Phase Transitions in the Multi-Vehicle Task Assignment Problem

A decomposition approach to multi-vehicle cooperative control

A shift-based model to solve the integrated staff rostering and task assignment problem with real-world requirements

High-performance computing systems typically employ heterogeneous multicore design to improve both execution performance and efficiency. Task assignment is critical in exploiting the diversity of computation capability, energy consumption, as well as communication cost on heterogeneous multicore processors. In this paper, we explore the opportunity of task assignment on heterogeneous multicore processors to minimize execution and communication costs considering time constraint. The general heterogeneous task assignment problem is NP-Complete. However, we find that optimal task assignment can be achieved for widely used, tree-shaped task graphs using dynamic programming. We first propose a dynamic programming algorithm, the Optimal Tree Assign (OTA) algorithm, to generate optimal assignments for trees. Then, we develop the Integer Linear Programming model of the general task assignment problem for Directed Acyclic Graphs. A polynomial-time heuristic, the Extended Tree Assignment algorithm, is also proposed to produce near-optimal solutions for the general heterogeneous task assignment problem efficiently. The experimental results show that the proposed algorithms outperform both homogeneous task assignment method and greedy strategy for all the benchmarks. The OTA algorithm reduces the total system time by 42.5 percent and 23.5 percent on average compared with the homogeneous task assignment method and greedy algorithm, respectively.

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

Vehicular mobile crowdsensing (VMCS) has emerged as powerful and promising paradigm for mobile crowdsensing (MCS) by deploying sensing devices on connected vehicles to collect perception data. Task assignment plays a crucial role in determining the efficiency and performance of in VMCS systems. However, the large number of connected vehicles and sensing tasks involved, coupled with the implication of task information and vehicle status over time, which poses new challenges to system stability. Therefore, this paper puts forth an edge computing-enabled task assignment for VMCS, seeking to enhance the efficiency of task assignment by scheduling a reasonable match between sensing tasks and connected vehicles, and to reduce decision-making delays. Specifically, the task assignment problem is acknowledged as an optimization process with the objective of reducing the tasks waiting duration by considering the task completion sequence rationality under the task time constraint and the energy limitation of the vehicles, and is demonstrated to be an NP-hard problem. Firstly, a valid task sequence is mainly generated for vehicles prior to task assignment to reduce the collision rate between different lists of vehicular tasks. Secondly, this issue is resolved by employing a cooperative game theory-based coalition formation game. On the basis of the introduction of the coalition formation game, the switching rule and historical selection set are used to prevent cyclical coalition switching, and a convergence-guaranteed algorithm with low complexity is proposed to achieve a Nash stable solution. Finally, a thorough set of experiments are performed under various dynamic conditions to show that our suggested scheme is superior to other comparison schemes in terms of task completion percentage, average waiting time, and running time.

As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

For the task assignment problem in a smart logistic center with many autonomous mobile robots, a task assignment method is proposed based on a market auction idea. Due to the existence of asymmetrical shelf-shift tasks within the center and diverse self-finished cost of each task besides the correlation between tasks, the problem of multi-robot task assignment in a logistic center is different from multi-robot routing problem. By introducing the correlation function and self-finished cost function, the model of task assignment is presented. Then, the bidding strategy is given by analyzing the performance difference. On these bases, an auction approach is developed for the static and dynamic task assignment. Finally, the simulation results show the applicability of the algorithm.

This paper addresses task assignment problem fortree-structure task model on heterogeneous multicore embedded systems with time constraint considering both execution time and communication load. The goal is to minimize the total system cost for a given task graph representing a set of tasks and data dependencies in a heterogeneous multicore systemwhile the time constraint is satisfied. Instead of assigning all the tasks to processors of the same type in a homogeneous environment, heterogeneous task assignment usually can reduce the system cost by exploring various capacities and costs in a heterogeneous multicore system. The general heterogeneous assignment problem is NP-complete. In this paper, we show that optimal task assignment can be found for some widely-used, special task graphs, such as tree-structure graph, using dynamic programming. A dynamic programming algorithm, the Tree Assign (TA) algorithm, is proposed in this paper to solve the heterogeneous task assignment problem for tree-structure task graphs. The experimental results show that our algorithm reduces the total system cost by 31.8% compared with assignment results on homogeneous multicore systems. Also, our algorithm achieves an average cost reduction by 21.9% compared with greedy algorithm.

