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An effective knowledge-based evolutionary algorithm for task assignment problem of pollination robots and spraying drones in multi-orchard scenarios

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Wang, Cun-Hai Pan, Quan-Ke Zhang, Wei Miao, Zhong-Hua Jing, Xue-Lei Li, Wei-Min Wang, Bing Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China State Key Lab Agr Equipment Technol Beijing 100083 Peoples R China Liaocheng Univ Network Informat Ctr Liaocheng 252000 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200000 Peoples R China

Collaborative task assignment between intelligent agricultural robots and drones has significantly enhanced operational efficiency in agriculture. However, research on robot-drone collaboration in distributed agricultural scenarios remains highly limited. To address this gap, this paper investigates the task assignment problem of pollination robots and spraying drones in multi-orchard scenarios (MORDTA), aiming to minimize the maximum completion time across all orchards. A mathematical model is formulated, and an improved evolutionary algorithm with problem-specific knowledge (IEAPK) is proposed to solve the problem. The IEAPK algorithm incorporates two heuristic strategies in the initialization phase, ensuring efficient assignment of robots and drones to orchards while optimizing task distribution. Four novel reallocation-based evolutionary operators are designed to redistribute tasks, making task assignment more reasonable. An evolutionary stage based on multiorchard equilibrium is then presented to guarantee a balanced distribution of robots and drones across orchards. An adaptive local search mechanism is also developed, allowing each orchard to dynamically select the optimal neighborhood search operator, further optimizing task assignments. Additionally, extra evolutionary operations are applied to the critical orchards, facilitating targeted optimization. Comprehensive experiments show that the proposed IEAPK algorithm outperforms existing state-of-the-art algorithms by a substantial margin in both efficiency and effectiveness.

关键词： Multi-orchard scenario task assignment problem Robot-drone collaboration Improved evolutionary algorithm Adaptive local search

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A novel global harmony search algorithm for task assignment problem

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JOURNAL OF SYSTEMS AND SOFTWARE 2010年第10期83卷 1678-1688页

作者： Zou, Dexuan Gao, Liqun Li, Steven Wu, Jianhua Wang, Xin Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ S Australia Div Business Adelaide SA 5001 Australia Shenyang Jian Zhu Univ Informat & Control Engn Fac Shenyang 110168 Liaoning Peoples R China

The objective of task assignment problem (TAP) is to minimize the sum of interprocessor communication and task processing costs for a distributed system which subjects to several resource constraints. We use a novel global harmony search algorithm (NGHS) to solve this problem, and the NGHS algorithm has demonstrated higher efficiency than the improved harmony search algorithm (IHS) on finding the near optimal task assignment. We also devise a new method called normalized penalty function method to tradeo (R) the costs and the constraints. A large number of experiments show that our algorithm performs well on finding the near optimal task assignment, and it is a viable approach for the task assignment problem. (C) 2010 Elsevier Inc. All rights reserved.

关键词： task assignment problem Novel global harmony search algorithm Improved harmony search algorithm Normalized penalty Function method

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Cooperative Multiple task assignment problem With Target Precedence Constraints Using a Waitable Path Coordination and Modified Genetic Algorithm

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IEEE ACCESS 2021年 9卷 39392-39410页

作者： Zhao, Yiyang Zhou, Deyun Piao, Haiyin Yang, Zhen Hou, Rui Kong, Weiren Pan, Qian Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China CAST Beijing Inst Spacecraft Syst Engn Beijing 100000 Peoples R China AVIC AVION Co Ltd Dept Adv Technol Beijing 100080 Peoples R China

task assignment is a critical technology for heterogeneous unmanned aerial vehicle (UAV) applications. Target precedence has typically been ignored in previous studies, such that it is possible to obtain a task assignment solution with an unreasonable target execution order. For this reason, a cooperative multiple task assignment problem with target precedence constraints (CMTAPTPC) model is proposed in this paper, which considers not only kinematic, resource, and task precedence constraints of the UAV, but also target precedence to achieve more realistic scenarios. In addition, a graph method is improved to detect and eliminate deadlocks in solutions that include target precedence constraints. We also introduced a waitable path coordination (WPC) algorithm to generate conflict-free flight paths. Unlike the traditional path elongation method, this method can reduce the number of path elongation operations and save the energy of UAVs. Based on the characteristics of the CMTAPTPC model, this study proposes a modified genetic algorithm that integrates the graph-based method and WPC algorithm to solve the task assignment problem. In the simulation, three problem-scale scenarios were designed, and the superior performance of the modified genetic algorithm was demonstrated by comparing it with a traditional genetic algorithm. Finally, a time series diagram shows the task assignment solution that meets all time constraints and illustrates the rationality of the WPC algorithm.

