Task Assignment Problem Research Articles

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

UAV Network Path Planning and Optimization Using a Vehicle Routing Model

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications

Solving Task Assignment Problem Using Branch and Bound Method

Solving Task Assignment Problem Using Branch and Bound Method

Joint task assignment and resource allocation in VFC based on mobility prediction information

Vehicular Fog Computing (VFC) is a novel architecture that utilizes end-user clients or near-user edge devices to reduce the processing and transmission time of vehicular tasks. However, the high mobility of vehicles in VFC presents a challenge in coordinating vehicles and managing multi-dimensional resources. In this paper, we investigate the joint task assignment and resource allocation optimization problem by incorporating mobility prediction information. We propose two schemes that consider execution speed and accuracy. The first scheme decomposes the original problem into two sub-problems: subtask assignment and bandwidth resource allocation, solved by matching and convex optimization methods. The second scheme uses an iterative optimization process based on 0–1 integer linear programming. Simulation results based on real-world mobility datasets demonstrate the effectiveness and complementarity of the proposed schemes.

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.

Hierarchical optimization method of space patrol task assignment based on deep neural network and consensus-based bundle algorithm

This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has significant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios.

Self-organizing Coalition Formation based on Non-cooperative Games in Social Networks

In multi-agent systems, the individual agent must form coalitions to accomplish complex tasks. However, the centralized management model is not flexible in a dynamic environment. To overcome the restriction caused by central control, the paper presents a self-organizing dynamic coalition mechanism based on game theory. Firstly, we adopt a distributed network to communicate among agents, allowing agents to solve the real-time task assignment problem autonomously. Next, a non-cooperative game negotiation model is introduced to find the optimal strategy for each agent. Finally, the effectiveness of our mechanism is validated by comparing it with the traditional command model in three distributed networks. Experimental results indicate the proposed mechanism is capable to improve the system utility.

Adjustable Fully Adaptive Cross-Entropy Algorithms for Task Assignment of Multi-UAVs

This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish the classification, attack, and verification tasks of targets under resource, precedence, and timing constraints. Due to complex coupling among these tasks, we decompose the considered problem into two subproblems: one with continuous and independent tasks and another with continuous and correlative tasks. To solve them, we first present an adjustable, fully adaptive cross-entropy (AFACE) algorithm based on the cross-entropy (CE) method, which serves as a stepping stone for developing other algorithms. Secondly, to overcome task precedence in the first subproblem, we propose a mutually independent AFACE (MIAFACE) algorithm, which converges faster than the CE method when obtaining the optimal scheme vectors of these continuous and independent tasks. Thirdly, to deal with task coupling in the second subproblem, we present a mutually correlative AFACE (MCAFACE) algorithm to find the optimal scheme vectors of these continuous and correlative tasks, while its computational complexity is inferior to that of the MIAFACE algorithm. Finally, numerical simulations demonstrate that the proposed MIAFACE (MCAFACE, respectively) algorithm consumes less time than the existing algorithms for the continuous and independent (correlative, respectively) task assignment problem.

Task Assignment for UAV Swarm Saturation Attack: A Deep Reinforcement Learning Approach

Task assignment is a challenging problem in multiple unmanned aerial vehicle (UAV) missions. In this paper, we focus on the task assignment problem for a UAV swarm saturation attack, in which a deep reinforcement learning (DRL) framework is developed. Specifically, we first construct a mathematical model to formulate the task assignment problem for a UAV swarm saturation attack and consider it as a Markov Decision Process (MDP). We then design a policy neural network using the attention mechanism. We also propose a training algorithm based on the policy gradient method so that our agent can learn an effective task assignment policy. The experimental results have shown that our DRL method can generate high-quality solutions for different problem scales, which meets the requirements of real-time and flexibility in the actual situation.

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

Due to the computational complexity, the staff scheduling problem is generally decomposed into two subproblems, i.e. staff rostering and task assignment problems. Very often, this could result in suboptimal solutions. Thus, we study an integrated staff rostering and task assignment (ISRTA) problem in this paper. Previous studies formulate this problem as task- and roster-based ISRTA models, which can only solve small-scale problems. To overcome this limitation, this paper proposes a novel shift-based ISRTA model, which exhibits less symmetry and greatly reduces the number of variables and constraints. We extend the shift-based ISRTA model to six real-world requirements, i.e., shift flexibility, qualification, travel time, contractual rules, task connection preference, and fairness, which ensure the applicability of the result schedule. To efficiently solve the shift-based ISRTA model, we propose a clique-based aggregated model that enhances the performance of the basic shift model. Further, two heuristics, i.e., rolling horizon algorithm and iterative shift selection algorithm, are proposed to speed up the solution process. Based on randomly generated 24 benchmark instances and 16 real-world instances from a major airline, computational results show that the shift-based ISRTA model outperforms the task- and roster-based ISRTA models. For small- and mid-scale problems, the shift-based ISRTA model can be solved optimally or near-optimally directly using a commercial solver, and the rolling horizon algorithm and iterative shift selection algorithm can solve large-scale problems and obtain high-quality solutions.

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic.

Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challenging for medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots. In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 ms) calculate near-optimal assignment solutions (within 1.15 times of the optimal value). Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.

