Task Assignment Problem Research Articles

A time-triggered dimension reduction algorithm for the task assignment problem

The task assignment problem is fundamental in combinatorial optimisation, aiming at allocating one or more tasks to a number of agents while minimizing the total cost or maximizing the overall assignment benefit. This problem is known to be computationally hard since it is usually formulated as a mixed-integer programming problem. In this paper, we consider a novel Time-Triggered Dimension Reduction Algorithm (TTDRA). We propose convexification approaches to convexify both the constraints and the cost function for the general non-convex assignment problem. The computational speed is accelerated via our time-triggered dimension reduction scheme, where the triggering condition is designed based on the optimality tolerance and the convexity of the cost function. Optimality and computational efficiency are verified via numerical simulations on benchmark examples.

Privacy-Aware Task Assignment for IoT Audit Applications on Collaborative Edge Devices

To meet the rapidly increasing demand for Internet of Things (IoT) applications, edge computing, as a novel computing paradigm, can combine devices at the edge of the network to collaboratively provide computing resources for IoT applications. However, the dynamic, heterogeneous, distributed, and resource-constrained nature of the edge computing paradigm also brings some problems, such as more serious privacy leakages and performance bottlenecks. Therefore, how to ensure that the resource requirements of the application are satisfied, while enhancing the protection of user privacy as much as possible, is a challenge for the task assignment of IoT applications. Aiming to address this challenge, we propose a privacy-aware IoT task assignment approach at the edge of the network. Firstly, we model the resource and privacy requirements for IoT applications and evaluate the resource satisfaction and privacy compatibility between edge devices and tasks. Secondly, we formulate the problem of privacy-aware IoT task assignment on edge devices (PITAE) and develop two solutions to the PITAE problem based on the greedy search algorithm and the Kuhn–Munkres (KM) algorithm. Finally, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the PITAE problem can be solved effectively and efficiently.

Rational Task Assignment and Path Planning Based on Location and Task Characteristics in Mobile Crowdsensing

With the great development in smart devices, mobile crowdsensing (MCS) has been an innovative paradigm for data gathering. Task assignment is a fundamental problem in MCS systems and applications. Previous studies only focused on the assignment of individual tasks, neglecting planning the task processing from a higher level, e.g., making assignments between task locations and workers, which impacts the crowdsensing performance adversely. Furthermore, task characteristics, e.g., route distance, task similarity, and task priority, have a great impact on the rationality of a visiting order and the quality of crowdsensing services. In this article, we tackle the problem of rational task assignment and path planning for MCS, which aims to assign a set of task locations to a set of workers and generate location visiting sequences. We measure the assignment rationality by taking into account geographical information and task characteristics, i.e., route distance, task similarity, and task priority. We prove that the problem of computing the rationality maximization task assignment is NP-hard. For the single-worker scenario, we reduce the problem to a simpler problem with respect to only route distance and task priority criteria since the similarity measurement has a fixed value. We propose an effective greedy algorithm. For the multiple-worker scenario, we first extend the greedy idea by considering all three criteria and then propose an effective approach by reducing the computational complexity of similarity measurement. Extensive experiments show that our proposed approaches achieve promising results.

A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method

Unmanned Aerial Vehicle (UAV) swarms are attracting more and more research attention due to their low cost and high efficiency. Task allocation is a highly important process for a UAV swarm, currently suffering from several constraints such as large scale and real-time requirements, with a possible solution being quite challenging. Hence, this paper models the behavioral characteristics of a wolf pack in its natural environment and exploits it to solve a UAV swarm dynamic task allocation problem in complex scenes. Specifically, the proposed method considers the non-balanced task assignment problem and utilizes the task scheduling algorithm to make the assignment results meet the task performance constraints. Moreover, a path planning algorithm and a coverage search algorithm appropriate for UAV swarms in complex scenes are proposed. Simulation experiments involving different target numbers and random target positions demonstrate that the suggested method achieves high task completion and balances the UAVs’ load affording a suitable solution for complex scenes.

