Task Allocation Research Articles

A Division-of-Labour Approach to Traffic Light Scheduling

Traffic light scheduling is a critical aspect of traffic management with many recently developed solutions that incorporate computational intelligence approaches. This paper presents a traffic light scheduling algorithm based on a task allocation model that simulates the division of labour among insects in a colony, specifically ant colonies. The developed algorithm switches the green light based on a probability calculated every second from the traffic volume around the traffic light. The application of this algorithm to several benchmark simulated traffic scenarios shows optimal performance compared to five other traffic scheduling algorithms.

A truthful randomized mechanism for task allocation with multi-attributes in mobile edge computing

Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.

Centralized multi-robot logistic system: An approach using the island model genetic algorithm as task scheduler

The practicality allied with technological and logistical advances led customers increasingly to join e-commerce. This phenomenon was even more expanded during the COVID-19 pandemic. Because of the growing demand for logistic services, warehouses must adapt and seek faster and more efficient processes. A means to achieve that is to use mobile robots in transportation-related tasks. However, their effectiveness is only achieved if the system has an efficient work distributor. This research developed a centralized coordination architecture using the island model genetic algorithm for multi-robot logistic task allocation. The coordination receives, allocates, and monitors tasks performed by robots with different payload capacities and average speeds. The architecture was developed upon the robotic operating system. Thus, any robot with a robotic operating system-based internal system can work under the coordination of this architecture. On the results, the task scheduler, developed in previous work, had the evaluation complemented. The scheduler based on the island model genetic algorithm allocates more tasks than the standard genetic algorithm when using the same heuristic. Regarding coordination architecture, the system successfully managed a group of robots in a simulated environment. It also detected and notified failures during task execution. Therefore, this system provides a complete task scheduling, allocation, and monitoring solution.

Task Allocation in Spatial Crowdsourcing: An Efficient Geographic Partition Framework

Task Allocation in Spatial Crowdsourcing: An Efficient Geographic Partition Framework

Intelligent mission planning for autonomous distributed satellite systems

System autonomy plays a critical role in the success of next generation Distributed Satellite Systems (DSS) mission architectures, enabling the inference of external and internal environment conditions to support system adaptation in unexpected and unfamiliar situations. Artificial Intelligence (AI) based techniques show promise in achieving this desired self-adaptive capability by offering on-board predictive real time analytics, supporting an evolution towards Trusted Autonomous Satellite Operations (TASO). In parallel, TASO requires an evolution of the control and coordination of increasingly autonomous large scale DSS, achieved in the design and operation of intelligent Mission Planning Systems (MPS). In this paper, we focus on intelligent mission planning functionality problem utilising a distributed ant colony optimisation approach to achieve local task allocation and platform coordination. The approach is implemented in the context of a Space-Based Space Surveillance (SBSS) mission architecture that considers self-adaptive capabilities, global coordination and supervisory control aspects. The effectiveness of the approach is shown, demonstrating that the proposed solution based on metaheuristics supports an efficient and pervasive SBSS capability.

A Heuristic Task Allocation Method Based on Overlapping Coalition Formation Game for Heterogeneous UAVs

A Heuristic Task Allocation Method Based on Overlapping Coalition Formation Game for Heterogeneous UAVs

From bias to balance: achieving organisational justice through algorithmic task allocation

From bias to balance: achieving organisational justice through algorithmic task allocation

Parallel implementation for real-time visual SLAM systems based on heterogeneous computing

Simultaneous localization and mapping has become rapidly developed and plays an indispensable role in intelligent vehicles. However, many state-of-the-art visual simultaneous localization and mapping (VSLAM) frameworks are very time-consuming both in front-end and back-end, especially for large-scale scenes. Nowadays, the increasingly popular use of graphics processors for general-purpose computing, and the progressively mature high-performance programming theory based on compute unified device architecture (CUDA) have given the possibility for large-scale VSLAM to solve the conflict between limited computing power and excessive computing tasks. The paper proposes a full-flow optimal parallelization scheme based on heterogeneous computing to speed up the time-consuming modules in VSLAM. Firstly, a parallel strategy for feature extraction and matching is designed to reduce the time consumption arising from multiple data transfers between devices. Secondly, a bundle adjustment method based solely on CUDA is developed. By fully optimizing memory scheduling and task allocation, a large increase in speed is achieved while maintaining accuracy. Besides, CUDA heterogeneous acceleration is fully utilized for tasks such as error computation and linear system construction in the VSLAM back-end to enhance the operation speed. Our proposed method is tested on numerous public datasets on both computer and embedded sides, respectively. A number of qualitative and quantitative experiments are performed to verify its superiority in terms of speed compared to other states-of-the-art.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

Achieving productivity and operator well-being: a dynamic task allocation strategy for collaborative assembly systems in Industry 5.0

Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator’s current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator’s stress.

Optimizing task allocation with temporal‐spatial privacy protection in mobile crowdsensing

AbstractMobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

Cooperative mission planning based on game theory for UAVs and USVs heterogeneous system in dynamic scenario

PurposeThe purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.Design/methodology/approachIn this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.FindingsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Research limitations/implicationsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Practical implicationsThe proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.Originality/valueThe decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm

As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.

