Optimal Task Allocation Research Articles

Bacterial foraging optimization building block distribution algorithm based dynamic allocation in multiple robotic system

Robots are increasingly utilized in healthcare applications through Ambient Assisted Living techniques to enhance lifestyles and improve task efficiency. However, optimizing task allocation in multiple-robot systems remains a significant challenge due to dynamic requirements, robot heterogeneity, and computational constraints. This paper introduces the Bacterial Foraging Optimization Building Block Distribution (BFOBBD) algorithm, a novel approach for dynamically allocating tasks to robots based on utility, interdependence, and computational efficiency. The proposed algorithm addresses the limitations of existing methods by integrating probabilistic modeling with multivariate factorization to optimize task allocation. Experimental results demonstrate that the BFOBBD algorithm reduces task allocation time to 4.23 s, significantly outperforming methods such as FA-POWERSET-MART (20.19 s) and FA-QABC-MART (5.26 s). This work contributes a robust, scalable solution for task allocation in healthcare environments, enabling robots to handle multiple tasks efficiently with minimal computation.

EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments

The widespread adoption of cloud services has posed several challenges, primarily revolving around energy and resource efficiency. Integrating cloud and fog resources can help address these challenges by improving fog-cloud computing environments. Nevertheless, the search for optimal task allocation and energy management in such environments continues. Existing studies have introduced notable solutions; however, it is still a challenging issue to efficiently utilize these heterogeneous cloud resources and achieve energy-efficient task scheduling in fog-cloud of things environment. To tackle these challenges, we propose a novel ML-based EcoTaskSched model, which leverages deep learning for energy-efficient task scheduling in fog-cloud networks. The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences. A real fog-cloud environment is implemented using the COSCO framework for the simulation setup together with four physical nodes from the Azure B2s plan to test the proposed model. The DeFog benchmark is used to develop task workloads, and data collection was conducted for both normal and intense workload scenarios. Before preprocessing the data was normalized, treated with feature engineering and augmentation, and then split into training and test sets. To evaluate performance, the proposed EcoTaskSched model demonstrated superiority by significantly reducing energy consumption and improving job completion rates compared to baseline models. Additionally, the EcoTaskSched model maintained a high job completion rate of 85%, outperforming GGCN and BiGGCN. It also achieved a lower average response time, and SLA violation rates, as well as increased throughput, and reduced execution cost compared to other baseline models. In its optimal configuration, the EcoTaskSched model is successfully applied to fog-cloud computing environments, increasing task handling efficiency and reducing energy consumption while maintaining the required QoS parameters. Our future studies will focus on long-term testing of the EcoTaskSched model in real-world IoT environments. We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.

Centralized and Distributed Strategies for Handover-Aware Task Allocation in Satellite Constellations

As satellite constellations grow in size, there is an increasing need for autonomous, scalable, and real-time dynamic task assignment to address the unique operational challenges of such distributed systems. In particular, a time-varying task assignment (i.e., for observing various regions of Earth) often means that the corresponding satellite has to reorient itself or its sensors, costing time and energy. However, most assignment algorithms for area requests proposed in the literature do not account for the significant cost associated with task handover in satellite constellations. In this paper, we develop a framework for solving the seemingly non-deterministic polynomial-time (NP)-hard problem of optimal dynamic task allocation while minimizing task handover. In particular, we develop Handover-Aware Assignment with Lookahead (HAAL), an algorithm with centralized and distributed variants, and solutions that provably achieve 50% of the optimal value. We then proceed to show that HAAL significantly outperforms similar heuristic methods proposed in the literature for realistic constellation experiments with up to a thousand satellites. The algorithm scales polynomially in the number of satellites/tasks and offers a smooth tradeoff between computational efficiency and performance, allowing the designer to tune the algorithm based on available computing resources, communication bandwidth, and required performance.