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

Task assignment is a fundamental research problem in mobile crowdsensing (MCS) since it directly determines an MCS system’s practicality and economic value. Due to the complex dynamics of tasks and workers, task assignment problems are usually NP-hard, and approximation-based methods are preferred to impractical optimal methods. In the literature, a graph neural network-based deep reinforcement learning (GDRL) method is proposed in Xu and Song (2022) to solve routing problems in MCS and shows high performance and time efficiency. However, GDRL, as a centralized method, has to cope with the limitation in scalability and the challenge of privacy protection. In this paper, we propose a multi-agent deep reinforcement learning-based method named CQDRL to solve a task assignment problem in a decentralized fashion. The CQDRL method not only inherits the merits of GDRL over traditional heuristic and metaheuristic methods but also exploits computation potentials in mobile devices and protects workers’ privacy with a decentralized decision-making scheme. Our extensive experiments show that the CQDRL method can achieve significantly better performance than other traditional methods and performs fairly close to the centralized GDRL method.

Cooperative reconnaissance is an important task scenario of multi-UAV cooperative mission. The complex environments require UAVs to move along the road-network and complete reconnaissance of the road in mission scenarios such as urban warfare, precision agriculture, logistics and transportation. In this paper, considering the road-network constraints during the task execution, the task assignment problem of multi-UAV cooperative road-network reconnaissance is defined, and the model of multi-UAV road-network reconnaissance traveling salesman problem (MRRTSP) is established. Furthermore, a customized genetic algorithm for road-network reconnaissance (CGA-RR) is used to solve the task assignment problem. Simulation results show the effectiveness of the algorithm, which can achieve the optimal multi-UAV task assignment.

Task Assignment is one of fundamental problems in multiple UAV systems. Decision of one UAV has effect on the others in the same team. Because the performance no longer measures from an individual vehicle, the tasks have to be distributed to maximize the team performance. An underlying problem is computational burden that grows considerably as the system gets larger. Many approaches cannot extend to larger systems because of this problem. The objective of this paper is to develop a quasi-decentralized methodology to solve the task assignment problem using less computational time. The assignment is decomposed into sub-problems that can be solved quickly. Then the results are combined in order to determine the best solution for the team performance. This approach is also more adaptable for dynamic environment. The coordination of multiple UAVs is utilized as a platform to demonstrate the methodology. The performance is compared to a centralized approach. For a system with five UAVs and ten goals, the cost is 5% higher than the optimal cost, while the run time is cut down by 55% on average.

This article deals with the task assignment problem for multiple autonomous underwater vehicles to efficiently collect underwater data from sensors. We formulate a unified framework to consistently address the heterogeneous task assignment problem (nonemergency and emergency cases) without strictly distinguishing the mixed cases. First, a unified problem, which bridges the gap between different constraints and optimization objectives of different cases, is constructed. Then, the proposed reinforced self-organizing mapping algorithm is reinforced in three aspects: the regional learning rate, the self-configuring neuron (SCN) strategy, and the workload balance mechanism. Specifically, the proposed regional learning rate comprehensively considers the individual worth of tasks and the topology to generate the regional learning rate of dynamic task regions, which consists of dynamic remaining tasks and the reconstructed topology. Based on this idea, the constructed unified problem can be solved consistently. Furthermore, the proposed SCN strategy optimizes the neuron population both in quality and quantity, and guides the update of neurons with enriched historical information to improve the mapping ability. This strategy greatly improves learning efficiency and applicability in a wide range of scenarios. Meanwhile, the proposed workload balance mechanism takes into consideration of both the work capability and consumed energy to extend the continuous working capability. The numerical results validate the effectiveness and adaptability of the proposed unified task assignment framework.