A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling

Chou, Yu-Cheng; Cao, Huajun; Cheng, Harry H.

doi:10.1016/j.jmsy.2013.01.005

Cited by 23 publications

(4 citation statements)

References 38 publications

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“…the inverted generational distance and spread [20]. In addition, an agent-based modelling method [14], [3] can be used to formulate the elite-selection scheme and for tracking the behaviour of individual MmGA models. Besides that, the applicability of the proposed model to undertaking other MOPs can be evaluated.…”

Section: Discussionmentioning

confidence: 99%

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

et al. 2017

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Application of an Evolutionary Algorithm-based Ensemble Model to Job-Shop SchedulingChoo Jun TAN, Siew Chin NEOH, Chee Peng LIM, Samer HANOUN, Wai Peng WONG, Chu Kong LOO, Li ZHANG, Saeid NAHAVANDI Abstract In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

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Section: Discussionmentioning

confidence: 99%

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

et al. 2017

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“…Chou, Cao and Cheng [15] developed a dynamic scheduling considering machine breakdown, and able to recover from its faults and prevent them from happening. It was proposed an agent-based solution to solve a distributed job-shop scheduling problem.…”

Section: Scheduling Requirementsmentioning

confidence: 99%

“…The real manufacturing environments need to deal with dynamic events that sometimes are not expected and may occur anytime. Those unexpected disturbances that can happen during the manufacturing process may be changes in the shop-floor, the arrival of new products to the factory or unexpected machine breakdowns, and a dynamic schedule should be able to respond to it in real-time [15], [16]. Furthermore, to respond to the market demand for more customized products, manufacturing systems are becoming more flexible, able to produce different products by sharing tools.…”

Section: Scheduling Requirementsmentioning

confidence: 99%

Production Scheduling Requirements to Smart Manufacturing

Alemão

Rocha

Barata

2019

IFIP Advances in Information and Communication Technology

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The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.

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“…Agents coordinate with each other by a pheromonebased approach. Chou et al (2013) presented a bio-inspired mobile agent-based integrated system for scheduling in flexible autonomic job shop. Nouiri et al (2015) developed two effective and distributed particle swarm optimization algorithms to solve the FJSP.…”

Section: Introductionmentioning

confidence: 99%

A new immune multi-agent system for the flexible job shop scheduling problem

Xiong

2015

J Intell Manuf

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Scheduling for the flexible job shop is very important and challenging in manufacturing field. Multiagent-based approaches have been used to solve the flexible job shop scheduling problem (FJSP), in order to reduce complexity and cost, increase flexibility, and enhance robustness. However, the quality of solution obtained by the multi-agent approach is always worse than the centralized meta-heuristic algorithms. The immune system is a distributed and complicated information processing system, which can protect body from foreign antigens by immune responses. In this paper, we analyze the similarities between the FJSP and humoral immunity, which is one of the immune responses. Based on the similarities, we develop a new immune multi-agent scheduling system (NIMASS) to solve the FJSP with the objective of minimizing the maximal completion time (makespan). In order to acquire the higher-quality solution of the FJSP, we simulate humoral immunity to establish the architecture of NIMASS and the negotiation strategies of NIMASS, which are proposed for negotiation among agents. NIMASS was tested on different benchmark instances of the FJSP. In comparison with the multi-agent approaches and the centralized heuristic algorithms, the computational results indicate that NIMASS can effectively improve the quality of solution in very short time. And the computational time of NIMASS is superior to that of the centralized meta-heuristic algorithms, especially for the complex FJSPs. These results indicate that NIMASS can be very useful in applications that deal with real-time FJSPs.

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A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling

Cited by 23 publications

References 38 publications

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Production Scheduling Requirements to Smart Manufacturing

A new immune multi-agent system for the flexible job shop scheduling problem

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