Delay-Sensitive Task Assignment for Spatial Crowdsourcing

In spatial crowdsourcing, mobile workers are recruited to perform special tasks related to location and time. The task assignment has been a focus of the research community due to its importance in spatial crowdsourcing. However, most of the existing efforts focus on optimizing the task requester’s goal and do not consider the requirements and constraints of workers. Unlike the existing methods, we consider task assignment comprising the goals of both the workers and the requesters, that is, the trade-off regarding the reward and travel cost of a worker and the delay of a task. We formulate the delay-sensitive task assignment (DSTA) problem that optimizes the combined goal of minimizing costs and maximizing rewards of a worker; meanwhile, reducing the delay to wait until the task is assigned. The DSTA algorithm based on greed idea (DSTA-G) is proposed to address the problem. To improve the efficiency, we introduce the Geohash algorithm and propose a new solution DSTA-GH, which is scalable to large-scale datasets. Moreover, the task assignment method that only considers the worker’s objective (TAW) is proposed as the baseline. Finally, extensive experiments offer evidence that DSTA and DSTA-GH outperform the baseline in terms of performance and DSTA-GH only takes about 0.03% of the CPU time as compared to DSTA-G.

A Two-Objective ILP Model of OP-MATSP for the Multi-Robot Task Assignment in an Intelligent Warehouse

Multi-robot task assignment is one of the main processes in an intelligent warehouse. This paper models multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). A two-objective integer linear programming (ILP) model for solving OP-MDTSP is proposed. The theoretical bound on the computational time complexity of this model is O(n!). We can solve the small multi-robot task assignment problem by solving the two-objective ILP model using the Gurobi solver. The multi-chromosome coding-based genetic algorithm has a smaller search space, so we use it to solve large-scale problems. The experiment results reveal that the two-objective ILP model is very good at solving small-scale problems. For large-scale problems, both EGA and NSGA3 genetic algorithms can efficiently obtain suboptimal solutions. It demonstrates that this paper’s multi-robot work assignment methods are helpful in an intelligent warehouse.

Cooperative task assignment in spatial crowdsourcing via multi-agent deep reinforcement learning

With the rapid development of mobile Internet, spatial crowdsourcing (SC) has become an emerging paradigm with many applications. As a key challenge in SC, the problem of task assignment has attracted extensive research. However, most previous work focus on the single mode setting where cooperation among workers is either allowed or prohibited. Moreover, only short-term benefit of either workers or requesters is considered separately. To address these issues, we first propose a new spatial crowdsourcing scenario that permits cooperation with no mandate among workers and tasks. Furthermore, we propose a multi-agent deep reinforcement learning (MADRL) solution for SC. Specifically, we extend the Advantage Actor–Critic (A2C) algorithm to multi-agent settings, and design a reward function that considers both local and global return. Through the game between agents, we generate a task assignment scheme that considers both workers’ and requesters’ long-term benefit. In order to improve the performance of our model, we further introduce the attention mechanism to guide information sharing between agents. We use simulations to conduct experimental evaluation on both synthetic and real-world datasets. Experimental results show that our proposed method outperforms other state-of-the-art task assignment algorithms in terms of worker profitability rate and task completion rate.

Energy-Efficient Joint Task Assignment and Power Control in Energy-Harvesting D2D Offloading Communications

In this article, we investigate the joint task assignment and power control problems for Device-to-Device (D2D) offloading communications with energy harvesting. Exploiting the D2D links for data offloading allows reducing the traffic load of the cellular base stations. The energy consumed by the D2D transmitters for data offloading can be compensated by energy harvesting. The main objective is to maximize the energy efficiency (EE) under energy causality and delay constraints, assuming a harvest–transmit model. Hence, the proposed model results in a nonconvex problem. We first derive an equivalent and more tractable optimization problem by exploiting nonlinear fractional programming, also known as the Dinkelbach method. We propose a layered optimization method by decoupling the EE maximization problem into power allocation and offloading assignment. The first step consists of computing the optimal power values by applying the conjugate gradient method. In the second step, the problem of the D2D pair formation for data offloading amounts to the bipartite graph matching. It can be solved to optimality using the Hungarian algorithm. Extensive simulations were performed on various network scenarios. Numerical results show that the proposed resource allocation scheme achieves remarkable improvements in terms of network EE.

The Application of Improved Harmony Search Algorithm to Multi-UAV Task Assignment

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints.