关键词： task analysis System recovery Genetic algorithms Unmanned aerial vehicles Stochastic processes Optimization Sociology Deadlocks genetic algorithm heterogeneous unmanned aerial vehicles path coordination task assignment problem

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Particle swarm optimization for task assignment problem

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MICROPROCESSORS AND MICROSYSTEMS 2002年第8期26卷 363-371页

作者： Salman, A Ahmad, I Al-Madani, S Kuwait Univ Coll Petroleum & Engn Dept Comp Engn Safat 13060 Kuwait

task assignment is one of the core steps to effectively exploit the capabilities of distributed or parallel computing systems. The task assignment problem is an NP-complete problem. In this paper, we present a new task assignment algorithm that is based on the principles of particle swarm optimization (PSO). PSO follows a collaborative population-based search, which models over the social behavior of bird flocking and fish schooling. PSO system combines local search methods (through self experience) with global search methods (through neighboring experience), attempting to balance exploration and exploitation. We discuss the adaptation and implementation of the PSO search strategy to the task assignment problem. The effectiveness of the proposed PSO-based algorithm is demonstrated by comparing it with the genetic algorithm, which is well-known population-based probabilistic heuristic, on randomly generated task interaction graphs. Simulation results indicate that PSO-based algorithm is a viable approach for the task assignment problem. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： particle swarm optimization task assignment problem task interaction graph

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Solving the task assignment problem with ant colony optimisation incorporating ideas from the clonal selection algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第2期7卷 129-143页

作者： Martinovic, Goran Bajer, Drazen Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Osijek 31000 Croatia

The task assignment problem commonly appears in distributed computing environments. It asks an assignment of tasks to processors is found such that it satisfies the imposed constraints and that the total execution and communication cost of the tasks is minimal. This paper presents an algorithm based on ant colony optimisation that incorporates ideas from the clonal selection algorithm. Namely, the ant colony optimisation algorithm includes the cloning of the iteration-best ant and mutation of its clones' solutions;the goal being a better exploitation of promising parts of the search space. Besides that, the solution construction procedure is modified to take the memory constraints into account and the pheromone update mechanism is modified to enable the best clone to deposit pheromone. The experimental analysis, conducted on a large number of problem instances, showed that the proposed algorithm performs better compared to the MAX-MIN ant system, a differential evolution and a particle swarm optimisation algorithm.

关键词： ant colony optimisation ACO clonal selection algorithm CSA clones' solutions cloning differential evolution DE iteration-best ant MAX-MIN ant system MMAS memory constraints mutation particle swarm optimisation PSO pheromone update mechanism solution construction procedure task assignment problem TAP

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Entropic simplified swarm optimization for the task assignment problem

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APPLIED SOFT COMPUTING 2017年 58卷 115-127页

作者： Lai, Chyh-Ming Yeh, Wei-Chang Huang, Yen-Cheng Natl Def Univ Inst Resources Management & Decis Sci Management Coll Taipei 112 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan

The task assignment problem (TAP) aims to assign application tasks to a number of distributed processors in a computation system in order to increase the efficiency and effectiveness of the system for minimizing or maximizing a certain cost. The problem is NP-hard;thus, finding the exact solutions is computationally intractable for larger size problems. In this paper, a novel entropic simplified swarm optimization, known as ESSO, is proposed for solving this problem. In this method, an entropic local search (ELS) inspired by information theory is proposed to enhance the exploitation capability of SSO. Entropy is adopted to describe the uncertainty level of assigned tasks;the task with higher uncertainty then has more chance to be reassigned. Furthermore, for each reassigned task, the corresponding list of potential processors can be constructed using information theory;this enhances the probability of finding promising solutions in ELS. To empirically evaluate the performance of the proposed method, experiments are conducted using twenty-four randomly generated problems ranging from small to large scale, and the corresponding results are compared with existing works. The experiment results indicate that ESSO is better than its competitors in both solution quality and efficiency. (C) 2017 Elsevier B.V. All rights reserved.

关键词： task assignment problem Simplified swarm optimization Information theory

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Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年第May期76卷 112-125页

作者： Jia, Zhenyue Yu, Jianqiao Ai, Xiaolin Xu, Xuan Yang, Di Beijing Inst Technol Sch Aerosp Engn 5 South Zhongguancun St Beijing 100081 Peoples R China

In this paper, a combinatorial optimization problem, formulated as a cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles, is studied in the form of a two-stage stochastic programming model. To create a more realistic mission scenario, we involve several types of constraints in this problem, such as kinematic constraints, resource constraints (both boarded weapons and fuels), and time constraints (both task sequences and time windows). Due to the prohibitive computational complexity of the problem, a novel meta -heuristic based on a modified genetic algorithm is proposed to improve the solution of this stochastic task assignment problem. After a feasible solution is obtained, a set of actual flight paths will be created by a path coordination process according to the requirements of the task precedence. In the simulation part, the effect of the proposed algorithm, both on searching capability and convergence speed, is demonstrated by comparison with the random search algorithm. Moreover, the stochastic nature of this problem caused by the stochastic flight velocities is also illustrated by comparison with a deterministic model. Additionally, actual flight trajectories meeting all time constraints are displayed for this stochastic task assignment problem. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： Stochastic programming task assignment problem Heterogeneous unmanned aerial vehicles Genetic algorithm Path coordination

