In the paper, we present a study devoted to the utilization of multiple-solution tasks (MSTs) in combinatorics as a part of a pre-service teachers course on didactics of mathematics from the view of the mathematics teachers’ specialized knowledge (MT...
The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even...
Mathematical creativity (MC) plays an important role in mathematics and education; however, its conceptualization and assessment remain inconsistent across empirical studies. This systematic review examined how MC has been defined, conceptualized, an...
The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and under...
Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for lar...
The Multiple Traveling Salesman Problem is an extension of the famous Traveling Salesman Problem. Finding an optimal solution to the Multiple Traveling Salesman Problem (mTSP) is a difficult task as it belongs to the class of NP-hard problems. The pr...
With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promis...
Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works us...
This paper examines the added value of a digital tool that constitutes a new model to introduce students to multiplication. Drawing on the theory of semiotic mediation, the semiotic potential of this new model is analysed with respect to the same tas...
This paper focuses on cooperative multi-task assignment and re-assignment problems when multiple unmanned aerial vehicles (UAVs) attack multiple known targets. A unified multi-objective optimization framework for UAV cooperative task assignment and r...
International trade allows countries to expand their markets and access goods and services that otherwise may not have been domestically available. As a result of international trade, the market is more competitive. This ultimately results in more co...
The task context switch operation, the inter-task synchronization and communication mechanisms, as well as the jitter occurred in treating aperiodic events, are crucial factors in implementing real-time operating systems (RTOS). In practice and liter...
We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a war...
The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To ad...
Task allocation is crucial for autonomous underwater vehicle (AUV) collaboration in multi-AUV maritime search and rescue missions. In real projects, there are possible target areas existing in task areas, which are not expected to be divided. Motivat...
A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperatio...
Reward sparsity is a considerable problem in reinforcement learning when solving complex problems. In an environment where the reward is given only after successfully solving a problem in multiple steps, the signals for optimization can be too weak,...
This study investigates the task allocation problem for multiple mobile robots in complex real-world scenarios. To address this challenge, a distributed game-theoretic approach is proposed to enable collaborative decision-making. First, the task allo...
Internet of Things (IoT) devices can be used to provide safety, security, and other services that ensure that smart systems work as intended. However, the increasing complexity of the tasks is increasing the potential of performance loss when limited...
A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar...
In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classification. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism...
Robots with multiple degrees of freedom (DOFs), such as underwater vehicle–manipulator systems (UVMSs), are expected to optimize system performance by exploiting redundancy with various basic tasks while still fulfilling the primary objective....
Optimal load distribution (OLD) among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. How...
In this paper, a distributed, autonomous, cooperative mission-planning (DACMP) approach was proposed to focus on the problem of the real-time cooperative searching and surveillance of multiple unmanned aerial vehicles (multi-UAVs) with threats in unc...
UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful...
The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An...
Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning...
In this paper, a dynamic multi-task scheduling prototype is proposed to improve the limited resource utilization in the vehicular networks (VNET) assisted by mobile edge computing (MEC). To ensure quality of service (QoS) and meet the growing data de...
With reference to the MeSmart project, the Municipality of Messina is making a great investments to deploy several types of cameras and digital devices across the city for carrying out different tasks related to mobility management, such as traffic f...
The unmanned aerial vehicle (UAV) has drawn increasing attention in recent years, especially in executing tasks such as natural disaster rescue and detection, and battlefield cooperative operations. Task assignment and path planning for multiple UAVs...
In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency a...
Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an...
This paper proposes a multi-equipment task assignment model for the horizontal stripe pre-cut mining method to address the problem of cooperative scheduling operation of multi-equipment in underground metal mines under complex constraints. The model...
This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots...
This paper presents an improved knowledge-based genetic algorithm for relay satellite task scheduling. It aims to address the issue of ensuring high-priority tasks under complex constraints and resource competition. The algorithm enhances the high-pr...
For a service robot, learning appropriate behaviours to acquire task knowledge and deliberation in various situations is essential, but the existing methods do not support merging the plan-based activity experiences from multiple situations in the sa...
As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offlo...
In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously...
A tremendous amount of work has been done in the recent years in the optimization of input parameters, however, current optimization techniques can only provide a single optimal input process parameter combination. Although alternative techniques hav...
For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC ser...
The scheduling of rescue aircraft needs to be studied in depth because of its criticality for the general aviation rescue of forest fires. This paper constructs a collaborative schedule optimization model for general aviation rescue under the conditi...
Cooperative jamming task allocation for UAV swarms must jointly consider radar priority, the effectiveness of different jamming modes, and heterogeneous resource limits. This paper establishes a radar threat assessment model from radar operating para...
Plastic pollution in aquatic environments is a major ecological problem requiring scalable autonomous solutions for cleanup. This study addresses the coordination of multiple Autonomous Surface Vehicles by formulating the problem as a Partially Obser...
To address the severe challenges of task allocation for UAV swarms in uncertain complex environments, this paper introduces the concept of equivalent load, constructs the load capability matrix of a single UAV and the task required load matrix of the...
The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collabora...
This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto so...
The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a p...
In this article, an optimization task with nonlinear differential-algebraic equations (DAEs) is considered. As a main result, a new solution procedure is designed. The computational procedure represents the sequential optimization approach. The propo...
Energy optimization for periodic applications running on safety/time-critical time-triggered multiprocessor systems has been studied recently. An interesting feature of the applications on the systems is that some tasks are strictly periodic while ot...
With the rapid growth in space-imaging demands, the scheduling problem of multiple agile optical satellites has become a crucial problem in the field of on-orbit satellite applications. Because of the considerable solution space and complicated const...
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