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

2022 ◽  
Vol 214 ◽  
pp. 177-192
Author(s):  
Yang Li ◽  
Yang Xu ◽  
Xinyu Xue ◽  
Xuemei Liu ◽  
Xinghua Liu
Keyword(s):  
Assignment Problem ◽  
Crop Protection ◽  
Task Assignment ◽  
Task Assignment Problem ◽  
Multi Uav

scholarly journals Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74542-74557 ◽  
Author(s):  
Moning Zhu ◽  
Xiaoxia Du ◽  
Xuehua Zhang ◽  
He Luo ◽  
Guoqiang Wang
Keyword(s):  
Assignment Problem ◽  
Rapid Assessment ◽  
Task Assignment ◽  
Earthquake Scenario ◽  
Assessment Task ◽  
Task Assignment Problem ◽  
Multi Uav

scholarly journals On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hua Yang ◽  
Jungang Yang ◽  
Wendong Zhao ◽  
Cuntao Liu
Keyword(s):  
Energy Consumption ◽  
User Satisfaction ◽  
Assignment Problem ◽  
Information Acquisition ◽  
Time Window ◽  
Task Assignment ◽  
Total Energy Consumption ◽  
Time Window Constraints ◽  
Task Assignment Problem ◽  
Multi Uav

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.

scholarly journals Task Assignment Problem of Robots in a Smart Warehouse Environment

2016 ◽  
Vol 4 (4) ◽  
Author(s):  
Zhenping Li ◽  
Wenyu Li ◽  
Lulu Jiang
Keyword(s):  
Assignment Problem ◽  
Task Assignment ◽  
Task Assignment Problem

Quadratic Convex Reformulation for Solving Task Assignment Problem with Continuous Hopfield Network

2021 ◽  
Author(s):  
Youssef Hami ◽  
Chakir Loqman
Keyword(s):  
Assignment Problem ◽  
Optimal Allocation ◽  
Task Assignment ◽  
Hopfield Network ◽  
Quadratic Program ◽  
Convex Objective Function ◽  
Calculation Results ◽  
Quadratic Convex Reformulation ◽  
Task Assignment Problem ◽  
Theoretical Results

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.

A modified salp swarm algorithm for task assignment problem

2020 ◽  
Vol 94 ◽  
pp. 106445
Author(s):  
Walaa H. El-Ashmawi ◽  
Ahmed F. Ali
Keyword(s):  
Assignment Problem ◽  
Task Assignment ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Task Assignment Problem

scholarly journals Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 862
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Xiaowei Wang
Keyword(s):  
Assignment Problem ◽  
Low Cost ◽  
Task Assignment ◽  
Task Completion ◽  
Volunteer Computing ◽  
Computing Services ◽  
Pruning Strategy ◽  
Computing Platforms ◽  
Deadline Constraints ◽  
Task Assignment Problem

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.

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