Learning-based airborne sensor task assignment in unknown dynamic environments

In sensor management, the existing researches rely on traditional system modeling and strive to maximize the information superiority. In fact, on the one hand, complex environmental disturbance, incomplete information or uncooperative behavior in air combat missions often bring out unknown system evolution; on the other hand, to take full advantage of sensor effectiveness is of course essential, but more importantly, the detection security is the primary guarantee. This paper proposes the airborne sensor task assignment problem in unknown dynamic environments. Different from traditional methods that minimize the estimation error covariance or information entropy based on system dynamic model, our scheme needs to maximize agent survival while maintaining the necessary sensor detection without such model support. In assignment implementation, it is not straightforward to apply existing reinforcement learning methods, but design the state space and rewards ingeniously to meet the actual combat requirements. First, instead of selecting the locations of agents and targets as fundamental and infinite state variables, we consider the situation variables, such as target threat ranking together with cumulative radiation and information acquisition indication of sensors, which are all discrete state variables to reduce computational burden. Second, the reward structure is also designed based on the complex constraints of the mission, which is to encourage lower assignment risk and relatively full utilization of sensing, while penalizing too dangerous continuance assignment and inadequate assignment revenue. Simulations show that our proposed scheme achieves the desirable mission completion rate and the acceptable target tracking accuracy.

A Survey on Task Assignment in Crowdsourcing

Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow parameters. In this survey, we review task assignment methods that address: heterogeneous task assignment, question assignment, and plurality problems in crowdsourcing. We discuss and contrast how these methods estimate worker performance, and highlight potential challenges in their implementation. Finally, we discuss future research directions for task assignment methods, and how crowdsourcing platforms and other stakeholders can benefit from them.

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

Conflict-free scheduling of large-scale multi-load AGVs in material transportation network

The process of scheduling an automated guided vehicle (AGV) includes task scheduling, path planning, and traffic control management. The conflict-free scheduling of large-scale multi-load AGVs is a challenging problem in manufacturing logistics and transportation. To solve the problem of scheduling such AGVs in a network logistics system, this study proposes a method of task allocation based on adjacency combination and the shortest path principle. Three priority rules for the mobility of AGVs between nodes are designed, and a transportation strategy that combines single and two-way paths is proposed to reduce computational complexity. By combining with Dijkstra’s method, the authors develop a method to prevent deadlocks and collisions between multiple AGVs based on a timetable of reservations that hierarchically handles conflicts among nodes in multiple stages. Such constraints as AGV congestion or deadlock weaken the effectiveness of the shortest distance rule-based solution. Based on the above, a heuristic search method based on variable neighborhood search is further proposed to optimize the problem of multi-AGV task assignment, and a corresponding theorem is given to avoid the generation of unfeasible solutions by the neighborhood operators and improve the efficiency of the solution. The results of experiments show that the proposed method can adequately solve the problem of scheduling multiple AGVs in a large and dense network.

Path Planning for Multi-Vehicle-Assisted Multi-UAVs in Mobile Crowdsensing

Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.

Optimal spraying task assignment problem in crop protection with multi-UAV systems and its order irrelevant enumeration solution

Creating a team of crop protection unmanned aerial vehicles (UAVs) for a scheduled spraying task over fields is a method that seeks to overcome the various limitations of using individual UAVs. However, given some of the issues with pesticide spraying operations involving multiple UAVs (multi-UAVs), such as differences in hardware, irregular field borders, spray drift, and operator proficiency, assigning spraying sub-tasks to multi-UAVs during crop protection is more important and more complex than for other homogeneous multi-agent systems in common use. In this study, a mathematical model of a heterogeneous multi-UAV crop protection system is established. Based on this model, an optimal spraying task assignment problem (OSTAP) for multi-UAVs used in crop protection is proposed. To solve this problem, it is converted to an equivalent combinatorial problem. The order irrelevant enumeration solution (OIES) algorithm is proposed where time and space complexities are more efficiently implemented on a horizontal comparison among OIES, equal task assignment (ETA), sequential task assignment (STA), discrete particle swarm optimisation (DPSO), and a full permutation algorithm. Numerical simulation with a six-UAV system showed that OIES could solve OSTAP in a finite time using C++ in the Visual Studio environment and performed efficiently and accurately. Finally, with the proposed OIES, an OSTAP application software was developed as a visualisation of an OSTAP solution. With this application, a spraying test with the five-UAV system was carried out to determine the effectiveness of OIES in an actual pesticide spraying operation. Test results showed that OIES performs best with these algorithms, which takes only 51.3% of the operating time taken by ETA and it requires fewer flights.

