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This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. [ABSTRACT FROM AUTHOR]

This study deals with the task assignment problem in a heterogeneous unmanned aerial vehicle system under the multiple constraints of limited resources, effective task execution scope, obstacle avoidance and task priority. Considering the nonlinearity, multimoding and high complexity of the task assignment problem, many existing optimization methods may fall into local optimum and even fail to provide satisfactory solutions. To address the issues, the discrete adaptive search whale optimization algorithm is proposed. First, we present the obstacle-avoiding distance estimation method to provide precise task scenario information and reduce the computational complexity. Then, the crossover-based updating method is presented to enable the algorithm to solve discrete problems such as task assignment. In addition, the search intensity adaptive mechanism is presented to optimize and balance the exploration and exploitation intensities in the solution space reasonably, thereby achieving high-quality task assignment results quickly. Further, joint leadership mechanism and local search mechanism are proposed to enhance the exploration ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time. [ABSTRACT FROM AUTHOR]

For the large-scale search and rescue (S&R) scenarios, the centralized and distributed multi-UAV multitask assignment algorithms for multi-UAV systems have the problems of heavy computational load and massive communication burden, which make it hard to guarantee the effectiveness and convergence speed of their task assignment results. To address this issue, this paper proposes a hierarchical task assignment strategy. Firstly, a model decoupling algorithm based on density clustering and negotiation mechanism is raised to decompose the large-scale task assignment problem into several nonintersection and complete small-scale task assignment problems, which effectively reduces the required computational amount and communication cost. Then, a cluster head selection method based on multiattribute decision is put forward to select the cluster head for each UAV team. These cluster heads will communicate with the central control station about the latest assignment information to guarantee the completion of S&R mission. At last, considering that a few targets cannot be effectively allocated due to UAVs' limited and unbalanced resources, an auction-based task sharing scheme among UAV teams is presented to guarantee the mission coverage of the multi-UAV system. Simulation results and analyses comprehensively verify the feasibility and effectiveness of the proposed hierarchical task assignment strategy in large-scale S&R scenarios with dispersed clustering targets. [ABSTRACT FROM AUTHOR]

In recent years, spatial crowdsourcing has emerged as an important new framework, in which each spatial task requires a set of right crowd-workers in the near vicinity to the target locations. Previous studies have focused on spatial task assignment in the static crowdsourcing environment. These algorithms may achieve local optimality by neglecting the uncertain features inherent in real-world crowdsourcing environments, where workers may join or leave during run time. Moreover, spatial task assignment is more complicated when large-scale crowd-workers exist in crowdsourcing environments. The large-scale nature of task assignments poses a significant challenge to uncertain spatial crowdsourcing. In this paper, we propose a novel algorithm combining spatial optimization and multi-agent temporal difference learning (SMATDL). The combination of grid-based optimization and multi-agent learning can achieve higher adaptability and maintain greater efficiency than traditional learning algorithms in the face of large-scale crowdsourcing problems. The SMATDL algorithm decomposes the uncertain crowdsourcing problem into numerous sub-problems by means of a grid-based optimization approach. In order to adapt to the change in the large-scale environment, each agent utilizes temporal difference learning to handle its own spatial region optimization in online crowdsourcing. As a result, multiple agents in SMATDL collaboratively learn to optimize their efforts in accomplishing the global assignment problems efficiently. Through extensive experiments, we illustrate the effectiveness and efficiency of our proposed algorithms on the experimental data sets. [ABSTRACT FROM AUTHOR]

In recent years, mobile crowdsourcing has emerged as a powerful computation paradigm to harness human power to perform spatial tasks such as collecting real-time traffic information and checking product prices in a specific supermarket. A fundamental problem of mobile crowdsourcing is: When both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each worker. Most existing studies focus on efficient assignment algorithms based on bipartite graph matching. However, they overlook an important fact that crowd workers might be unreliable. Thus, their task assignment schemes cannot ensure the overall quality. In this article, we investigate the Quality-aware Online Task Assignment (QAOTA) problem in mobile crowdsourcing. We propose a probabilistic model to measure the quality of tasks and a hitchhiking model to characterize workers' behavior patterns. We model task assignment as a quality maximization problem and derive a polynomial-time online assignment algorithm. Through rigorous analysis, we prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Finally, we demonstrate the efficiency and effectiveness of our solution through intensive experiments. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing has emerged as a new sensing paradigm which has many advantages over traditional sensing paradigms. In this paper, we focus on the task allocation problem for eco-friendly mobile crowdsensing which aims to minimize carbon emissions under various constraints such as task deadline and road traffic constraints. We first describe the system model of eco-friendly mobile crowdsensing and formulate the task allocation problem in offline scenario and online scenario, respectively. Then we propose Eco-Friendly Task Allocation algorithm (EFTA) to address the allocation problem in offline scenario. This algorithm consists of two processes including transportation selection and worker-task matching. After this, we propose Online Eco-Friendly Task Allocation algorithm (OEFTA) to tackle the allocation problem in online scenario. The algorithm adopts greedy online task assignment/reassignment upon arrival of a new task or a new worker. Extensive simulation results show our proposed algorithms have much better performance than baseline algorithms. [ABSTRACT FROM AUTHOR]

Abstract: An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE algorithm: scale factor and crossover rate. Specially, scale factor is adaptively adjusted According to the objective function values of all candidate solutions, and crossover rate is dynamically adjusted with the increasement of iterations. The adaptive scale factor and dynamical crossover rate are combined to increase the diversity of candidate solutions, and to enhance the exploration capacity of solution space of the proposed algorithm. In addition, a usual penalty function method is adopted to trade-off the objective and the constraints. Experimental results demonstrate that the optimal solutions obtained by the IDE algorithm are all better than those obtained by the other two DE algorithms on solving some task assignment problems. [Copyright &y& Elsevier]

In this paper, we study an extended formulation of moldable task scheduling problem (MTSP) motivated by the assignments of quay cranes to vessels. In container terminals, handling time of a vessel depends on the number of quay cranes assigned to that vessel. This characteristic allows us to model quay crane assignment problem (QCAP) as a variant of MTSP. By considering the modeling requirements of various properties of QCAP, we develop an extended formulation of MTSP with specific task to machine assignments. Even though this formulation brings modeling flexibility, it can only be solved for small instances because of its size. For this reason, we provide a generic solution algorithm based on a logic based Benders decomposition by utilizing the extended formulation. There are various characteristics of QCAP observed in different terminals. Accordingly, we implement the proposed decomposition algorithm for contiguous assignments of QCs, uniform QCs as well as the availability of QCs. Computational experiments show that the proposed algorithm is able to solve instances of considerable sizes to optimality and provides a modeling flexibility that allows implementation to different terminal settings. • We study an extended formulation of moldable task scheduling problem (MTSP). • We provide an exact solution framework for MTSP applications in production systems. • Logic-based Benders decomposition is applied to a large time-indexed formulation. • We implement a detailed quay crane assignment problem by utilizing the framework. • The framework solves realistic instances for various MTSP extensions and objectives. [ABSTRACT FROM AUTHOR]