A Multi-Satellite Multi-Target Observation Task Planning and Replanning Method Based on DQN

This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constellations by eliminating the need for network model retraining during satellite malfunctions. It enables real-time generation of optimal task allocation schemes in contingency scenarios, ensuring efficient and adaptive task planning. Firstly, a mission scenario model is established by formulating task constraints and defining optimization objectives; secondly, a deep reinforcement learning framework is constructed to output the observation target sequence; then, the observation target sequence is transformed into a target sequence matrix, and a matrix-sorting planning method is proposed to carry out the optimal assignment of the task; lastly, a replanning method is designed for sudden satellite failure and insertion of urgent tasks. The experimental results show that the method has fast task planning speed, high task completion, and immediate task replanning capability.

Research on the relationship between anomalous cases in parallel scheduling problems and executor performance

The article examines the problem of optimal task allocation with a partial order, aimedat minimizing the total execution time. It suggests that adding certain conditions, which in-tuitively should improve the result, may actually worsen the objective function.Anomalies arising from varying executor performance are analyzed. It is shown that anuneven task distribution can create "bottlenecks" and reduce efficiency, even if the execu-tors' productivity increases. Conditions for avoiding anomalies are proposed: prioritizedtask assignment, load balancing, and dynamic rescheduling.

Assignment Technology Based on Improved Great Wall Construction Algorithm

The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, and threat assessments is developed for optimized task allocation and trajectory planning. The algorithm is enhanced using a good point set initialization strategy, Gaussian distribution estimation, and a Cauchy reorganization variant. The simulation results show that replacing straight-line distances with actual flight distances leads to more rational mission sequences, improving combat effectiveness under realistic terrain and threat conditions. The enhanced algorithm demonstrates superior accuracy and faster convergence.

Efficient Workflow Scheduling in Cloud Computing Using Hybrid Algorithm

In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, considering deadlines and budgets while optimizing task allocation to virtual machines (VMs). Our study hypothesizes that improved scheduling can reduce energy consumption, streamline process execution, and lower operational costs. To test this hypothesis, we conducted a comparative analysis of two optimization techniques: Genetic Algorithm with Multiple Particle Swarm Optimization (GA + MPSO) and Genetic Algorithm with Bat Algorithm (GA + BAT). The analysis reveals that the combination of Genetic Algorithm and Bat Algorithm (GA + BAT) excels in optimizing cloud computing workflow scheduling. GA + BAT demonstrated superior performance by significantly reducing energy consumption, shortening process execution times, and decreasing operational costs. These findings validate our hypothesis, underscoring that optimizing cloud computing workflow scheduling can deliver substantial benefits. Consequently, by adopting GA + BAT, cloud service providers can enhance efficiency, reduce costs, and foster a more sustainable and responsive cloud infrastructure.

Optimal Task Allocation and Sequencing for Flight Test Based on a Memetic Algorithm With Lexicographic Optimisation

ABSTRACTThe flight test plays an important role in the development of an aircraft. Currently, with the increasing complexity and higher validation requirements for aircraft, there is a crucial need to generate high‐quality flight test task schedules in an efficient way. This paper proposes a flight test task scheduling problem (FTTSP), which involves assigning suitable aircraft and executing the flight test tasks in a given order. Generally, the flight test duration (FTD) is the primary optimisation objective for the flight test task schedule, as it has a direct impact on aircraft development costs and the time to enter the market. In this study, the FTTSP not only considers FTD but also takes into account task transfer consumption (TTC). A mixed‐integer linear programming mathematical model is first formulated to describe the FTTSP characteristics with the optimisation of the FTD and the TTC in a sequential manner. Then, a memetic algorithm with lexicographic optimisation (MALO) is proposed, which can efficiently obtain a high‐quality solution and ensure that the most critical metric can be fully optimised. In MALO, a two‐vector encoding and a task logic relationship repair mechanism based on the binary tree are established. An idle time insertion decoding method is designed to improve the aircraft utilisation rate. In addition to the selection, crossover and mutation operators, a local search operator is designed to enhance the solution quality. Finally, the full‐scale test instances are generated for the FTTSP to evaluate the algorithm's performance. The numerical results demonstrate the effectiveness and competitiveness of the MALO in generating a high‐quality schedule for flight test tasks.

