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Article
Publication date: 22 September 2020

Seenu N., Kuppan Chetty R.M., Ramya M.M. and Mukund Nilakantan Janardhanan

This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task

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Abstract

Purpose

This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.

Design/methodology/approach

This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.

Findings

This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.

Originality/value

This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

Details

Industrial Robot: the international journal of robotics research and application, vol. 47 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 16 August 2021

Muhammad Usman Arif

Multi-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have…

Abstract

Purpose

Multi-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.

Design/methodology/approach

This paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.

Findings

Allowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.

Originality/value

To the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

Details

International Journal of Intelligent Unmanned Systems, vol. 10 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

Open Access
Article
Publication date: 12 June 2017

Lizhen Cui, Xudong Zhao, Lei Liu, Han Yu and Yuan Miao

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a…

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Abstract

Purpose

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

Design/methodology/approach

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

Findings

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

Originality/value

The paper can give a better task allocation strategy in the crowdsourcing systems.

Details

International Journal of Crowd Science, vol. 1 no. 2
Type: Research Article
ISSN: 2398-7294

Keywords

Article
Publication date: 5 October 2022

Sophiya Shiekh, Mohammad Shahid, Manas Sambare, Raza Abbas Haidri and Dileep Kumar Yadav

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be…

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Abstract

Purpose

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

Design/methodology/approach

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

Findings

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

Originality/value

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 5
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 26 February 2024

Xiaohui Jia, Chunrui Tang, Xiangbo Zhang and Jinyue Liu

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single…

Abstract

Purpose

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

Design/methodology/approach

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

Findings

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

Originality/value

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 3
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 7 March 2023

Preeti Godabole and Girish Bhole

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main…

Abstract

Purpose

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

Design/methodology/approach

The paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.

Findings

Both partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.

Research limitations/implications

The simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.

Practical implications

The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

Originality/value

This paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.

Details

International Journal of Pervasive Computing and Communications, vol. 20 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 3 June 2024

Jianhua Sun, Suihuai Yu, Jianjie Chu, Wenzhe Cun, Hanyu Wang, Chen Chen, Feilong Li and Yuexin Huang

In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine…

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Abstract

Purpose

In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine system by rationally distributing workload and minimizing task completion time. Existing related studies exhibit a limited consideration of workload distribution and involve the violation of precedence constraints in the solution process. This study proposes a CTAS method to address these issues.

Design/methodology/approach

The method defines visual, auditory, cognitive and psychomotor (VACP) load balancing objectives and integrates them with workload balancing and minimum task completion time to ensure equitable workload distribution and task execution efficiency, and then a multi-objective optimization model for CTAS is constructed. Subsequently, it designs a population initialization strategy and a repair mechanism to maintain sequence feasibility, and utilizes them to improve the non-dominated sorting genetic algorithm III (NSGA-III) for solving the CTAS model.

Findings

The CTAS method is validated through a numerical example involving a mission with a specific type of armored vehicle. The results demonstrate that the method achieves equitable workload distribution by integrating VACP load balancing and workload balancing. Moreover, the improved NSGA-III maintains sequence feasibility and thus reduces computation time.

Originality/value

The study can achieve equitable workload distribution and enhance the search efficiency of the optimal CTAS scheme. It provides a novel perspective for task planners in objective determination and solution methodologies for CTAS.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 13 February 2024

José Nogueira da Mata Filho, Antonio Celio Pereira de Mesquita, Fernando Teixeira Mendes Abrahão and Guilherme C. Rocha

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization…

Abstract

Purpose

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

Design/methodology/approach

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

Findings

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

Originality/value

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 25 January 2024

Anil Kumar Inkulu and M.V.A. Raju Bahubalendruni

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study…

Abstract

Purpose

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

Design/methodology/approach

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

Findings

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

Originality/value

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Article
Publication date: 1 June 1991

Steven L. Johnson and O. Felix Offodile

The history, successes, failures and future needs that relate tothe allocation of functions to humans and/ or machines in manufacturingenvironments are presented. The various…

Abstract

The history, successes, failures and future needs that relate to the allocation of functions to humans and/ or machines in manufacturing environments are presented. The various methodologies that have been proposed for performing function allocation are discussed. The basic process involves matching the capabilities and limitations of the particular human or automated system with the requirements imposed by the manufacturing operation. This process can range from a global, systems approach down to the delineation of specific capabilities of humans and automated systems. Both recent advances and obstacles to the effective allocation of tasks to humans or machines based on the capabilities of each are presented. The current status and the areas where future research and development are needed are discussed.

Details

International Journal of Operations & Production Management, vol. 11 no. 6
Type: Research Article
ISSN: 0144-3577

Keywords

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