Genetic Algorithm Based Reactive Scheduling in Manufacturing System –Advanced Crossover Method for Tardiness Minimization Problems–

Sakaguchi, Tatsuhiko; Tanimizu, Yoshitaka; Harada, Kozo; Iwamura, Koji; Sugimura, Nobuhiro

doi:10.1016/b978-008044963-0/50043-6

Cited by 1 publication

(2 citation statements)

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Section: Survey and Representative Researchmentioning

confidence: 99%

“…Bonfill et al (2005) utilise stochastic GAs to produce schedules to minimise idle machine time and waiting times in a manner that can handle unexpected events. Several other authors also study how GA based solutions can handle unexpected events ( e.g, (Sakaguchi et al 2006)). Chan et al (2006) argue that job shop scheduling should not only aim to allocate jobs to machines to determine optimal completion times but also take account of maintenance.…”

Section: Gas In Schedulingmentioning

confidence: 99%

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A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Survey and Representative Researchmentioning

confidence: 99%