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Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches

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OPTIMIZATION LETTERS 2012年第2期6卷 315-329页

作者： Hoai An Le Thi Duc Manh Nguyen Tao Pham Dinh Paul Verlaine Univ UFR MIM Lab Theoret & Appl Comp Sci F-57045 Metz France Natl Inst Appl Sci Lab Modelling Optimizat & Operat Res F-76801 St Etienne France

In this paper, we consider a task allocation model that consists of assigning a set of m unmanned aerial vehicles (UAVs) to a set of n tasks in an optimal way. The optimality is quantified by target scores. The mission is to maximize the target score while satisfying capacity constraints of both the UAVs and the tasks. This problem is known to be NP-hard. Existing algorithms are not suitable for the large scale setting. Scalability and robustness are recognized as two main issues. We deal with these issues by two optimization approaches. The first approach is the Cross-Entropy (CE) method, a generic and practical tool of stochastic optimization for solving NP-hard problem. The second one is Branch and Bound algorithm, an efficient classical tool of global deterministic optimization. The numerical results show the efficiency of our approaches, in particular the CE method for very large scale setting.

关键词： UAV task assignment problem Stochastic programming Binary nonlinear programming Cross-entropy (CE) method Brand and bound algorithm

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The task assignment problem for unrestricted movement between workstation groups

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JOURNAL OF SCHEDULING 2006年第4期9卷 315-341页

作者： Bard, JF Wan, L Univ Texas Grad Program Operat Res & Ind Engn Austin TX 78712 USA Amazon Com Seattle WA 98104 USA

The purpose of this paper is to investigate the problem of assigning tasks to workers during their daily shifts. For a homogeneous workforce, a given set of workstation groups, and a corresponding demand for labor, the objective is to develop a disaggregated schedule for each worker that minimizes the weighted sum of transitions between workstation groups. In the formulation of the problem, each day is divided into 48 1/2-hour time periods and a multi-commodity network is constructed in which each worker corresponds to a unique commodity and each node represents a workstation group-time period combination. Lunch breaks and idle time are also included in the model. Initial attempts to solve large instances with a commercial code indicated a need for a more practical approach. This led to the development of a reduced network representation in which idle periods are treated implicitly, and a sequential methodology in which the week is decomposed into 7 daily problems and each solved in turn. To gain more computational efficiency, a tabu search procedure was also developed. All procedures were tested using data obtained from a U.S. Postal Service mail processing and distribution center. Depending on the labor category, anywhere from 3 to 28 workstation groups and up to 311 full-time and part-time workers had to be scheduled together. The results were mixed. While small problems could be solved to near-optimality with the integer programming approaches, tabu search was the best alternative for the very large instances. However, the excessive number of swaps needed to gain marginal improvements, undermined its *** the two provided a good balance in most cases.

关键词： multi-commodity network integer programming tabu search task assignment problem

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Research on Distributed Heterogeneous Factory task assignment problem With Loading Efficiency Constraints: A Case Study

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IEEE ACCESS 2024年 12卷 164427-164443页

作者： Zhong, Meisong Zeng, Zhiqiang Hong, Zhiyong Chen, Chuangquan Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China

In a distributed heterogeneous factory (DHF), an order can be assigned to multiple factories for production. Although this method reduces production costs, it can cause reduced loading efficiency and increase transportation costs. Balancing loading efficiency and total cost is a key issue that must be resolved in the task assignment problem (TAP) for DHF. This study investigates the DHFTAP with total cost objective and loading efficiency constraints. To solve this problem, three mixed integer linear programming (MILP) models are constructed based on different production strategies: Model I: split orders based on the combination of factory and warehouse;Model II: merge orders based on product types;Model III: split orders based on tasks. In the experiment, three MILP models and three evolutionary algorithms (EAs) are compared with the rule-based traditional method, and the sensitivity of minimum loading efficiency and the impact of factory load rate are analyzed. The experimental results reveal that MILP and EA can effectively solve the researched problem. Finally, valuable suggestions are provided for enterprises.

关键词： Production facilities Costs Loading Load modeling Manufacturing systems Transportation Heuristic algorithms Metaheuristics Job shop scheduling Biological system modeling task analysis Mixed integer linear programming Evolutionary computation Distributed heterogeneous factory task assignment problem mixed integer linear programming evolutionary algorithm

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