A Review of Multi-UAV Task Allocation Algorithms for a Search and Rescue Scenario

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations by accessing inaccessible areas, accomplishing challenging tasks, and providing real-time monitoring and modeling in situations where human presence is unsafe. Multi-UAVs can collaborate more efficiently and cost-effectively than a single large UAV for performing SAR operations. In multi-UAV systems, task allocation (TA) is a critical and complex process involving cooperative decision making and control to minimize the time and energy consumption of UAVs for task completion. This paper offers an exhaustive review of both static and dynamic TA algorithms, confidently assessing their strengths, weaknesses, and limitations. It provides valuable insights into addressing research questions related to specific UAV operations in SAR. The paper rigorously discusses outstanding issues and challenges and confidently presents potential directions for the future development of task assignment algorithms. Finally, it confidently highlights the challenges of multi-UAV dynamic TA methods for SAR. This work is crucial for gaining a comprehensive understanding of multi-UAV dynamic TA algorithms and confidently emphasizes critical open issues and research gaps for future SAR research and development, ensuring that readers feel informed and knowledgeable.

Implementing care-related services in care units - an interview study

BackgroundThe growing concern about a dwindling healthcare workforce, exacerbated by demographic changes, calls for innovative solutions. One viable approach involves implementing new professional roles and restructuring existing healthcare teams within hospital care units.ObjectivesTo evaluate the implementation of an innovative task-shifting concept, care-related services (CRS), from the managers’ perspective in somatic care units across the hospitals in a region in Sweden.MethodsThe qualitative study was conducted in 2022, after the implementation of CRS. Individual interviews were conducted with 24 key stakeholders, including 14 care unit managers, six CRS managers, and four process managers. A qualitative content analysis was performed, utilizing the Consolidated Framework of Implementation Research (CFIR).ResultsThe implementation of CRS involved collaboration between care unit managers, CRS managers, and project managers, alongside CRS staff, registered nurses (RNs), and licensed vocational nurses (LVNs). In particular, their roles encompassed defining boundaries, establishing routines, and managing personnel. Throughout the implementation process, challenges emerged, stemming from undefined goals, difficulties in recruiting qualified CRS staff, and issues associated with seamlessly integrating CRS into existing work routines. These challenges arose due to a constrained timeframe, widespread team apprehension, shortcomings in the training of CRS staff, unclear task allocation, and an increased workload for care unit managers. Factors associated with successful CRS implementation included effective cooperation among managers and an open-minded approach.ConclusionsOur findings highlight the crucial role of clear communication, effective recruitment, integration of CRS staff, clarification of roles, responsibilities, and defined goals for successful CRS implementation.

Pickup and delivery problem with electric vehicles and time windows considering queues

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm is further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

A lexicographic optimization-based approach for efficient task allocation in industrial transportation multi-robot systems

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects.

Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challenges

Mobile Crowd Sensing/Souring (MCS) is a novel sensing approach that leverages the collective participation of users and their mobile devices to collect sensing data. As large volumes of data get stored and processed by the MCS platform, Artificial Intelligence (AI) techniques are being deployed to make informed decisions that help optimize the system performance. Despite their effectiveness in solving many of the challenges, incorporating AI models in the system introduces many concerns, which could adversely affect its performance. This includes exploiting the vulnerabilities of the models by an adversary to manipulate the data and cause harm to the system. Adversarial Machine Learning (AML) is a field of research that studies attacks and defences against machine learning models. In this study, we conduct a systematic literature review to comprehensively analyze state-of-the-art works that address various aspects of AI-based MCS systems. The review focuses mainly on the applications of AI in different components of MCS, including task allocation and data aggregation, to improve its performance and enhance its security. This work also proposes a novel classification framework that can be adapted to compare works in this domain. This framework can help study AML in the context of MCS, as it facilitates identifying the attack surfaces that adversaries can exploit, and hence highlights the potential vulnerabilities of AI-based MCS systems to adversarial attacks, motivating future research to focus on designing resilient systems.

A spatiotemporal optimization method for connected and autonomous vehicle operations in long tunnel constructions

With the advancement of technology, connected and autonomous vehicles (CAVs) can be applied to complex tunnel networks in long tunnel construction to enhance vehicle operation safety and efficiency. This paper proposes an optimization method for CAVs' operation in long tunnel constructions. Firstly, a spatiotemporal coordinated optimization model with decentralized time and hierarchical networks is proposed to minimize the total working time for completing transportation services. The model integrates macro task allocation and micro node control and optimizes the vehicle-space-time relationships of CAVs to prevent conflicts and collisions. Secondly, a heuristic algorithm named Search-Adjustment Genetic Algorithm (SAGA) is developed to solve the problem considering the model's complexity and engineering characteristics. Thirdly, numerical experiments are designed to validate the feasibility and efficiency of the proposed model and algorithm. The results indicate that (1) the proposed model can effectively deconflict CAVs in the road network to ensure safety and obtain a low total working time to fulfill the transportation demand. (2) Compared to the commercial solver Gurobi, the proposed algorithm demonstrates significantly superior solution accuracy and efficiency within an acceptable time limit. (3) The solution ensures the safety and efficiency of CAVs and increases their utilization compared with engineering-oriented methods, resulting in a 50 % reduction in CAV acquisition costs, a 29 % and 85 % reduction in running time and delay respectively, and a reduction in fuel consumption. (4) As the number of transportation services and the complexity of the road network increases, the efficiency gains become more prominent and better adapted to the needs of the actual long tunnel construction project. To sum up, the proposed model and algorithm can ensure the safety and efficiency of providing transportation services in future long tunnel construction. Moreover, it can be adapted for controlling CAVs in road networks such as other construction scenarios and urban road networks.

Reinforcement-Learning-Assisted Multi-UAV Task Allocation and Path Planning for IIoT

Reinforcement-Learning-Assisted Multi-UAV Task Allocation and Path Planning for IIoT