UAV-Assisted Computing Power Network Task Allocation and 3D Urban Trajectory Optimization

UAV-Assisted Computing Power Network Task Allocation and 3D Urban Trajectory Optimization

Optimization of Task Allocation for Resource-Constrained Swarm Robots

Optimization of Task Allocation for Resource-Constrained Swarm Robots

An energy aware AVAO algorithm-based optimal task allocation scheme for multi-core systems

An energy aware AVAO algorithm-based optimal task allocation scheme for multi-core systems

Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization

Context: In the age of rapid e-commerce growth; the Robotic Mobile Fulfillment Systems (RMFS) have become the major trend in warehouse automation. These systems involve the use of self- governed mobile chares to collect shelves as well as orders for deliveries with regard to optimization of task allocation and with reduced expenses. However, in a manner to implement such systems, one needs to find enhanced algorithms pertaining to resource mapping and the planning of movement of robots in sensitive environments. Problem Statement: Despite RMFS have certain challenges especially when it comes to the distribution of tasks and the overall distances that employees have to cover. Objective: The main goal of this paper is to propose a new compound optimization model based on RL-ACO to optimize the RMFS’s task assignment and navigation. Also, the direction of the study is to investigate how such methods can be applied to real-life warehouse automation and how effective such methods can be on a large scale. Methodology: This research introduces a new optimization model for RMFS selection which integrates reinforcement learning with Ant Colony Optimization (ACO). Specifically, a real gym environment was created to perform the order assignment and training in the way of robotic movement. Reinforcement Learning (RL) models were trained with Proximal Policy Optimization (PPO) for improving the dynamic control of robots and ACO was used for computing optimal shelf trajectories. The performance was also measured by policy gradient loss, travelled distance and time taken to complete the tasks. Results: The proposed framework showed potential in enhancing the efficiency of tasks required and the travel distances involved. In each of the RL models used the shortest paths were identified and the best route was determined to have a total distance of 102.91 units. Also, other values such as, value function loss and policy gradient loss showed learning and convergence in iterations. To build a global solution, ACO integration went a step forward in enabling route optimization through effective combinatorial problems solving. Implications: This research offers a practical, generalizable and flexible approach for the improvement of the operations of RMFS and thinking for warehouse automation.

Modeling and Optimization of Task Scheduling in Multi-Team IT Projects

The article explores the integration of the Critical Path Method (CPM) and Linear Programming (LP) for optimizing task allocation in multi-team IT projects. The research aims to develop and implement a model that minimizes the overall project completion time, considering task dependencies and ensuring an even distribution of workload among teams. The article describes a mathematical model that includes variables such as task duration, task dependencies, start and finish times, maximum completion time for all tasks, and binary variables for assigning tasks to teams. The model is solved using linear programming, which allows finding the optimal task distribution and the minimum project completion time. The research results demonstrate the effectiveness of the proposed model using real data. An analysis of tasks and their dependencies was conducted, the critical path was calculated, the task distribution among teams was determined, and the project completion time was evaluated. The proposed model ensures a reduction in the overall project completion time and an even workload distribution among teams. The article also provides recommendations for implementing the model in IT project management practice. These include training project teams, customizing the model to the specifics of each project, phased implementation, regular monitoring of the model’s effectiveness, and its continuous improvement. Implementing the model will significantly improve IT project management efficiency, minimize project execution time, and ensure optimal resource allocation. Thus, the research has shown that the integration of CPM and LP is an effective approach for optimizing task allocation in multi-team IT projects, ensuring the achievement of project goals within optimal timeframes.

Distributed Task Allocation Algorithm for Heterogeneous UAV Cluster Based on Game Theory

This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources, two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands, airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.

Theorising Bounded Rationality in EU Policy‐Making

AbstractThis article proposes a theoretical framework combining multi‐level governance (MLG) with bounded rationality. MLG theories have become increasingly popular to understand European Union (EU) policy‐making and interactions between national, sub‐national and EU levels. At the same time, principles of bounded rationality are entering the public policy debate, highlighting the role of cognitive biases in policy formation. However, these two developments have been separate, as bounded rationality has been little used in EU studies. The article therefore proposes a framework integrating the two lenses. This sheds new light on the optimal allocation of tasks between EU and national levels, given the prevalence of different cognitive biases at each level. Findings are that EU‐level policy‐making can be hampered by availability heuristics and undefined preferences, whilst national policy‐making is prone to zero‐sum bias and myopia. Insights are applied to research policy as an example, helping to explain obstacles to EU integration in this area.