OCVC: An Overlapping-Enabled Cooperative Vehicular Fog Computing Protocol

With increasing time-critical and computation-intensive tasks generated by mobile applications, vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on roadside units (RSUs) or cloud centers. In VFC, tasks are offloaded to vehicular fog nodes local to the client devices, which exploits the under-explored computational resources of nearby vehicles. In this paper, we propose a novel cooperative vehicular fog computing architecture from an overlapping perspective, termed Overlapping-enabled Cooperative Vehicular fog Computing (OCVC) to fully utilize vehicular fog nodes' local potential resources. Different from traditional cooperative VFC architecture where each vehicle only works in one fog computing group at one time, the proposed OCVC architecture enables vehicles to participate in different computing groups simultaneously, and thus is able to fully exploit potential computational resources in an overlapping manner. In addition, we provide a distributed OCVC scheme to solve the complicated computing group formation, overlapping resource allocation, and task assignment problem by employing the overlapping coalition formation (OCF) game framework and a heuristic offloading algorithm. We conduct simulations for performance comparison in terms of diversified performance metrics and numerical results show that the proposed OCVC scheme performs better than other benchmarks under different conditions.

Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency

In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.

Two-stage Bilateral Online Priority Assignment in Spatio-temporal Crowdsourcing

With the advent of intelligent technology, the users of spatio-temporal crowdsourcing and their participation in the crowdsourcing tasks continue to increase exponentially. This poses new challenges to the crowdsourcing field. One of the core research areas of spatio-temporal crowdsourcing is task assignment. Most of the existing research on task assignment is focused on offline optimal task assignment, where, the platform has already learned all the information about workers and tasks beforehand. However, these studies cannot obtain good results in real-world situations. At the same time, online task assignment problems often result in local optimal assignment. To solve these problems, more attention needs to be paid to online task assignments and the arrival time of workers. This paper proposes an Online Bilateral Assignment (OBA) problem based on the online assignment model. The competitive ratio of the Greedy algorithm is analyzed according to the OBA problem model. Also, another solution to the OBA problem according to the Greedy algorithm, the Improved-Baseline algorithm, is proposed. Additionally, a Bilateral Online Priority Reassignment algorithm (BOPR) is proposed. The BOPR algorithm realizes real-time task/worker assignment through the bilateral assignment as a solution for online task assignment. In order to guarantee the number of matching tasks, a priority queue is designed in the BOPR algorithm. Considering the waiting time deadlines of tasks and workers and the error rate for priority ranking, it avoids tasks and workers waiting too long and assigns each task to the best possible extent. On this basis, a two-stage assignment strategy is designed for unsuccessful tasks, which could minimize the error rate of the task and significantly improve the efficiency of task assignment. Finally, through experiments on real data sets, the algorithm's performance in terms of global utility value and the number of matches is evaluated.

Intelligent Warehouse Robot Scheduling System Using a Modified Nondominated Sorting Algorithm

In intelligent warehouse, the problem of transporting goods in intelligent warehouse is becoming increasingly complex, and the traditional way of automatically guiding vehicles (AGVs) is inefficient, so automated robot systems are introduced into intelligent warehouses. In this paper, a task assignment model for robots is presented with the transportation problem of robots in intelligent warehouse as the research background. To solve the robot task assignment problem in intelligent warehouse, a novel Pareto‐based multiobjective optimization algorithm (MOEA) is proposed, and the aggregation function is invoked to replace the crowding distance; the brain storm operator is used for crossover and mutation. Finally, the ability of the algorithm to solve the benchmark test problem suite and real‐world problems is experimentally confirmed.

An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing

The Blockchain has been given great attention in recent literature among emerging technologies in software architectures. More specifically, when verifiable transactions between untrusted parties are concerned in a safe and reliable environment, its peculiar decentralized and tamper-proof structure makes it suitable for a vast class of business domains, such as Cloud Manufacturing, which is a new paradigm in the industry based on cloud technologies. However, the stiffness of existing solutions, that are unable to provide and implement heterogeneous services in a Cloud environment, emphasizes the need of a standard framework to overcome this limit and improve collaboration. Firstly, this paper introduces a Blockchain based platform designed with Smart Contracts for improving digital processes in a manufacturing environment. The primary contribution is the integration of two popular cloud technologies within the Blockchain: Docker, a scalable platform to run applications in lightweight environments, and Cloud Storage. Each process available in the platform requires input files and produces output files by using cloud storage as a repository and it is delivered by the owner as a self-contained Docker image, whose digest is safely stored in the chain. Secondly, with the purpose of selecting the fastest node for each new process instance required by consumers, we introduce a task assignment problem based on a deep learning approach and past metrics. The proposed platform is applied to a real-world industrial case study regarding ophthalmic lenses manufacturing and the optimization of lens surface calculation.