributed algorithms based on gossip communication: the first algorithm is based on a local exact optimization and the second is based on a local approximate greedy heuristic. We consider the case where a set of heterogeneous tasks arbitrarily distributed in a plane has to be serviced by a set of mobile robots, each with a given movement speed and task execution speed. Our goal is to minimize the maximum execution time of robots.