Joint Task Allocation and Trajectory Optimization for Multi-UAV Collaborative Air–Ground Edge Computing

Joint Task Allocation and Trajectory Optimization for Multi-UAV Collaborative Air–Ground Edge Computing

Mired in tasks: how daily workplace multitasking shapes employee service innovation behavior

PurposeDrawing on conservation of resources theory, this paper aims to propose that daily multitasking increases employees’ daily role pressure, ultimately hindering their service innovation behavior. Furthermore, we suggest that trait mindfulness may mitigate this negative impact.Design/methodology/approachTo empirically test the study hypotheses, this paper conducted a multilevel diary field study, collecting data from 64 employees over 10 working days to comprehensively validate the theoretical model.FindingsThe findings indicate that daily multitasking indirectly reduces daily service innovation behavior by increasing employee role pressure. Notably, this indirect effect is more pronounced among employees with lower trait mindfulness, while it is less significant for those with higher trait mindfulness.Practical implicationsHotels should manage multitasking, reduce employee stress and promote service innovation by optimizing task allocation and redesigning work processes. Additionally, hotel management should adopt a multifaceted and innovative approach to foster mindfulness traits.Originality/valueThis study contributes to the literature on multitasking and innovation by examining how and when daily multitasking influences employee service innovation behavior.

Optimization of office process task allocation based on deep reinforcement learning

In office platforms, we often need to face the situation of a large number of parallel heterogeneous process tasks. This not only tests the ability of the task executors themselves, but also puts forward requirements for the performance of the collaborative scheduling system. Using the reinforcement learning method, combined with quantitative analysis such as collaborative coordination and slackness, and based on the Markov Bollinger theory, a multi-agent Bollinger model is proposed to realize the optimization scheduling system with overall process coordination and maximum completion time as the optimization objectives, thereby improving the overall execution efficiency. Taking the real business system process as the test scenario, under the same optimization objective, the reinforcement learning algorithm based on D3QN and DRL and the meta-heuristic algorithm based on ant colony are compared to verify the effectiveness of the proposed method.

Task allocation and path planning for multi-robot systems in intelligent warehousing

Faced with today's increasingly complex market demands, traditional manual warehouse systems are becoming inadequate, necessitating the urgent intelligent transformation and upgrading of warehouse systems. In this context, this paper aims to design a task allocation and path planning strategy for a multi-robot warehouse system to efficiently accomplish mixed single-robot and multi-robot types of warehouse tasks. The study proposes a warehouse task allocation strategy that incorporates traffic flow impact factors into the auction algorithm, optimizing task allocation by predicting robot density in various areas of the environment. For multi-robot formation tasks, a three-robot formation model based on the virtual structure method is designed. Additionally, a two-layer path planning strategy is proposed: the outer layer conducts global path planning based on the Floyd algorithm, while the inner layer resolves various collision issues through traffic rule constraints, achieving local optimal path planning. Simulation experiments conducted on the MATLAB platform show that the multi-robot system can flexibly handle mixed types of warehouse tasks, effectively reducing collision risks between robots and stagnation in dense areas, thereby improving the safety and efficiency of the multi-robot system. This study provides a reference for future research and practical applications of multi-robot systems.

Modification of the hopfield neural network model for solving the task of optimal task allocation in a group of mobile robots

In the context of group interaction among mobile robots, there arises the challenge of task distribution within the group, considering the robots' characteristics and the working environment. This study aims to modify the Hopfield neural network and develop methodologies for its application in solving the task allocation problem for an arbitrary number of tasks within a group of mobile robots. To achieve this, the Hopfield neural network is represented as a graph. An algorithm is presented, demonstrating the transition from the initial problem to the Traveling Salesman Problem (TSP). The application of the Hopfield model to the task distribution problem in a group of robots is described, along with the development of an optimization function calculation algorithm. An assessment is conducted to evaluate the impact of neural network parameters on the quality and speed of solving the optimization problem. By comparing it with other heuristic methods (genetic and ant colony algorithms), the domains of application for the modified algorithm are determined.