Ant colony intelligence in multi-agent dynamic manufacturing scheduling

Xiang, Wei; Lee, H. P.

doi:10.1016/j.engappai.2007.03.008

Cited by 184 publications

(87 citation statements)

References 32 publications

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“…When presented with an event, however, simulation and AI-based approaches centrally control their sub-systems after evaluating potential responses through a computational model of the system. While this can lead to good coordination, on large scale, complex problems, scalability, along with the difficulty of designing and evaluating the model, makes these approaches impractical [33]. Agent-based approaches, however, are particularly attractive when designing scalable systems due to being inherently decentralised.…”

Section: Introductionmentioning

confidence: 99%

“…This approach would be sufficient if we had a test problem which could accurately predict algorithmic performance on real world problems. However, Xiang and Lee [33] claim that current task allocation test problems do not give accurate results as they are oversimplified, and try to address this problem by designing a more realistic problem model. However, this new problem still contains unrealistic assumptions and is specific to the job shop scheduling problem [33].…”

Section: Introductionmentioning

confidence: 99%

“…However, Xiang and Lee [33] claim that current task allocation test problems do not give accurate results as they are oversimplified, and try to address this problem by designing a more realistic problem model. However, this new problem still contains unrealistic assumptions and is specific to the job shop scheduling problem [33]. Further, it is not clear that performance on one specific real world problem generalises well to problems with different conditions and constraints.…”

Section: Introductionmentioning

confidence: 99%

“…The common algorithms for distributed task-allocation can be broken down into three categories: simulation-, AI-, and agent-based [33]. When presented with an event, however, simulation and AI-based approaches centrally control their sub-systems after evaluating potential responses through a computational model of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Current comparative studies of threshold-and market-based algorithms tend to focus on single test problems, with fixed parameters [6,14,24,33]. They compare quantitative performance to establish which algorithm performs best in their setting.…”

Section: Introductionmentioning

confidence: 99%

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The effect of load on agent-based algorithms for distributed task allocation

Goldingay

Mourik

2013

Information Sciences

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Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The effect of load on agent-based algorithms for distributed task allocation

Goldingay

Mourik

2013

Information Sciences

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Influencing Message Propagation in a Social Network Using Embedded Boolean Networks: A Demonstration Using Agent‐Based Modeling

Polacek

Verma

Yang

2016

INCOSE International Symp

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A Boolean network is a self‐organizing construct originally developed in the field of theoretical biology to investigate the limited variety of processes within genetic networks. Our research objective is to reformulate this descriptive biological/mathematical model into an engineering approach that can be used to shape the operational state space of a targeted complex system in a desired way. We demonstrate this by coupling the state control of a Boolean network to the rules used by the agents in an agent‐based system, thereby controlling when these rules are expressed. The application presented in this paper uses an agent‐based model of the financial community network on Twitter as the target system. A Boolean network is employed to influence the propagation of information through the community by inhibiting the retweeting rule of agents with high network centrality. The results are compared against those produced by manually removing the high centrality nodes.

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