Perception Task Offloading With Collaborative Computation for Autonomous Driving

Autonomous driving has so far received numerous attention from academia and industry. However, the inevitable occlusion is a great menace to safety and reliable driving. Existing works have primarily focused on improving the perception ability of a single autonomous vehicle (AV), but the safety problem brought by occlusions remains unanswered. In this paper, we propose a multi-tier perception task offloading framework with a collaborative computing approach, where an AV is able to achieve a comprehensive perception of the concerned region-of-interest (RoI) by leveraging collaborative computation with nearby AVs and road side units (RSUs). Besides, the collaborative computation provides offloading service for computationally intensive tasks so as to reduce processing delay. Specifically, we formulate a joint problem of perception task assignment, offloading and resource allocation, by fully considering the AV’s mobility, task dependency, and delay requirement. The collaborative offloading is modeled as a mixed-integer nonlinear programming (MINLP) problem. We design a two-layer binary intelligent firefly (TL-BIFA) algorithm to solve MINLP, with the goal of minimizing execution delay. The proposed TL-BIFA synthesizes the advantages of heuristic methods and deterministic methods. Through extensive simulations, the proposed collaborative offloading approach and the TL-BIFA show superiority in enhancing the autonomous driving system’s safety, efficiency and resource utilization.

Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

A dynamic integrated scheduling method based on hierarchical planning for heterogeneous AGV fleets in warehouses.

In modern industrial warehouses, heterogeneous and flexible fleets of automated guided vehicles (AGVs) are widely used to improve transport efficiency. However, as their scale and limit of battery capacity increase, the complexity of dynamic scheduling also increases dramatically. The problem is to assign tasks and determine detailed paths to AGVs to keep the multi-AGV system running efficiently and sustainedly. In this context, a mixed-integer linear programming (MILP) model is formulated. A hierarchical planning method is used, which decomposes the integrated problem into two levels: the upper-level task-assignment problem and the lower-level path-planning problem. A hybrid discrete state transition algorithm (HDSTA) based on an elite solution set and the Tabu List method is proposed to solve the dynamic scheduling problem to minimize the sum of the costs of requests and the tardiness costs of conflicts for the overall system. The efficacy of our method is investigated by computational experiments using real-world data.

A Multi-Objective Mission Planning Method for AUV Target Search

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated.

Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization

Mobile crowdsourcing is rapidly boosting the Internet of Things revolution. Its natural development leads to an adaptation to various real-world scenarios, thus imposing a need for wide generality on data-processing and task-assigning methods. We consider the task assignment problem in mobile crowdsourcing while taking into consideration the following: (i) we assume that additional information is available for both tasks and workers, such as location, device parameters, or task parameters, and make use of such information; (ii) as an important consequence of the worker-location factor, we assume that some workers may not be available for selection at given times; (iii) the workers' characteristics may change over time. To solve the task assignment problem in this setting, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Optimistic Matching for Mobile Crowdsourcing</i> (AOM-MC), an online learning algorithm that incurs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tilde{O}(T^{(\bar{D}+1)/(\bar{D}+2)+\epsilon })$</tex-math></inline-formula> regret in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> rounds, for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon &gt;0$</tex-math></inline-formula> , under mild continuity assumptions. Here, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bar{D}$</tex-math></inline-formula> is a notion of dimensionality which captures the structure of the problem. We also present extensive simulations that illustrate the advantage of adaptive discretization when compared with uniform discretization, and a time- and location-dependent crowdsourcing simulation using a real-world dataset, clearly demonstrating our algorithm's superiority to the current state-of-the-art and baseline algorithms.

SA-reCBS: Multi-robot task assignment with integrated reactive path generation

In this paper, we study the multi-robot task assignment and path-finding problem (MRTAPF), where a number of robots are required to visit all given tasks while avoiding collisions with each other. We propose a novel two-layer algorithm SA-reCBS that cascades the simulated annealing algorithm and conflict-based search to solve this problem. Compared to other approaches in the field of MRTAPF, the advantage of SA-reCBS is that without requiring a pre-bundle of tasks to groups with the same number of groups as the number of robots, it enables a part of robots needed to visit all tasks in collision-free paths. We test the algorithm in various simulation instances and compare it with state-of-the-art algorithms. The result shows that SA-reCBS has a better performance with a higher success rate, less computational time, and better objective values.

ACTA: Autonomy and Coordination Task Assignment in Spatial Crowdsourcing Platforms

Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.

Solving an airport ground service task assignment problem with an exact algorithm.

In this paper, an airport ground service task assignment problem is studied. A task represents a service, which must be performed by one or multiple ground crew of a shift with required qualification/proficiency within a prescribed time period. For every assigned task, define "task priority" times "task duration" as the "benefit" generated. The objective is to maximize the summation of "benefit" for all the assigned tasks. The problem is modeled as an integer linear programming problem with mathematical formulation. A branch-and-price algorithm is proposed for solving the problem instances to optimality. To expedite the column generation process, an acceleration strategy is proposed. The computational results show that our proposed branch-and-price algorithm is capable of solving large-sized instances and the acceleration strategy is quite effective in reducing the computational time. Moreover, the impact of changing various characteristics of tasks and shifts on the performance of the algorithm is studied in detail with supporting computational experiments. In particular, the impact of reducing the qualifications is significant with 20.82% improvement in the objective value.

Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method.