task assignment problem
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Author(s):  
Youssef Hami ◽  
Chakir Loqman

This research is an optimal allocation of tasks to processors in order to minimize the total costs of execution and communication. This problem is called the Task Assignment Problem (TAP) with nonuniform communication costs. To solve the latter, the first step concerns the formulation of the problem by an equivalent zero-one quadratic program with a convex objective function using a convexification technique, based on the smallest eigenvalue. The second step concerns the application of the Continuous Hopfield Network (CHN) to solve the obtained problem. The calculation results are presented for the instances from the literature, compared to solutions obtained both the CPLEX solver and by the heuristic genetic algorithm, and show an improvement in the results obtained by applying only the CHN algorithm. We can see that the proposed approach evaluates the efficiency of the theoretical results and achieves the optimal solutions in a short calculation time.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hua Yang ◽  
Jungang Yang ◽  
Wendong Zhao ◽  
Cuntao Liu

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of “exploration-exploitation” into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users’ priority-based satisfaction, users’ weighted satisfaction can be improved by about 47% based on our algorithm in situations where users’ information acquisition is tight time-window constraints. In comparison, UAVs’ energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users’ weighted satisfaction by about 5% with almost the same energy consumption of UAVs.


2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jie Chen ◽  
Kai Xiao ◽  
Kai You ◽  
Xianguo Qing ◽  
Fang Ye ◽  
...  

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.


2021 ◽  
Author(s):  
Hardi M. Mohammed ◽  
Tarik A. Rashid

Abstract Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.


2021 ◽  
pp. 002029402110022
Author(s):  
Song Han ◽  
Chenchen Fan ◽  
Xinbin Li ◽  
Xi Luo ◽  
Zhixin Liu

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.


2021 ◽  
Vol 11 (4) ◽  
pp. 1909
Author(s):  
Jung-Fa Tsai ◽  
Chun-Hua Huang ◽  
Ming-Hua Lin

With the advent of the Internet of Things era, more and more emerging applications need to provide real-time interactive services. Although cloud computing has many advantages, the massive expansion of the Internet of Things devices and the explosive growth of data may induce network congestion and add network latency. Cloud-fog computing processes some data locally on edge devices to reduce the network delay. This paper investigates the optimal task assignment strategy by considering the execution time and operating costs in a cloud-fog computing environment. Linear transformation techniques are used to solve the nonlinear mathematical programming model of the task assignment problem in cloud-fog computing systems. The proposed method can determine the globally optimal solution for the task assignment problem based on the requirements of the tasks, the processing speed of nodes, and the resource usage cost of nodes in cloud-fog computing systems.


Computers ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 15
Author(s):  
Son Tung Ngo ◽  
Jafreezal Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Bui Ngoc Anh

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The lecturer’s assigning task is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign constrained tasks (the time and required skills) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that requires the trade-off between two or more conflicting objectives to indicate the optimal solution. We use the compromise programming approach to the multi-objective problem to solve this. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, we tested the model and algorithm with real scheduling data, including 139 sections of 17 subjects to 27 lecturers in 10 timeslots. Finally, a web application supports the decision-maker to visualize and manipulate the obtained results.


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